diff --git a/img/RNAGym.png b/.github/resources/RNAGym.png similarity index 100% rename from img/RNAGym.png rename to .github/resources/RNAGym.png diff --git a/.github/workflows/fitness.yml b/.github/workflows/fitness.yml new file mode 100644 index 0000000..6ec52bf --- /dev/null +++ b/.github/workflows/fitness.yml @@ -0,0 +1,30 @@ +name: Fitness + +on: + pull_request: + push: + branches: [main] + workflow_dispatch: + +permissions: + contents: read + +jobs: + cpu: + runs-on: ubuntu-latest + timeout-minutes: 15 + env: + CONDA_OVERRIDE_CUDA: '12.9' + defaults: + run: + working-directory: rnagym/fitness + steps: + - uses: actions/checkout@v7 + - uses: prefix-dev/setup-pixi@v0.10.2 + with: + pixi-version: v0.78.0 + manifest-path: rnagym/fitness/pixi.toml + environments: default + locked: true + - run: pixi run --locked lint + - run: pixi run --locked test diff --git a/.gitignore b/.gitignore index 27b1092..ee55fc7 100644 --- a/.gitignore +++ b/.gitignore @@ -5,8 +5,8 @@ # Anywere __pycache__/ +activations_debug.log slurm-*.out archives .vibe/ .rg_jobs/ - diff --git a/README.md b/README.md index 69b845c..23d761d 100644 --- a/README.md +++ b/README.md @@ -1,167 +1,60 @@

- RNAGym Logo + + RNAGym logo + RNAGym +
+ + Fitness  ·   + 2D structure  ·   + 3D structure +

GitHub stars bioRxiv - DOI License + Fitness CI + Full fitness coverage requires 80 percent Website - Python

-[RNAGym](https://rnagym.org) is an extensive benchmark suite and resource for RNA fitness and structure prediction. This code repository provides unified access to all baselines leveraged in our paper, as well as to the underlying datasets used to assess their respective fitness and/or structure prediction performance. +[RNAGym][5] provides datasets, baseline models and evaluation workflows for RNA +fitness, secondary structure and tertiary structure prediction. +See the [leaderboards][3] for results and [releases][4] for stable versions. -RNAGym is under active development. Please refer to our [releases](https://github.com/MarksLab-DasLab/RNAGym/releases) page to track the latest updates and stable releases. +## Getting started -> **Update (v0.1.1):** We updated the [leaderboard](leaderboard/fitness/). Our [website][rnagym] will be updated shortly. + -## Baselines +All three benchmarks share the RNAGym v0.2 data archive. Download and extract +it from the repository root: -### Fitness prediction - -We have currently implemented the following baselines (see `./fitness`): - -Model name | Model type | Reference ---- | --- | --- | -Evo 1 | Genomic Language Model | Nguyen et al. "Sequence modeling and design from molecular to genome scale with Evo". Science, 2024. -Evo 1.5 | Genomic Language Model | Merchant et al. "Semantic mining of functional de novo genes from a genomic language model". bioRxiv, 2024. -Evo 2 | Genomic Language Model | Brixi et al. "Genome modeling and design across all domains of life with Evo 2". bioRxiv, 2025. -RNAErnie | Genomic Language Model | Wang et al. "Multi-purpose RNA language modelling with motif-aware pretraining and type-guided fine-tuning". Nature Machine Intelligence, 2024. -RiNALMo | RNA Language Model | Penić et al. "RiNALMo: General-Purpose RNA Language Models Can Generalize Well on Structure Prediction Tasks". arXiv, 2024. -RNA-FM | RNA Language Model | Chen et al. "Interpretable RNA Foundation Model from Unannotated Data for Highly Accurate RNA Structure and Function Predictions" arXiv, 2022. -Nucleotide Transformer v3 | Genomic Language Model | Boshar et al. "A foundational model for joint sequence-function multi-species modeling at scale for long-range genomic prediction". bioRxiv, 2025. -GenSLM | Genomic Language Model | Zvyagin et al. "GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics". The International Journal of High Performance Computing Applications, 2023. -Orthrus | RNA Language Model | Fradkin et al. "Orthrus: Towards Evolutionary and Functional RNA Foundation Models". bioRxiv, 2024. - - -### Secondary structure prediction - -We have currently implemented the following baselines (see `./rnagym/s2d`). - -Model name | Model type | Reference ---- | --- | --- | -Ribonanzanet | Structure prediction | He et al. "Ribonanza: deep learning of rna structure through dual crowdsourcing". bioRxiv, 2024. -EternaFold | Structure prediction | Wayment-Steele et al. "RNA secondary structure packages evaluated and improved by high-throughput experiments." Nature Methods, 2022. -CONTRAfold | Structure prediction | Do et al. "CONTRAfold: RNA secondary structure prediction without physics-based models" Bioinformatics, 22 14:e90–8, 2006. -Vienna | Structure prediction | Gruber et al. "The vienna rna websuite." Nucleic Acids Research, 36:W70 – W74, 2008. -RNAstructure | Structure prediction | Reuter et al. "Rnastructure: software for rna secondary structure prediction and analysis". BMC Bioinformatics, 11:129 – 129, 2010. -RNA-FM | RNA Language Model | Chen et al. "Interpretable RNA Foundation Model from Unannotated Data for Highly Accurate RNA Structure and Function Predictions" arXiv, 2022. -UFold | Structure prediction | Fu et al. "UFold: fast and accurate RNA secondary structure prediction with deep learning" Nucleic Acids Research, 2022. -Mxfold2 | Structure prediction | Sato et al. "RNA secondary structure prediction using deep learning with thermodynamic integration" Nature Methods, 2021. - -### Tertiary structure prediction - -We have also developed a data curation pipeline, structure datasets, and -implemented several baselines for RNA tertiary structure (see -[`./rnagym/s3d`][1]). - -Model name | Model type | Reference ---- | --- | --- | -AlphaFold3 | Structure prediction | Abramson et al. "Accurate structure prediction of biomolecular interactions with AlphaFold 3" Nature, 2024. -NuFold | Structure prediction | Kagaya et al. "NuFold: end-to-end approach for RNA tertiary structure prediction with flexible nucleobase center representation" Nature Communications, 2025. -RhoFold+ | Structure prediction | Shen et al. "Accurate RNA 3D structure prediction using a language model-based deep learning approach" Nature Methods, 2024. -RoseTTAFoldNA | Structure prediction | Baek et al. "Accurate prediction of protein–nucleic acid complexes using RoseTTAFoldNA" Nature Methods, 2023. -trRosettaRNA | Structure prediction | Wang et al. "trRosettaRNA: automated prediction of RNA 3D structure with transformer network" Nature Communications, 2023. - -## Setup - -The RNAGym environment may be created via conda and the provided rnagym_env.yml file as follows: -``` -conda env create -f rnagym_env.yml -conda activate rnagym -python -m pip install -e . -``` -For the fitness prediction task, we recommend the following folder structure: -``` -fitness/ -├── processed_DMS_files/ -├── model_predictions/ -└── model_checkpoints/ -``` - -For the structure prediction task, the data processing and scoring scripts expect the following folder structure: +```bash +wget https://marks.hms.harvard.edu/rnagym/v0.2/data.tar.xz +tar -xJf data.tar.xz ``` -rnagym/s2d/ -├── test_data/ -├── raw_data/ -├── model_predictions/ -└── models/ -``` -The content for `models`, `model_predictions`, `raw_data` and `test_data` may all be downloaded via the links in the next section. -The `data_folder` argument in the data and scoring scripts should be set to the location of the `rnagym/s2d` folder. - -For information on setting up the tertiary structure benchmark, see -[`./rnagym/s3d`][1]. - -## Resources - -To download and unzip the data, run the following commands for each of the data sources you would like to download, as listed in the table below. -For example, you can download & unzip the zero-shot predictions for all baselines for all DMS substitution assays as follows: -``` -curl -o rnagym_assays.zip https://marks.hms.harvard.edu/rnagym/fitness_prediction/rnagym_assays.zip -unzip rnagym_assays.zip && rm rnagym_assays.zip -``` -### Fitness Prediction - -| Data | Size (unzipped) | Link | -| --- | --- | --- | -| Processed assay data | 73MB | https://marks.hms.harvard.edu/rnagym/fitness_prediction/fitness_processed_assays.zip | -| Raw assay data | 88MB | https://marks.hms.harvard.edu/rnagym/fitness_prediction/fitness_raw_data.zip | -| Model predictions | 438MB | https://marks.hms.harvard.edu/rnagym/fitness_prediction/model_predictions.zip | -| Alignments | 319KB | https://marks.hms.harvard.edu/rnagym/fitness_prediction/fitness_MSAs.zip | -| 3D structures | 621KB | https://marks.hms.harvard.edu/rnagym/fitness_prediction/fitness_assays_3D_structures.zip | -| CV splits (supervised) | 145MB | https://marks.hms.harvard.edu/rnagym/fitness_prediction/fitness_CV_splits.zip | - -### 2D Structure Prediction - -| Data | Size (unzipped) | Link | -| --- | --- | --- | -| Processed eval data | 3.2GB | https://marks.hms.harvard.edu/rnagym/structure_prediction/test_data.zip | -| Raw assay data | 5.1GB | https://marks.hms.harvard.edu/rnagym/structure_prediction/raw_data.zip | -| Model predictions | 34GB | https://marks.hms.harvard.edu/rnagym/structure_prediction/model_predictions.zip | -| Model files | 441MB | https://marks.hms.harvard.edu/rnagym/structure_prediction/models.zip | -| Training data (supervised) | 8.1GB | https://marks.hms.harvard.edu/rnagym/structure_prediction/train_data.zip | -| Additional annotations (PDB, Rfam, PseudoBase) | 29MB | https://marks.hms.harvard.edu/rnagym/structure_prediction/test_sequences_annotated.zip | - -Model files for 2D structure prediction task were prepared for a linux 64-bit system. Refer to the [Arnie repo](https://github.com/DasLab/arnie) for different systems. - -### 3D Structure Prediction - -| Data | Size (unzipped) | Link | -| --- | --- | --- | -| Train.csv | 3.6MB | https://github.com/MarksLab-DasLab/RNAGym/blob/main/rnagym/s3d/train.csv | -| Test.csv (monomers) | 84KB | https://github.com/MarksLab-DasLab/RNAGym/blob/main/rnagym/s3d/monomer.csv | -| Test.csv (complexes) | 140KB | https://github.com/MarksLab-DasLab/RNAGym/blob/main/rnagym/s3d/complex.csv | -| Alignments | 55.3MB | https://marks.hms.harvard.edu/rnagym/tertiary_structure_prediction/3D_alignments.tar.xz | -| Monomer scores | 7KB | https://marks.hms.harvard.edu/rnagym/tertiary_structure_prediction/monomer.csv | -| Complex scores | 15KB | https://marks.hms.harvard.edu/rnagym/tertiary_structure_prediction/multimer.csv | -| Model predictions (PDBs) | 165MB | https://marks.hms.harvard.edu/rnagym/tertiary_structure_prediction/3D_model_outputs.tar.xz | -| Test-to-train TM scores | 150MB | https://marks.hms.harvard.edu/rnagym/tertiary_structure_prediction/3D_train_to_test_usalign.tar.xz | - -Data is also available on our -[HuggingFace](https://huggingface.co/datasets/Marks-lab/RNAgym) and -[website](https://rnagym.org). - -## Contact us - -Please report questions, comments, and concerns on [our issue tracker][0] and we will get back to you as soon as possible. - -## Acknowledgements -Our codebase leveraged code from the following repositories to compute baselines: +Data defaults to `data/`. Set `RNAGYM_DATA_DIR` to use another data directory. +Then follow the setup and reproduction instructions for the [fitness][0], +[2D structure][6], and [3D structure][7] benchmarks. -Model | Repo ---- | --- -arnie | https://github.com/DasLab/arnie +Set `RNAGYM_CHECKPOINT_DIR` for model weights and `RNAGYM_DATABASE_DIR` for +reference databases. Both locations are shared across benchmarks. Model +environments and source checkouts stay under each benchmark's `.pixi/` directory. -## License +## Contributing -This project is available under the MIT license found in the LICENSE file in this GitHub repository. +Contributions of models, datasets and fixes are welcome. Use the [issue tracker][2] +to report a problem or propose a change. Run the relevant benchmark's tests and lint +before submitting a pull request. -[rnagym]: https://rnagym.org/ -[0]: https://github.com/MarksLab-DasLab/RNAGym/issues/new/choose -[1]: https://github.com/MarksLab-DasLab/RNAGym/tree/main/rnagym/s3d +[0]: rnagym/fitness/README.md +[2]: https://github.com/MarksLab-DasLab/RNAGym/issues/new/choose +[3]: leaderboard/ +[4]: https://github.com/MarksLab-DasLab/RNAGym/releases +[5]: https://rnagym.org/ +[6]: rnagym/s2d/README.md +[7]: rnagym/s3d/README.md diff --git a/data/fitness/.gitignore b/data/fitness/.gitignore index 24bfeb7..270201c 100644 --- a/data/fitness/.gitignore +++ b/data/fitness/.gitignore @@ -1,2 +1,6 @@ -# Generated alignments +# Local benchmark data +/assays/ +/merged/ +/model_predictions/ /msa/ +/reports/ diff --git a/data/fitness/README.md b/data/fitness/README.md index 673ecfd..97cc745 100644 --- a/data/fitness/README.md +++ b/data/fitness/README.md @@ -2,14 +2,19 @@ | File or directory | Contents | | --- | --- | +| `assays/` | Processed DMS assay tables | +| `merged/` | Assays joined to model predictions | +| `model_predictions/` | One prediction directory per model | | `msa/` | Riboseek alignments for unique ncRNA fitness sequences | | `msa/by_assay/` | Alignment symlinks named by the paper's `DMS_ID` assay key | +| `reference_sheet_final.csv` | Assay metadata and wild-type constructs | +| `reports/` | Supplemental fitness analysis tables | ## Assay lookup Use the `DMS_ID` from -[`fitness/reference_sheet_final.csv`](../../fitness/reference_sheet_final.csv) +[`reference_sheet_final.csv`](reference_sheet_final.csv) directly, for example `msa/by_assay/Kobori_2018_ribozyme.a3m`. The canonical `sequence_` files are shared by exact sequence, so assays with identical wild-type sequences resolve to the same alignment. diff --git a/fitness/reference_sheet_final.csv b/data/fitness/reference_sheet_final.csv similarity index 100% rename from fitness/reference_sheet_final.csv rename to data/fitness/reference_sheet_final.csv diff --git a/fitness/.gitignore b/fitness/.gitignore deleted file mode 100644 index 015dca8..0000000 --- a/fitness/.gitignore +++ /dev/null @@ -1,5 +0,0 @@ -# Fitness data -/assays/ -/model_predictions/ -/merged/ -/results_*/ diff --git a/fitness/README.md b/fitness/README.md deleted file mode 100644 index 6ed10fd..0000000 --- a/fitness/README.md +++ /dev/null @@ -1,126 +0,0 @@ -# Fitness benchmark - -The fitness benchmark evaluates RNA foundation models against experimentally -measured effects from 70 deep mutational scanning assays. The published ncRNA -leaderboard reports signed Spearman correlation, averaged equally across the -ribozyme, tRNA and aptamer categories. - -## Directory structure - -``` -fitness/ -├── baselines/ # Model scoring scripts -│ ├── AIDO_RNA/ -│ ├── EVmutation/ -│ ├── Evo/ -│ ├── GenSLM/ -│ ├── Nucleotide_Transformer/ -│ ├── Orthrus/ -│ ├── RiNALMo/ -│ ├── RNAERNIE/ -│ ├── RNAGenesis/ -│ ├── RNA_FM/ -│ └── masked_lm/ # Shared masked-marginal engine -├── analyze_fill_strategies.py # Compare the four fill conventions -├── merge_scoring_files.py # Merge assays with predictions -├── model_registry.py # Prediction folders and score columns -├── performance_fitness.py # Produce benchmark tables -├── pixi.toml # Shared workflow and test environment -├── reference_sheet_final.csv # Assay metadata -└── utils/ # Table conversion and CV split helpers -``` - -### Data directories (not in repo) - -The following directories are gitignored. Download them from the -[RNAGym fitness archive][0]: - -| Directory | Archive to download | -|-----------|---------------------| -| `assays/` | `fitness_processed_assays.zip`: 70 processed DMS assay CSVs | -| `model_predictions/` | `model_predictions.zip`: precomputed model scores per assay | -| `merged/` | Generated by `merge_scoring_files.py` (or `fitness_results.zip` for published results) | -| `results_*/` | Generated by `performance_fitness.py` | - -## Metrics - -- **Spearman**: signed correlation between predicted and experimental scores -- **AUC**: AUROC of continuous model scores against assay labels split at their median -- **MCC**: signed Matthews correlation after both score vectors are split at their medians - -Values below 0 for Spearman or MCC, and below 0.5 for AUC, mean that the model -ranks variants in the wrong direction. - -## Usage - -### 1. Create the environment - -```bash -cd fitness -pixi install -``` - -The default environment runs merging, analysis and the checkpoint-free test -suite. Individual model environments are not yet encoded in this manifest. - -### 2. Download data - -```bash -cd fitness -wget https://marks.hms.harvard.edu/rnagym/fitness_prediction/fitness_processed_assays.zip -wget https://marks.hms.harvard.edu/rnagym/fitness_prediction/model_predictions.zip -unzip fitness_processed_assays.zip -d assays -unzip model_predictions.zip -d model_predictions -``` - -### 3. Merge assay data with model predictions - -```bash -cd fitness -pixi run python merge_scoring_files.py \ - --processed_folder assays \ - --model_predictions_folder model_predictions \ - --output_folder merged -``` - -The merge defaults to the 31 ncRNA leaderboard assays. Pass `--type coding`, -`--type non-coding` or `--type all` to select another assay group. As of -`v0.2`, not all models are tested on coding or splice variants. - -### 4. Evaluate model performance - -```bash -cd fitness -pixi run python performance_fitness.py \ - --reference_file reference_sheet_final.csv \ - --combined_dir merged \ - --performance_dir results -``` - -Key flags: - -| Flag | Description | -|------|-------------| -| `--type {all,ncRNA,non-coding,coding}` | Select an assay group. The default is the 31-assay ncRNA leaderboard. `non-coding` also includes mRNA-splicing | -| `--msa_only` | Restrict to datapoints where EVmutation has scores | -| `--models MODEL ...` | Evaluate selected merged model columns instead of the defaults | - -## Quality checks - -Run these commands from `fitness/`: - -```bash -pixi run test -pixi run coverage -pixi run lint -pixi run fmt -``` - -## Models - -The default merge registry includes Evo 1, Evo 1.5, Evo 2 (1B base, 7B, 20B and -40B), GenSLM, Nucleotide Transformer v3, RNA-ERNIE, RNA-FM, RiNALMo, RNAGenesis, -Orthrus and the five released AIDO.RNA checkpoints. EVmutation is merged -separately with `--assays_with_MSAs_only` because it only covers assays with an MSA. - -[0]: https://marks.hms.harvard.edu/rnagym/fitness_prediction diff --git a/fitness/analyze_fill_strategies.py b/fitness/analyze_fill_strategies.py deleted file mode 100644 index 8b304ec..0000000 --- a/fitness/analyze_fill_strategies.py +++ /dev/null @@ -1,244 +0,0 @@ -#!/usr/bin/env python3 -""" -Compare the masked-marginal fill strategies across checkpoints. - -Reads the prediction files written by fitness/baselines/masked_lm, one per assay -per checkpoint, each holding every computed strategy as its own column, and -reports the benchmark metric: signed Spearman per assay, averaged within each -ncRNA category, then a macro mean over the categories so that the 26 ribozyme -assays do not swamp the 3 tRNA and 2 aptamer assays. - -Also reports where the strategies differ, whether they reorder the checkpoints, -and how the comparison responds to dropping the category weighting. This is the -code behind the sensitivity section of leaderboard/fitness/README.md. - -The folders and columns come from model_registry.SCORE_COLS, so the -registry is not duplicated here. - -Example: - python fitness/analyze_fill_strategies.py \\ - --predictions_folder path/to/model_predictions \\ - --ref_sheet fitness/reference_sheet_final.csv \\ - --output_folder leaderboard/fitness -""" - -import argparse -from pathlib import Path - -import numpy as np -import pandas as pd -from scipy import stats - -if __package__: - from .model_registry import FOUR_FILL_MODELS, SCORE_COLS, resolve_source -else: - from model_registry import FOUR_FILL_MODELS, SCORE_COLS, resolve_source - -NCRNA = ["Ribozyme", "tRNA", "Aptamer"] -STRATEGIES = ["wt_fill", "mask_fill", "mut_fill", "match_fill"] - - -def parse_args(): - parser = argparse.ArgumentParser( - description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter - ) - parser.add_argument( - "--predictions_folder", - required=True, - help="Folder holding one subfolder per prediction set", - ) - parser.add_argument( - "--ref_sheet", required=True, help="Reference sheet with DMS_ID and RNA_TYPE" - ) - parser.add_argument( - "--output_folder", - default=None, - help="Where to write the per-assay and macro CSVs (default: no CSVs)", - ) - parser.add_argument( - "--allow_incomplete", - action="store_true", - help="Report on checkpoints missing some assays instead of skipping them", - ) - return parser.parse_args() - - -def checkpoints(): - """Group the registry's four-fill entries by checkpoint.""" - grouped = {} - for entry in FOUR_FILL_MODELS: - for strategy in STRATEGIES: - suffix = f"_{strategy}" - if entry.endswith(suffix): - grouped.setdefault(entry[: -len(suffix)], {})[strategy] = entry - break - incomplete = { - name: sorted(set(STRATEGIES) - set(entries)) - for name, entries in grouped.items() - if set(entries) != set(STRATEGIES) - } - if incomplete: - raise ValueError(f"Incomplete four-fill registry entries: {incomplete}") - return grouped - - -def collect(args, expected, rna_type): - """Per-assay signed Spearman for every checkpoint and strategy.""" - records = [] - for name, entries in checkpoints().items(): - columns, folders = {}, set() - for strategy, entry in entries.items(): - folder, columns[strategy] = resolve_source(SCORE_COLS, entry) - folders.add(folder) - if len(folders) != 1: - raise ValueError(f"{name}: its strategies are registered across {folders}") - directory = Path(args.predictions_folder) / folders.pop() - if not directory.is_dir(): - continue - found = sorted( - path.stem for path in directory.glob("*.csv") if path.stem in expected - ) - if set(found) != set(expected): - missing = sorted(set(expected) - set(found)) - print( - f"{name:18s} incomplete: {len(found)}/{len(expected)} assays, " - f"missing {missing[:4]}" - ) - if not args.allow_incomplete: - continue - for assay in found: - assay_path = directory / f"{assay}.csv" - frame = pd.read_csv(assay_path) - absent = [s for s, c in columns.items() if c not in frame.columns] - if absent: - raise KeyError(f"{assay_path} has no columns for {absent}") - # Every strategy must cover the same variants, or the comparison - # between them is confounded by coverage rather than by method - values = { - strategy: pd.to_numeric(frame[column], errors="raise").to_numpy() - for strategy, column in columns.items() - } - infinite = [ - strategy - for strategy, strategy_values in values.items() - if np.isinf(strategy_values).any() - ] - if infinite: - raise ValueError(f"{assay_path} has infinite scores for {infinite}") - masks = { - strategy: np.isfinite(strategy_values) - for strategy, strategy_values in values.items() - } - if not all( - np.array_equal(mask, masks["wt_fill"]) - for strategy, mask in masks.items() - if strategy != "wt_fill" - ): - raise ValueError(f"{assay_path}: strategies cover different variants") - dms_score = pd.to_numeric(frame["DMS_score"], errors="raise").to_numpy() - if np.isinf(dms_score).any(): - raise ValueError(f"{assay_path} has infinite DMS scores") - for strategy in STRATEGIES: - usable = np.isfinite(dms_score) & masks[strategy] - if usable.sum() < 3: - raise ValueError(f"{assay}: only {usable.sum()} usable variants") - rho = stats.spearmanr( - dms_score[usable], values[strategy][usable] - ).correlation - if not np.isfinite(rho): - raise ValueError(f"{assay}: Spearman undefined for {strategy}") - records.append( - { - "model": name, - "strategy": strategy, - "assay": assay, - "RNA_TYPE": rna_type[assay], - "n": int(usable.sum()), - "spearman": rho, - } - ) - return pd.DataFrame(records) - - -def macro_table(per_assay): - rows = [] - for (model, strategy), group in per_assay.groupby(["model", "strategy"]): - by_type = {t: group[group.RNA_TYPE == t]["spearman"].mean() for t in NCRNA} - rows.append( - { - "model": model, - "strategy": strategy, - **by_type, - "macro_3ncRNA": float(np.mean([by_type[t] for t in NCRNA])), - } - ) - return pd.DataFrame(rows) - - -def main(): - args = parse_args() - ref = pd.read_csv(args.ref_sheet, encoding="utf-8-sig") - required = {"DMS_ID", "RNA_TYPE"} - missing = sorted(required - set(ref.columns)) - if missing: - raise ValueError(f"Reference sheet is missing columns: {missing}") - if ref[list(required)].isna().any().any(): - raise ValueError("Reference sheet has missing DMS_ID or RNA_TYPE values") - duplicated = sorted(ref.loc[ref["DMS_ID"].duplicated(), "DMS_ID"].astype(str)) - if duplicated: - raise ValueError(f"Reference sheet repeats DMS_ID values: {duplicated[:5]}") - rna_type = dict(zip(ref["DMS_ID"], ref["RNA_TYPE"])) - expected = sorted(d for d, t in rna_type.items() if t in NCRNA) - print(f"{len(expected)} ncRNA assays expected per checkpoint") - - per_assay = collect(args, expected, rna_type) - if per_assay.empty: - raise SystemExit("No complete checkpoint found in the predictions folder") - macro = macro_table(per_assay) - wide = macro.pivot(index="model", columns="strategy", values="macro_3ncRNA")[ - STRATEGIES - ] - order = list(wide.sort_values("wt_fill", ascending=False).index) - - print("\n=== signed Spearman, macro over the 3 ncRNA categories ===") - print(wide.loc[order].round(4).to_string()) - - print("\n=== ordering under each strategy, best first ===") - for strategy in STRATEGIES: - print( - f" {strategy:10s} " - + " > ".join(wide[strategy].sort_values(ascending=False).index) - ) - - print("\n=== where the strategies differ ===") - category_scores = macro.set_index(["model", "strategy"])[NCRNA] - by_model = category_scores.groupby(level="model") - spread = (by_model.max() - by_model.min()).loc[order] - print(spread.round(4).to_string()) - print(" mean spread: " + ", ".join(f"{c} {spread[c].mean():.4f}" for c in NCRNA)) - - print("\n=== without the category weighting ===") - flat = ( - per_assay.groupby(["model", "strategy"])["spearman"] - .mean() - .unstack()[STRATEGIES] - ) - print(flat.loc[order].round(4).to_string()) - print( - f" wt-fill best on {(wide.loc[order].idxmax(axis=1) == 'wt_fill').sum()}/{len(order)} " - f"under the macro metric, {(flat.loc[order].idxmax(axis=1) == 'wt_fill').sum()}/{len(order)} " - "under a flat mean over all assays" - ) - - if args.output_folder: - out = Path(args.output_folder) - out.mkdir(parents=True, exist_ok=True) - per_assay.to_csv(out / "fill_strategy_per_assay.csv", index=False) - macro.to_csv(out / "fill_strategy_macro.csv", index=False) - print( - f"\nwrote {out}/fill_strategy_per_assay.csv and {out}/fill_strategy_macro.csv" - ) - - -if __name__ == "__main__": - main() diff --git a/fitness/baselines/AIDO_RNA/score_aido_rna.sh b/fitness/baselines/AIDO_RNA/score_aido_rna.sh deleted file mode 100644 index 76f0212..0000000 --- a/fitness/baselines/AIDO_RNA/score_aido_rna.sh +++ /dev/null @@ -1,21 +0,0 @@ -#!/usr/bin/env bash -set -euo pipefail - -script_dir=$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd) - -# AIDO.RNA-1.6B is an encoder-only masked language model and fits on a single -# 48 GB GPU in bfloat16. It requires torch, transformers and modelgenerator -export model_name="genbio-ai/AIDO.RNA-1.6B" - -export reference_sheet="reference_sheet.csv" -export output_scores_dir="path/to/model_predictions/aido_rna_4fill" -export dms_data_dir="path/to/dms/data/dir" - -DMS_index=${SLURM_ARRAY_TASK_ID:-0} - -python "$script_dir/score_aido_rna_single_dms.py" \ - --row_id "$DMS_index" \ - --ref_sheet "$reference_sheet" \ - --dms_dir_path "$dms_data_dir" \ - --output_dir_path "$output_scores_dir" \ - --model_name "$model_name" diff --git a/fitness/baselines/EVmutation/compute_fitness.py b/fitness/baselines/EVmutation/compute_fitness.py deleted file mode 100755 index 323ced8..0000000 --- a/fitness/baselines/EVmutation/compute_fitness.py +++ /dev/null @@ -1,331 +0,0 @@ -#!/usr/bin/env python3 -""" -Score non-coding RNA assays with EVmutation. -""" - -import argparse -import re -import shlex -import shutil -import string -import subprocess -import sys -from pathlib import Path - -import pandas as pd -import yaml -from evcouplings.couplings import CouplingsModel -from evcouplings.mutate import predict_mutation_table - - -def run_command(command_str, output_handle=None): - """Run shell command quietly using shlex.""" - try: - cmd_parts = shlex.split(command_str) - return subprocess.run( - cmd_parts, - check=True, - capture_output=output_handle is None, - stdout=output_handle, - stderr=subprocess.PIPE if output_handle is not None else None, - text=True, - ) - except subprocess.CalledProcessError as error: - print(f"[ERROR] Command failed: {command_str}") - print(f"{error.stderr.decode() if error.stderr else ''}") - raise error - - -def get_rfam_match(sequence, name, database_path, temp_path, cpu_count=1, threshold=0.2): - """ - Scan Rfam and return the accession with the largest alignment file with - E-value less than the provided threshold. - """ - if not re.search(r"ribozyme|trna|aptamer", name, re.IGNORECASE): - return None - - query_fasta = temp_path / "query.fa" - query_fasta.write_text(f">{name}\n{sequence}\n") - table_out = temp_path / "rfam.tbl" - - try: - run_command( - f"cmscan --rfam --nohmmonly --cpu {cpu_count} --tblout {table_out} " - f"{database_path}/Rfam.cm {query_fasta}" - ) - except subprocess.CalledProcessError: - return None - - if not table_out.exists(): - return None - - candidates = set() - with open(table_out) as file_handle: - for line in file_handle: - if not line.startswith("#"): - parts = line.split() - if len(parts) > 15: - accession, e_value = parts[1], float(parts[15]) - if e_value <= threshold: - candidates.add(parts[1]) - - # Select deepest alignment based on file size - best_accession = None - max_file_size = -1 - for accession in candidates: - full_path = database_path / "alignments" / f"{accession}.sto" - current_size = full_path.stat().st_size - if current_size > max_file_size: - max_file_size = current_size - best_accession = accession - - if best_accession is not None: - print(f" > Selected {best_accession} ({max_file_size / 1024**2:.1f} MB)") - return best_accession - - -def prepare_alignment(accession, sequence, name, database_path, temp_path): - """ - Fetch CM, align query, and merge with reference (full/seed) into consensus A2M. - """ - # Select Reference (Full vs Seed logic) - full_sto = database_path / "alignments" / f"{accession}.sto" - - # Threshold: 300 MB - reference_file = ( - full_sto - if full_sto.exists() and full_sto.stat().st_size <= 300 * 1024**2 - else None - ) - - if not reference_file: - reference_file = temp_path / "ref_seed.sto" - with open(reference_file, "w") as file_handle: - run_command( - f"esl-afetch {database_path}/Rfam.seed {accession}", - output_handle=file_handle, - ) - - # Align Query - cm_file = temp_path / f"{accession}.cm" - with open(cm_file, "w") as file_handle: - run_command( - f"cmfetch {database_path}/Rfam.cm {accession}", output_handle=file_handle - ) - - query_sto = temp_path / "query.sto" - run_command(f"cmalign --notrunc -o {query_sto} {cm_file} {temp_path}/query.fa") - - # Merge and filter (keeping uppercase and gaps, skipping insertions in - # lowercase) - trans_table = str.maketrans("", "", string.ascii_lowercase + ".") - combined_a2m = temp_path / "combined.a2m" - - with open(combined_a2m, "w") as output_handle: - for source_file in [query_sto, reference_file]: - proc = run_command( - f"esl-reformat a2m {source_file}", - ) - - for line in proc.stdout.splitlines(keepends=True): - if line.startswith(">"): - output_handle.write(line) - else: - output_handle.write(line.strip().translate(trans_table) + "\n") - - return combined_a2m - - -def run_evcouplings( - job_name, aligned_a2m, dms_csv_path, sequence_id, temp_path, cpu_count -): - """Configure, run pipeline, and score mutations.""" - config = { - "stages": ["align", "couplings"], - "pipeline": "protein_monomer", - "global": { - "alphabet": "rna", - "prefix": str(temp_path / job_name), - "theta": 0.9, - "cpu": cpu_count, - "region": None, - "sequence_id": sequence_id, - "sequence_file": str(temp_path / "query.fa"), - }, - "align": { - "alphabet": "rna", - "protocol": "existing", - "input_alignment": str(aligned_a2m), - "first_index": 1, - "sequence_id": sequence_id, - "seqid_filter": None, - "focus_sequence": sequence_id, - "compute_num_effective_seqs": True, - "minimum_sequence_coverage": 50, - "minimum_column_coverage": 70, - "extract_annotation": True, - "sequence_weights": "nogaps", - }, - "couplings": { - "protocol": "standard", - "iterations": "100", - "alphabet": "rna", - "ignore_gaps": True, - "lambda_J": 0.01, - "lambda_J_times_Lq": True, - "lambda_h": 0.01, - "lambda_group": None, - "scale_clusters": None, - "reuse_ecs": False, - "min_sequence_distance": 4, - "scoring_model": "logistic_regression", - "save_model": True, - }, - "tools": { - "jackhmmer": "jackhmmer", - "plmc": "plmc", - "hmmbuild": "hmmbuild", - "hmmsearch": "hmmsearch", - "hhfilter": "hhfilter", - "psipred": "psipred", - "cns": "cns", - "maxcluster": "maxcluster64bit", - "usalign": "USalign", - }, - "databases": { - "uniprot": "", - "uniref100": "", - "uniref90": "", - "uniref50": "", - "sequence_download_url": "", - "sifts_mapping_table": "", - "sifts_sequence_db": "", - }, - } - - config_path = temp_path / "config.yaml" - with open(config_path, "w") as file_handle: - yaml.dump(config, file_handle) - - try: - run_command(f"evcouplings_runcfg {config_path}") - - # Locate model file - model_files = list(temp_path.glob("**/*.model")) - if not model_files: - print(f"[ERROR] Model file not found in {temp_path}") - return None - model_path = model_files[0] - - # Score - couplings_model = CouplingsModel(str(model_path)) - dms_df = pd.read_csv(dms_csv_path) - - # Clean and Filter - mutation_col = "mutant" - dms_df["mutant_clean"] = ( - dms_df[mutation_col] - .astype(str) - .str.upper() - .str.replace("T", "U") - .str.replace(" ", "") - ) - - # Strict Regex - valid_mask = dms_df["mutant_clean"].str.match( - r"^([A-Z]\d+[A-Z])(,[A-Z]\d+[A-Z])*$" - ) - valid_rows = dms_df[valid_mask] - - if len(valid_rows) < len(dms_df) - 1: - raise ValueError("{dms_csv_path} contains invalid dms rows") - - # Score and save in output_file - scoring_df = valid_rows.copy() - scoring_df["mutant"] = scoring_df["mutant_clean"] - predictions_df = predict_mutation_table( - couplings_model, scoring_df, "prediction_epistatic" - ) - score_map = predictions_df.set_index("mutant")["prediction_epistatic"] - dms_df["prediction_epistatic"] = dms_df["mutant_clean"].map(score_map) - output_file = temp_path / f"{job_name}_prediction_epistatic.csv" - dms_df.drop(columns=["mutant_clean"]).to_csv(output_file, index=False) - - return output_file - - except Exception as error: - print(f"[ERROR] EVcouplings/Scoring failed: {error}") - return None - - -def main(): - parser = argparse.ArgumentParser( - description="Score RNA DMS assays using EVmutation" - ) - parser.add_argument("--rfam_dir", type=Path, required=True) - parser.add_argument("--ref_sheet", type=Path, required=True) - parser.add_argument("--dms_dir", type=Path, required=True) - parser.add_argument("--out_dir", type=Path, required=True) - parser.add_argument("--tmp_dir", type=Path, default=Path("./ev_tmp")) - parser.add_argument("--cpu", type=int, default=4) - args = parser.parse_args() - - args.out_dir.mkdir(exist_ok=True) - args.tmp_dir.mkdir(exist_ok=True) - - rfam_cm_path = args.rfam_dir / "Rfam.cm" - if not rfam_cm_path.exists(): - sys.exit(f"[ERROR] Rfam.cm missing in {args.rfam_dir}") - - reference_df = pd.read_csv(args.ref_sheet) - for _, row in reference_df.iterrows(): - name = row["DMS_ID"] - raw_seq = row["RAW_CONSTRUCT_SEQ"] - sequence = raw_seq.upper().replace("T", "U").replace("N", "-") - output_path = args.out_dir / f"{name}.csv" - - dms_path = args.dms_dir / f"{name}.csv" - if output_path.exists() or not dms_path.exists(): - continue - - print(f"--- Processing {name} ---") - assay_temp_dir = args.tmp_dir / name - if assay_temp_dir.exists(): - shutil.rmtree(assay_temp_dir) - assay_temp_dir.mkdir() - - try: - rfam_id = get_rfam_match( - sequence, name, args.rfam_dir, assay_temp_dir, args.cpu - ) - - if rfam_id: - print(f" > Aligning to {rfam_id}...") - aligned_a2m = prepare_alignment( - rfam_id, sequence, name, args.rfam_dir, assay_temp_dir - ) - - print(" > Running Pipeline...") - result_file = run_evcouplings( - name, aligned_a2m, dms_path, name, assay_temp_dir, args.cpu - ) - - if result_file: - shutil.copy(result_file, output_path) - print(f" [SUCCESS] Saved {output_path}") - else: - print(" [ERROR] Prediction failed.") - else: - print(" [SKIP] No Rfam match.") - - except Exception as error: - print(f" [ERROR] Exception processing {name}: {error}") - raise error - - finally: - if assay_temp_dir.exists(): - shutil.rmtree(assay_temp_dir) - - -if __name__ == "__main__": - main() diff --git a/fitness/baselines/EVmutation/run.sh b/fitness/baselines/EVmutation/run.sh deleted file mode 100755 index df6bd42..0000000 --- a/fitness/baselines/EVmutation/run.sh +++ /dev/null @@ -1,19 +0,0 @@ -#!/bin/bash - -# Path to directory containing Rfam.cm and alignments/ folder -export rfam_dir="/n/lw_groups/hms/sysbio/marks/lab/databases/Rfam-15.0" -export ref_sheet="../../reference_sheet_final.csv" -export dms_dir="../../assays" -export out_dir="../../model_predictions/EVmutation" -export tmp_dir="./tmp_ev" - -mkdir -p "$out_dir" -mkdir -p "$tmp_dir" - -./compute_fitness.py \ - --rfam_dir "$rfam_dir" \ - --ref_sheet "$ref_sheet" \ - --dms_dir "$dms_dir" \ - --out_dir "$out_dir" \ - --tmp_dir "$tmp_dir" \ - --cpu 8 diff --git a/fitness/baselines/Evo/score_evo.sh b/fitness/baselines/Evo/score_evo.sh deleted file mode 100644 index 29db7e7..0000000 --- a/fitness/baselines/Evo/score_evo.sh +++ /dev/null @@ -1,20 +0,0 @@ -#!/bin/bash - -# script used to score both evo models. (evo-1-8k-base and evo-1.5-8k-base) -export model_name="evo-1.5-8k-base" - -export reference_sheet="reference_sheet.csv" -export output_scores_dir="path/to/output/scores/dir" -export dms_data_dir="path/to/dms/data/dir" - -# Get the current index from the array (0-31) -DMS_index=0 - -# Run the scoring script with the array task ID -python score_evo_single_dms.py \ - --row_id "$DMS_index" \ - --ref_sheet "$reference_sheet" \ - --dms_dir_path "$dms_data_dir" \ - --output_dir_path "$output_scores_dir" \ - --model_name "$model_name" - diff --git a/fitness/baselines/Evo/score_evo2.sh b/fitness/baselines/Evo/score_evo2.sh deleted file mode 100644 index d3243a2..0000000 --- a/fitness/baselines/Evo/score_evo2.sh +++ /dev/null @@ -1,37 +0,0 @@ -#!/bin/bash - -# Script used to score the Evo 2 models (evo2_40b by default; also evo2_7b, etc.) -# with the official evo2 package (https://github.com/ArcInstitute/evo2). -# Variants are scored by their mean per-token log-likelihood, averaged over the -# forward and reverse-complement strands (the evo2 baseline convention; pass -# --no-average_reverse_complement for forward strand only). -# -# evo2_40b requires FP8 via Transformer Engine on a Hopper GPU and does not fit -# on a single 80GB GPU; Vortex shards it across the visible GPUs, so select them -# with CUDA_VISIBLE_DEVICES and do not call .to(device). -export model_name="evo2_40b" - -export reference_sheet="reference_sheet_final.csv" -# Write predictions under a folder named "evo2_40b" so they line up with the -# "evo2_40b" entry in merge_scoring_files.py (which reads the evo2_40b_score -# column from model_predictions/evo2_40b/). -export output_scores_dir="path/to/model_predictions/evo2_40b" -export dms_data_dir="path/to/dms/data/dir" -# Optional: path to a pre-merged evo2 checkpoint for fully-offline loading on -# air-gapped compute nodes (leave empty to download from HuggingFace). -export local_path="" - -# Reference-sheet row to score. Set by a Slurm array job (0-69), or defaults -# to 0 when run directly. -DMS_index=${SLURM_ARRAY_TASK_ID:-0} - -extra_args=() -[ -n "$local_path" ] && extra_args+=(--local_path "$local_path") - -python score_evo2_single_dms.py \ - --row_id "$DMS_index" \ - --ref_sheet "$reference_sheet" \ - --dms_dir_path "$dms_data_dir" \ - --output_dir_path "$output_scores_dir" \ - --model_name "$model_name" \ - "${extra_args[@]}" diff --git a/fitness/baselines/Evo/score_evo2_single_dms.py b/fitness/baselines/Evo/score_evo2_single_dms.py deleted file mode 100644 index 7bf56b4..0000000 --- a/fitness/baselines/Evo/score_evo2_single_dms.py +++ /dev/null @@ -1,305 +0,0 @@ -#!/usr/bin/env python3 -"""Score one or more RNAGym assays with the official Evo 2 predictor. - -Multiple ``--row_ids`` share one model load. Vortex controls model placement -and sharding across the visible GPUs, so this script never moves the model. -""" - -import argparse -import re -import sys -from pathlib import Path -from tempfile import NamedTemporaryFile - -import numpy as np -import pandas as pd -import torch -from scipy.stats import spearmanr - - -def _nonnegative_int(value: str) -> int: - """Parse a nonnegative command-line integer.""" - parsed = int(value) - if parsed < 0: - raise argparse.ArgumentTypeError("must be nonnegative") - return parsed - - -def _positive_int(value: str) -> int: - """Parse a positive command-line integer.""" - parsed = int(value) - if parsed < 1: - raise argparse.ArgumentTypeError("must be positive") - return parsed - - -def effective_length(sequence_length: int, prepend_bos: bool, fp8: bool) -> int: - """Return the sequence dimension used by the largest input projection.""" - length = sequence_length + int(prepend_bos) - return ((length + 15) // 16) * 16 if fp8 else length - - -def load_dms_data(dms_dir: Path, dms_id: str) -> pd.DataFrame: - """Load and validate an assay table.""" - path = dms_dir / f"{dms_id}.csv" - data = pd.read_csv(path) - required = {"mutant", "DMS_score", "sequence"} - missing = sorted(required - set(data.columns)) - if missing: - raise ValueError(f"{path} is missing columns: {missing}") - return data - - -def load_reference_data(reference_file: Path, row_ids: list[int]) -> list[str]: - """Return the DMS IDs at the selected reference-sheet rows.""" - reference = pd.read_csv(reference_file, encoding="utf-8-sig") - if "DMS_ID" not in reference: - raise ValueError(f"{reference_file} is missing column 'DMS_ID'") - - invalid = [row_id for row_id in row_ids if row_id < 0 or row_id >= len(reference)] - if invalid: - raise ValueError( - f"Reference rows {invalid} fall outside 0-{len(reference) - 1}" - ) - dms_ids = reference.iloc[row_ids]["DMS_ID"] - missing = [row_id for row_id, dms_id in zip(row_ids, dms_ids) if pd.isna(dms_id)] - if missing: - raise ValueError(f"DMS_ID is missing for reference rows {missing}") - return dms_ids.astype(str).tolist() - - -def parse_args(argv=None) -> argparse.Namespace: - """Parse command-line arguments.""" - parser = argparse.ArgumentParser( - description="Score one or more RNAGym assays with Evo 2", - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - ) - rows = parser.add_mutually_exclusive_group(required=True) - rows.add_argument( - "--row_id", - type=_nonnegative_int, - help="Reference-sheet row to score", - ) - rows.add_argument( - "--row_ids", - help="Comma-separated rows and ranges to score in one model load, e.g. 0-8,11-32", - ) - parser.add_argument( - "--ref_sheet", - type=Path, - required=True, - help="Reference sheet containing DMS_ID", - ) - parser.add_argument( - "--dms_dir_path", - type=Path, - required=True, - help="Directory containing assay CSVs", - ) - parser.add_argument( - "--output_dir_path", - type=Path, - required=True, - help="Directory for scored CSVs", - ) - parser.add_argument( - "--model_name", - default="evo2_40b", - help="Evo 2 checkpoint name", - ) - parser.add_argument( - "--local_path", - help="Optional local checkpoint path", - ) - parser.add_argument( - "--batch_size", - type=_positive_int, - default=1, - help="Sequences per forward pass", - ) - parser.add_argument( - "--max_tokens_per_batch", - type=_positive_int, - help="Derive each assay's batch size from this token budget", - ) - parser.add_argument( - "--reduce_method", - choices=("mean", "sum"), - default="mean", - help="Per-sequence log-likelihood reduction", - ) - parser.add_argument( - "--prepend_bos", - action="store_true", - help="Prepend the BOS/EOD token", - ) - parser.add_argument( - "--average_reverse_complement", - action=argparse.BooleanOptionalAction, - default=True, - help="Average forward and reverse-complement scores", - ) - parser.add_argument( - "--require_fp8", - action="store_true", - help="Require FP8 input projections in the constructed model", - ) - parser.add_argument( - "--overwrite", - action="store_true", - help="Replace existing scored CSVs", - ) - return parser.parse_args(argv) - - -def parse_row_ids(specification: str) -> list[int]: - """Expand a row selection such as ``0-8,11-32``.""" - rows = set() - for selection in specification.split(","): - match = re.fullmatch(r"\s*(\d+)(?:-(\d+))?\s*", selection) - if match is None: - raise ValueError(f"Invalid --row_ids selection: {selection!r}") - start = int(match.group(1)) - end = int(match.group(2) or start) - if end < start: - raise ValueError(f"Descending --row_ids range: {selection!r}") - rows.update(range(start, end + 1)) - return sorted(rows) - - -def preprocess_sequence(sequence: str) -> str: - """Convert an RNA or DNA sequence to uppercase DNA.""" - return sequence.strip().upper().replace("U", "T") - - -def score_assay(model, args: argparse.Namespace, dms_id: str, fp8: bool) -> float: - """Score one assay, write its prediction table, and return Spearman rho.""" - data = load_dms_data(args.dms_dir_path, dms_id) - raw_sequences = data["sequence"] - invalid_sequences = raw_sequences.isna() | raw_sequences.astype( - "string" - ).str.strip().eq("") - if invalid_sequences.any(): - count = int(invalid_sequences.sum()) - rows = list(data.index[invalid_sequences][:5]) - raise ValueError( - f"{dms_id} has {count} missing or empty sequences, including rows {rows}" - ) - sequences = [preprocess_sequence(value) for value in raw_sequences] - if not sequences: - raise ValueError(f"{dms_id} has no nonempty sequences") - - lengths = {len(sequence) for sequence in sequences} - if len(lengths) != 1: - raise ValueError(f"{dms_id} contains mixed sequence lengths: {sorted(lengths)}") - sequence_length = lengths.pop() - batch_size = args.batch_size - if args.max_tokens_per_batch is not None: - length = effective_length(sequence_length, args.prepend_bos, fp8) - batch_size = max(1, args.max_tokens_per_batch // length) - - print( - f"{dms_id}: scoring {len(sequences)} sequences of length {sequence_length} " - f"in batches of {batch_size}" - ) - scores = np.asarray( - model.score_sequences( - sequences, - batch_size=batch_size, - prepend_bos=args.prepend_bos, - reduce_method=args.reduce_method, - average_reverse_complement=args.average_reverse_complement, - ), - dtype=float, - ) - if scores.shape != (len(sequences),): - raise ValueError( - f"{dms_id} returned score shape {scores.shape}, expected {(len(sequences),)}" - ) - if not np.isfinite(scores).all(): - raise FloatingPointError(f"{dms_id} returned nonfinite model scores") - - assay_scores = pd.to_numeric(data["DMS_score"], errors="coerce").to_numpy( - dtype=float - ) - if not np.isfinite(assay_scores).all(): - raise ValueError(f"{dms_id} contains missing or nonfinite DMS scores") - - score_column = f"{args.model_name}_score" - data[score_column] = scores - if len(data) < 2: - correlation = pvalue = float("nan") - else: - result = spearmanr(assay_scores, scores) - correlation, pvalue = result.statistic, result.pvalue - - output_file = args.output_dir_path / f"{dms_id}.csv" - write_csv_atomically(data, output_file) - print(f"{dms_id}: Spearman={correlation:.3f} p={pvalue:.2e} -> {output_file}") - return correlation - - -def write_csv_atomically(data: pd.DataFrame, output_file: Path) -> None: - """Replace an output only after its complete CSV has been written.""" - permissions = output_file.stat().st_mode & 0o777 if output_file.exists() else 0o644 - with NamedTemporaryFile( - dir=output_file.parent, - prefix=f".{output_file.name}.", - suffix=".tmp", - delete=False, - ) as handle: - temporary_file = Path(handle.name) - try: - data.to_csv(temporary_file, index=False) - temporary_file.chmod(permissions) - temporary_file.replace(output_file) - finally: - temporary_file.unlink(missing_ok=True) - - -def run(args: argparse.Namespace, model_factory=None) -> None: - """Load Evo 2 once and score all requested assays.""" - row_ids = [args.row_id] if args.row_id is not None else parse_row_ids(args.row_ids) - dms_ids = load_reference_data(args.ref_sheet, row_ids) - args.output_dir_path.mkdir(parents=True, exist_ok=True) - pending = [ - (row_id, dms_id) - for row_id, dms_id in zip(row_ids, dms_ids) - if args.overwrite or not (args.output_dir_path / f"{dms_id}.csv").exists() - ] - if not pending: - print("Nothing to score") - return - - if not torch.cuda.is_available(): - print("WARNING: Evo 2 requires CUDA", file=sys.stderr) - print(f"Loading {args.model_name} on {torch.cuda.device_count()} visible GPUs") - if model_factory is None: - from evo2 import Evo2 - - model_factory = Evo2 - model = model_factory(args.model_name, local_path=args.local_path) - fp8 = bool(model.model.config.get("use_fp8_input_projections", False)) - if args.require_fp8 and not fp8: - raise RuntimeError("The constructed model does not use FP8 input projections") - - failures = [] - for row_id, dms_id in pending: - try: - score_assay(model, args, dms_id, fp8) - except Exception as error: - if len(pending) == 1: - raise - print(f"Row {row_id} ({dms_id}) failed: {error}", file=sys.stderr) - failures.append(dms_id) - if failures: - raise RuntimeError(f"Failed assays: {', '.join(failures)}") - - -def main() -> None: - """Run the Evo 2 scoring command.""" - run(parse_args()) - - -if __name__ == "__main__": - main() diff --git a/fitness/baselines/Evo/score_evo_single_dms.py b/fitness/baselines/Evo/score_evo_single_dms.py deleted file mode 100644 index 0293f17..0000000 --- a/fitness/baselines/Evo/score_evo_single_dms.py +++ /dev/null @@ -1,282 +0,0 @@ -#!/usr/bin/env python3 -""" -Script to run Evo model inference on DMS assay sequences. -Takes a reference sheet and row ID to process specific assays. -""" - -import argparse -import os -import sys -from pathlib import Path - -import pandas as pd -import numpy as np -import torch -from scipy.stats import spearmanr -from tqdm.auto import tqdm -from evo import Evo -from evo.scoring import prepare_batch, logits_to_logprobs -from torch.utils.data import Dataset, DataLoader - - -def preprocess_sequence(sequence: str) -> str: - """ - Preprocess RNA sequence for DNA model: - - Convert RNA (U) to DNA (T) - - Convert to uppercase - - Remove any whitespace - - Args: - sequence: Input RNA or DNA sequence - - Returns: - str: Preprocessed DNA sequence - """ - return sequence.strip().upper().replace("U", "T") - - -def parse_args(): - """Parse command line arguments.""" - parser = argparse.ArgumentParser( - description="Run Evo model inference on DMS assay sequences." - ) - parser.add_argument( - "--row_id", - type=int, - required=True, - help="Row ID in the reference sheet to process", - ) - parser.add_argument( - "--ref_sheet", - type=str, - required=True, - help="Path to reference sheet containing DMS_ID column", - ) - parser.add_argument( - "--dms_dir_path", - type=str, - required=True, - help="Directory containing DMS CSV files", - ) - parser.add_argument( - "--output_dir_path", - type=str, - required=True, - help="Directory to save output files", - ) - parser.add_argument( - "--device", - type=str, - default="cuda:0" if torch.cuda.is_available() else "cpu", - help="Device to run inference on (default: cuda:0 if available, else cpu)", - ) - parser.add_argument( - "--model_name", - type=str, - default="evo-1-8k-base", - help="Name of the Evo model to use (default: evo-1-8k-base)", - ) - parser.add_argument( - "--batch_size", - type=int, - default=128, - help="Batch size for processing sequences (default: 128)", - ) - return parser.parse_args() - - -def load_reference_data(ref_sheet_path: str, row_id: int) -> str: - """ - Load reference sheet and get DMS_ID for specified row. - - Args: - ref_sheet_path: Path to reference sheet - row_id: Row ID to process - - Returns: - str: DMS_ID for specified row - - Raises: - ValueError: If row_id is not found or DMS_ID is missing - """ - try: - ref_df = pd.read_csv(ref_sheet_path) - if row_id >= len(ref_df): - raise ValueError( - f"Row ID {row_id} exceeds number of rows in reference sheet" - ) - - dms_id = ref_df.loc[row_id, "DMS_ID"] - if pd.isna(dms_id): - raise ValueError(f"DMS_ID is missing for row {row_id}") - - return str(dms_id) - - except FileNotFoundError: - raise FileNotFoundError(f"Reference sheet not found: {ref_sheet_path}") - except KeyError: - raise KeyError("Reference sheet must contain 'DMS_ID' column") - - -def load_dms_data(dms_dir_path: str, dms_id: str) -> pd.DataFrame: - """ - Load DMS data for specified DMS_ID. - - Args: - dms_dir_path: Directory containing DMS files - dms_id: DMS ID to process - - Returns: - pd.DataFrame: DataFrame containing sequences to process - - Raises: - FileNotFoundError: If DMS file is not found - """ - dms_file = Path(dms_dir_path) / f"{dms_id}.csv" - if not dms_file.exists(): - raise FileNotFoundError(f"DMS file not found: {dms_file}") - - df = pd.read_csv(dms_file) - required_cols = ["mutant", "DMS_score", "sequence"] - missing_cols = [col for col in required_cols if col not in df.columns] - if missing_cols: - raise ValueError(f"Missing required columns in DMS file: {missing_cols}") - - return df - - -class SequenceDataset(Dataset): - """Dataset for processing sequences in batches.""" - - def __init__(self, sequences): - self.sequences = sequences - - def __len__(self): - return len(self.sequences) - - def __getitem__(self, idx): - return self.sequences[idx] - - -def run_inference( - model, tokenizer, sequences: list, device: str, batch_size: int -) -> np.ndarray: - """ - Run Evo model inference on sequences in batches. - - Args: - model: Loaded Evo model - tokenizer: Evo tokenizer - sequences: List of sequences to process - device: Device to run inference on - batch_size: Number of sequences to process in each batch - - Returns: - np.ndarray: Log probabilities array - """ - dataset = SequenceDataset(sequences) - dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=False) - - all_logprobs = [] - - # Create progress bar - total_batches = len(dataloader) - pbar = tqdm(total=len(sequences), desc="Scoring sequences", unit="seq") - - for batch_seqs in dataloader: - input_ids, seq_lengths = prepare_batch( - batch_seqs, - tokenizer, - prepend_bos=True, - device=device, - ) - assert len(seq_lengths) == input_ids.shape[0] - - with torch.inference_mode(): - logits, *_ = model(input_ids) - - # Calculate log probabilities - logprobs = logits_to_logprobs(logits, input_ids, trim_bos=True) - - # Store batch results - all_logprobs.append(logprobs.float().cpu().numpy()) - - # Update progress bar by batch size - pbar.update(len(batch_seqs)) - - pbar.close() - - # Concatenate all batches - return np.concatenate(all_logprobs) - - -def main(): - args = parse_args() - - # Create output directory if it doesn't exist - output_dir = Path(args.output_dir_path) - output_dir.mkdir(parents=True, exist_ok=True) - - try: - # Load DMS ID from reference sheet - dms_id = load_reference_data(args.ref_sheet, args.row_id) - print(f"Processing DMS ID: {dms_id}") - - # Load DMS data - dms_df = load_dms_data(args.dms_dir_path, dms_id) - - # Preprocess sequences - print("Preprocessing sequences...") - sequences = [ - preprocess_sequence(seq) - for seq in tqdm( - dms_df["sequence"].tolist(), desc="Preprocessing", unit="seq" - ) - ] - print(f"Preprocessed {len(sequences)} sequences") - - # Initialize model - print("Initializing Evo model...") - evo_model = Evo(args.model_name) - model, tokenizer = evo_model.model, evo_model.tokenizer - model.to(args.device) - model.eval() - - # Run inference in batches - print(f"Running inference with batch size {args.batch_size}...") - logprobs = run_inference( - model, tokenizer, sequences, args.device, args.batch_size - ) - - # Calculate sequence scores (mean log probability) - sequence_scores = np.mean(logprobs, axis=1) - - # Add scores to DataFrame - score_column = f"{args.model_name.replace('-', '_')}_score" - dms_df[score_column] = sequence_scores - - # Calculate Spearman correlation - correlation, pvalue = spearmanr(dms_df["DMS_score"], dms_df[score_column]) - - # Save results - output_file = output_dir / f"{dms_id}.csv" - dms_df.to_csv(output_file, index=False) - print(f"Saved results to: {output_file}") - - # Print summary statistics - print("\nSummary:") - print(f"Number of sequences: {len(sequences)}") - print( - f"Spearman correlation with DMS scores: {correlation:.3f} (p-value: {pvalue:.2e})" - ) - print(f"Output saved to: {output_file}") - - except Exception as e: - print(f"Error: {str(e)}", file=sys.stderr) - sys.exit(1) - - -if __name__ == "__main__": - main() - -# python score_evo_single_dms.py --row_id 0 --ref_sheet reference_sheet.csv --dms_dir_path fitness_processed_assays --output_dir_path evo_output --model_name evo-1-8k-base diff --git a/fitness/baselines/GenSLM/compute_fitness.py b/fitness/baselines/GenSLM/compute_fitness.py deleted file mode 100644 index 9921d2f..0000000 --- a/fitness/baselines/GenSLM/compute_fitness.py +++ /dev/null @@ -1,159 +0,0 @@ -import os -import argparse -import torch -import torch.nn as nn -import numpy as np -import pandas as pd -from tqdm.auto import tqdm -from torch.utils.data import DataLoader -from genslm import GenSLM, SequenceDataset - - -def get_sequences(wt_sequence, df): - # Function to apply a single mutation - def apply_mutation(sequence, mutation): - sequence = sequence.replace("U", "T") - possible_bases = ["A", "T", "C", "G", "N", ""] - mutation = mutation.replace(" ", "") - pos = int(mutation[1:-1]) - 1 # Get the position, 1-based to 0-based index - new_base = mutation[-1] # Get the new base - old_base = mutation[0] - old_base = "T" if old_base == "U" else old_base - new_base = "T" if new_base == "U" else new_base - - assert old_base in possible_bases, mutation - assert new_base in possible_bases, mutation - - if old_base == "N": - # This is going to be an insertion - mutated_sequence = sequence[: pos + 1] + new_base + sequence[pos + 1 :] - elif new_base == "": - # This is going to be a deletion - mutated_sequence = sequence[:pos] + sequence[pos + 1 :] - else: - # This is a substitution - assert old_base == sequence[pos], mutation - mutated_sequence = sequence[:pos] + new_base + sequence[pos + 1 :] - - return mutated_sequence - - # Function to apply multiple mutations - def apply_mutations(sequence, mutations): - for mutation in mutations.split(","): - sequence = apply_mutation(sequence, mutation) - return sequence - - # Determine which column to use for mutations - mutation_column = ( - "mutant" - if "mutant" in df.columns - else "mutation" - if "mutation" in df.columns - else "mutations" - if "mutations" in df.columns - else None - ) - if mutation_column: - # Apply the mutations to create a new column with mutated sequences - df["mutated_sequence"] = df[mutation_column].apply( - lambda x: apply_mutations(wt_sequence, x) - ) - else: - raise ValueError("No 'mutant' or 'mutation' column found in the DataFrame") - - return df - - -def process_single_row(row, model, device, base_dir, results_dir, score_column): - dataset = row["fitness filename"] - if "snoRNA" in dataset: - return - df_path = os.path.join(base_dir, f"{dataset}.csv") - df = pd.read_csv(df_path) - df.columns = df.columns.str.lower() - df.dropna(inplace=True) - df = df.loc[:, ~df.columns.duplicated()] - wt_seq = row["Raw Construct Sequence"].upper() - try: - sequences = get_sequences(wt_seq, df) - except AssertionError as e: - print("assertion error", e, "in", dataset) - return - - output_file = os.path.join(results_dir, f"{dataset}.csv") - - seq_length = min( - model.seq_length, sequences["mutated_sequence"].str.len().max() + 2 - ) - if model.seq_length < sequences["mutated_sequence"].str.len().max() + 2: - print("warning: max str length exceeded") - dataset = SequenceDataset( - sequences["mutated_sequence"], seq_length, model.tokenizer - ) - dataloader = DataLoader(dataset, batch_size=4) - - loss_fn = nn.CrossEntropyLoss(reduction="none") - losses = [] - lengths = [] - with torch.no_grad(): - for batch in tqdm(dataloader): - outputs = model( - batch["input_ids"].to(device), - batch["attention_mask"].to(device), - output_hidden_states=True, - ) - loss = ( - loss_fn(outputs.logits.cpu().permute((0, 2, 1)), batch["input_ids"]) - * batch["attention_mask"].squeeze(1) - ).sum(1) - losses.append(loss) - lengths.append(batch["attention_mask"].squeeze(1).sum(1)) - losses = np.concatenate(losses) - lengths = np.concatenate(lengths) - - sequences[score_column] = losses - sequences.to_csv(output_file, index=False) - - -def main(args): - model = GenSLM( - "genslm_2.5B_patric", model_cache_dir=os.path.join(args.checkpoint_dir, "2.5B") - ) - model.eval() - - # Select GPU device if it is available, else use CPU - device = "cuda" if torch.cuda.is_available() else "cpu" - model.to(device) - - base_dir = args.dms_directory - results_dir = args.output_directory - score_column = "logit_scores" - wt_seqs = pd.read_csv(args.reference_sheet, encoding="latin-1") - wt_seqs.dropna(inplace=True) - wt_seqs["Year"] = wt_seqs["Year"].astype(int) - wt_seqs["First Author Last Name"] = wt_seqs["First Author Last Name"].astype(str) - wt_seqs["Molecule Type"] = wt_seqs["Molecule Type"].astype(str) - - # Select the row corresponding to the task ID - row = wt_seqs.iloc[args.task_id] - - process_single_row(row, model, device, base_dir, results_dir, score_column) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("--reference_sheet", type=str, required=True) - parser.add_argument("--task_id", type=int, required=True) - parser.add_argument( - "--checkpoint_dir", type=str, help="GenSLM checkpoints directory" - ) - parser.add_argument( - "--dms_directory", - type=str, - help="Directory containing the mutational datasets to be scored", - ) - parser.add_argument( - "--output_directory", type=str, help="Directory for scored fitness files" - ) - args = parser.parse_args() - main(args) diff --git a/fitness/baselines/GenSLM/run_model.sh b/fitness/baselines/GenSLM/run_model.sh deleted file mode 100644 index be9bc6a..0000000 --- a/fitness/baselines/GenSLM/run_model.sh +++ /dev/null @@ -1,20 +0,0 @@ -#!/bin/bash - -# the directory containing the GenSLM checkpoints -# downloaded from https://github.com/ramanathanlab/genslm?tab=readme-ov-file#usage -# should contain a file named 2.5B/patric_2.5b_epoch00_val_los_0.29_bias_removed.pt -export checkpoint_dir="path/to/checkpoint/dir" - -export reference_sheet="reference_sheet.csv" -export output_scores_dir="path/to/output/scores/dir" -export dms_data_dir="path/to/dms/data/dir" - -# Get the current index from the array (0-31) -DMS_index=0 - -python compute_fitness.py \ - --reference_sheet "$reference_sheet" \ - --task_id "$DMS_index" \ - --checkpoint_dir "$checkpoint_dir" \ - --dms_directory "$dms_data_dir" \ - --output_directory "$output_scores_dir" diff --git a/fitness/baselines/Nucleotide_Transformer/run.sh b/fitness/baselines/Nucleotide_Transformer/run.sh deleted file mode 100644 index 6a8dce1..0000000 --- a/fitness/baselines/Nucleotide_Transformer/run.sh +++ /dev/null @@ -1,17 +0,0 @@ -#!/usr/bin/env bash -set -euo pipefail - -script_dir=$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd) - -ref_sheet="$script_dir/../../reference_sheet_final.csv" -dms_dir_path="path/to/dms/data/dir" -output_dir_path="path/to/output/scores/dir" -model_name="InstaDeepAI/NTv3_650M_pre" -row_id=${SLURM_ARRAY_TASK_ID:-0} - -python "$script_dir/compute_fitness.py" \ - --row_id "$row_id" \ - --ref_sheet "$ref_sheet" \ - --dms_dir_path "$dms_dir_path" \ - --output_dir_path "$output_dir_path" \ - --model_name "$model_name" diff --git a/fitness/baselines/Orthrus/score_orthrus.sh b/fitness/baselines/Orthrus/score_orthrus.sh deleted file mode 100644 index 92f3b1e..0000000 --- a/fitness/baselines/Orthrus/score_orthrus.sh +++ /dev/null @@ -1,20 +0,0 @@ -#!/usr/bin/env bash -set -euo pipefail - -script_dir=$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd) - -# Orthrus requires causal-conv1d, mamba-ssm and transformers remote model code -export model_name="antichronology/orthrus-mlm-6-track" - -export reference_sheet="reference_sheet.csv" -export output_scores_dir="path/to/model_predictions/orthrus_4fill" -export dms_data_dir="path/to/dms/data/dir" - -DMS_index=${SLURM_ARRAY_TASK_ID:-0} - -python "$script_dir/score_orthrus_single_dms.py" \ - --row_id "$DMS_index" \ - --ref_sheet "$reference_sheet" \ - --dms_dir_path "$dms_data_dir" \ - --output_dir_path "$output_scores_dir" \ - --model_name "$model_name" diff --git a/fitness/baselines/RNAERNIE/compute_fitness.py b/fitness/baselines/RNAERNIE/compute_fitness.py deleted file mode 100644 index 0472c6b..0000000 --- a/fitness/baselines/RNAERNIE/compute_fitness.py +++ /dev/null @@ -1,122 +0,0 @@ -import argparse -import torch -import torch.nn.functional as F -import pandas as pd -from paddlenlp.transformers import ErnieForMaskedLM -import paddle -from src.rna_ernie import BatchConverter - - -# Function Definitions -def construct_file_path(directory, filename, extension=".csv"): - """Constructs a full file path given a directory, filename, and extension.""" - return f"{directory}{filename}{extension}" - - -def calculate_mutation_score(mutation, sequence, token_probs, alphabet, offset): - """Calculates the mutation score for a given mutation.""" - score = 0 - for mut in mutation.split(","): - mut = mut.strip() - wt, idx, mt = mut[0], int(mut[1:-1]) - offset, mut[-1] - assert sequence[idx] == wt, "Mismatch between sequence and wildtype." - - wt_encoded, mt_encoded = alphabet[wt], alphabet[mt] - score += ( - token_probs[0, 1 + idx, mt_encoded] - token_probs[0, 1 + idx, wt_encoded] - ).item() - return score - - -def main(args): - # Load Model - language_model = ErnieForMaskedLM.from_pretrained(args.model_checkpoint) - language_model.eval() - - # Initialize BatchConverter - batch_converter = BatchConverter( - k_mer=1, vocab_path=args.vocab_path, batch_size=256, max_seq_len=512 - ) - - # Load Reference Data - reference_data = pd.read_csv(args.reference_sequences) - reference_data["path"] = reference_data["DMS_ID"].apply( - lambda x: construct_file_path(args.dms_directory, x) - ) - reference_data["RAW_CONSTRUCT_SEQ"] = reference_data[ - "RAW_CONSTRUCT_SEQ" - ].str.replace("U", "T") - - alphabet = batch_converter.tokenizer.vocab.token_to_idx - - # Processing Loop - for idx, row in reference_data.iterrows(): - experiment_id = row["DMS_ID"] - wildtype_sequence = row["RAW_CONSTRUCT_SEQ"].upper() - mutation_file_path = row["path"] - - # Prepare Data for Model - data_batch = [(experiment_id, wildtype_sequence)] - - for _, _, input_ids in batch_converter(data_batch): - with paddle.no_grad(): - logits = language_model(input_ids).detach() - logits_tensor = torch.tensor( - logits.numpy() - ) # Convert Paddle tensor to PyTorch - probabilities = F.softmax(logits_tensor, dim=2) - - # Load Mutations and Format - mutation_data = pd.read_csv(mutation_file_path).dropna(subset=["mutant"]) - mutation_data["mutant"] = mutation_data["mutant"].str.replace("U", "T") - mutation_list = mutation_data["mutant"].tolist() - - # Score Mutations - mutation_scores = [ - calculate_mutation_score( - mut, wildtype_sequence, probabilities, alphabet, offset=1 - ) - for mut in mutation_list - ] - - # Append Scores and Save - mutation_data["Mutation_Scores"] = mutation_scores - output_path = construct_file_path(args.output_directory, experiment_id) - mutation_data.to_csv(output_path, index=False) - print(f"Scores saved to {output_path}") - - -if __name__ == "__main__": - parser = argparse.ArgumentParser( - description="Calculate mutation scores using an RNA language model." - ) - parser.add_argument( - "--reference_sequences", - type=str, - required=True, - help="Path to the reference sequences CSV file.", - ) - parser.add_argument( - "--dms_directory", - type=str, - required=True, - help="Directory containing DMS files.", - ) - parser.add_argument( - "--output_directory", - type=str, - required=True, - help="Directory to save output files.", - ) - parser.add_argument( - "--model_checkpoint", - type=str, - required=True, - help="Path to the model checkpoint.", - ) - parser.add_argument( - "--vocab_path", type=str, required=True, help="Path to the vocabulary file." - ) - - args = parser.parse_args() - main(args) diff --git a/fitness/baselines/RNAERNIE/run.sh b/fitness/baselines/RNAERNIE/run.sh deleted file mode 100644 index c357b61..0000000 --- a/fitness/baselines/RNAERNIE/run.sh +++ /dev/null @@ -1,17 +0,0 @@ -#!/bin/bash - -# RNAErnie model -export model_checkpoint="src/" -export vocab_path="src/vocab_1MER.txt" - -export reference_sheet="../../reference_sheet.csv" -export dms_data_dir="../fitness_processed_assays/" -export output_scores_dir="../out" - - -python compute_fitness.py \ - --model_checkpoint "$model_checkpoint" \ - --reference_sequences "$reference_sheet" \ - --dms_directory "$dms_data_dir" \ - --output_directory "$output_scores_dir" \ - --vocab_path "$vocab_path" diff --git a/fitness/baselines/RNAERNIE/src/.ipynb_checkpoints/arg_utils-checkpoint.py b/fitness/baselines/RNAERNIE/src/.ipynb_checkpoints/arg_utils-checkpoint.py deleted file mode 100644 index 7db532f..0000000 --- a/fitness/baselines/RNAERNIE/src/.ipynb_checkpoints/arg_utils-checkpoint.py +++ /dev/null @@ -1,130 +0,0 @@ -""" -This module defines some common functions for argument parsing. - -Author: wangning(wangning.roci@gmail.com) -Date : 2022/12/7 7:41 PM -""" - -import argparse -import random - -import numpy as np - -import paddle -from paddlenlp.utils.log import logger - - -def set_seed(seed): - """set seed for random, numpy and paddle - - Args: - seed (int): seed value - """ - random.seed(seed) - np.random.seed(seed) - paddle.seed(seed) - - -def default_logdir(): - """generate default log dir - - Returns: - path.Path: path of log dir - """ - from datetime import datetime - - current_time = datetime.now().strftime("%b%d_%H-%M-%S") - return current_time - - -def str2bool(v): - """convert string args to boolean - - Args: - v (str): from args - - Returns: - boolean: True or False - """ - if isinstance(v, bool): - return v - if v.lower() in ('yes', 'true', 't', 'y', '1'): - return True - elif v.lower() in ('no', 'false', 'f', 'n', '0'): - return False - else: - raise argparse.ArgumentTypeError("Boolean value expected.") - - -def str2list(v): - """convert string to list - - Args: - v (str): from args - - Returns: - list: sperated by ',' - """ - if isinstance(v, list): - return v - elif isinstance(v, str): - vs = v.split(",") - return [v.strip() for v in vs] - else: - raise argparse.ArgumentTypeError("Str value seperated by ', ' expected.") - - -def str2intlist(v): - """convert string to int list - - Args: - v (string): from args - - Returns: - list: int list - """ - if isinstance(v, list): - return v - elif isinstance(v, str): - vs = v.split(",") - if vs[-1] == "": - vs = vs[:-1] - return [int(v.strip()) for v in vs] - else: - raise argparse.ArgumentTypeError("Str value seperated by ', ' expected.") - - -def list2str(list_value): - """convert list to string - - Args: - v (list): int list - - Returns: - str: s - """ - if isinstance(list_value, list): - res = "" - for x in list_value: - res = res + x + "," - return res[:-1] - else: - raise NotImplementedError - - -def print_config(args=None, key=""): - """print the configuration of the experiment - - Args: - args (argparse.Namespace): from args - - Returns: - None - """ - logger.debug("=" * 60) - - logger.debug('{:^40}'.format("{} Configuration Arguments".format(key))) - logger.debug('{:30}:{}'.format("paddle commit id", paddle.version.commit)) - - for k, v in vars(args).items(): - logger.debug('{:30}:{}'.format(k, v)) diff --git a/fitness/baselines/RNAERNIE/src/.ipynb_checkpoints/rna_ernie-checkpoint.py b/fitness/baselines/RNAERNIE/src/.ipynb_checkpoints/rna_ernie-checkpoint.py deleted file mode 100644 index ba68cac..0000000 --- a/fitness/baselines/RNAERNIE/src/.ipynb_checkpoints/rna_ernie-checkpoint.py +++ /dev/null @@ -1,104 +0,0 @@ -""" -This module inference sequences embeddings. -""" - -from Bio import SeqIO - -import paddle -from paddlenlp.utils.log import logger -from paddlenlp.data import Stack -from paddlenlp.transformers import ErnieModel - -from src.dataset_utils import seq2input_ids -from src.tokenizer_nuc import NUCTokenizer - - -class BatchConverter(object): - """Convert sequences to batch inputs. - """ - - def __init__(self, k_mer=1, vocab_path="./data/vocab/vocab_1MER.txt", batch_size=256, max_seq_len=512, is_pad=True, st_pos=0): - """this class predicts embeddings from RNA sequences list - - Args: - k_mer (int): k for splitting RNA sequences - vocab_path (str): root path to vocab file - batch_size (int): batch size - max_seq_len (int): max sequence to input sequences - st_pos (int): start position of the sequence - """ - self.tokenizer = NUCTokenizer( - k_mer=k_mer, - vocab_file=vocab_path, - ) - self.stack_fn = Stack() - - self.batch_size = batch_size - self.max_seq_len = max_seq_len - self.is_pad = is_pad - self.st_pos = st_pos - - def __call__(self, data): - """call function to convert sequences to batch inputs. - - Args: - data (List[tuple(name, sequence)], str): data or .fasta file to be converted - """ - - if type(data) == list: - # convert sequences to batch inputs - self.data = data - elif type(data) == str and (data.split(".")[-1] == "fasta" or data.split(".")[-1] == "fa"): - # load .fasta file with SeqIO - records = list(SeqIO.parse(data, "fasta")) - self.data = [(str(x.description), str(x.seq)) for x in records] - # return generator to iterate data by step batch_size - for d in range(0, len(self.data), self.batch_size): - raw_data = self.data[d:d + self.batch_size] - names = [x[0] for x in raw_data] - seqs = [x[1] for x in raw_data] - seqs = [x[self.st_pos:self.st_pos + self.max_seq_len - 2] - for x in seqs] - seqs = [x.upper().replace("U", "T") for x in seqs] - # 2 means [CLS] and [SEP] - input_ids = [seq2input_ids(x, self.tokenizer) for x in seqs] - if self.is_pad: - input_ids = [x + [0] * (self.max_seq_len - len(x)) - for x in input_ids] - input_ids = self.stack_fn(input_ids) - input_ids = paddle.to_tensor(input_ids) - yield names, seqs, input_ids - - -if __name__ == "__main__": - # ========== Set device - logger.debug("Set device.") - paddle.set_device("gpu") - - # ========== Prepare Data - data = [ - ("RNA1", "GGGUGCGAUCAUACCAGCACUAAUGCCCUCCUGGGAAGUCCUCGUGUUGCACCCCU"), - ("RNA2", "GGGUGUCGCUCAGUUGGUAGAGUGCUUGCCUGGCAUGCAAGAAACCUUGGUUCAAUCCCCAGCACUGCA"), - ("RNA3", "CGAUUCNCGUUCCC--CCGCCUCCA"), - ] - data = "./data/ft/seq_cls/nRC/test.fa" - - # ========== Batch Converter - logger.debug("Loading converter.") - batch_converter = BatchConverter(k_mer=1, - vocab_path="./data/vocab/vocab_1MER.txt", - batch_size=256, - max_seq_len=512) - - # ========== RNAErnie Model - rna_ernie = ErnieModel.from_pretrained( - "./output/BERT,ERNIE,MOTIF,PROMPT/checkpoint_final") - - # call batch_converter to convert sequences to batch inputs - for names, _, inputs_ids in batch_converter(data): - with paddle.no_grad(): - # extract whole sequence embeddings - embeddings = rna_ernie(inputs_ids)[0].detach() - # extract [CLS] token embedding - embeddings = embeddings[:, 0, :] - print(embeddings.shape) diff --git a/fitness/baselines/RNAERNIE/src/arg_utils.py b/fitness/baselines/RNAERNIE/src/arg_utils.py deleted file mode 100644 index 65a4bb8..0000000 --- a/fitness/baselines/RNAERNIE/src/arg_utils.py +++ /dev/null @@ -1,130 +0,0 @@ -""" -This module defines some common functions for argument parsing. - -Author: wangning(wangning.roci@gmail.com) -Date : 2022/12/7 7:41 PM -""" - -import argparse -import random - -import numpy as np - -import paddle -from paddlenlp.utils.log import logger - - -def set_seed(seed): - """set seed for random, numpy and paddle - - Args: - seed (int): seed value - """ - random.seed(seed) - np.random.seed(seed) - paddle.seed(seed) - - -def default_logdir(): - """generate default log dir - - Returns: - path.Path: path of log dir - """ - from datetime import datetime - - current_time = datetime.now().strftime("%b%d_%H-%M-%S") - return current_time - - -def str2bool(v): - """convert string args to boolean - - Args: - v (str): from args - - Returns: - boolean: True or False - """ - if isinstance(v, bool): - return v - if v.lower() in ("yes", "true", "t", "y", "1"): - return True - elif v.lower() in ("no", "false", "f", "n", "0"): - return False - else: - raise argparse.ArgumentTypeError("Boolean value expected.") - - -def str2list(v): - """convert string to list - - Args: - v (str): from args - - Returns: - list: sperated by ',' - """ - if isinstance(v, list): - return v - elif isinstance(v, str): - vs = v.split(",") - return [v.strip() for v in vs] - else: - raise argparse.ArgumentTypeError("Str value seperated by ', ' expected.") - - -def str2intlist(v): - """convert string to int list - - Args: - v (string): from args - - Returns: - list: int list - """ - if isinstance(v, list): - return v - elif isinstance(v, str): - vs = v.split(",") - if vs[-1] == "": - vs = vs[:-1] - return [int(v.strip()) for v in vs] - else: - raise argparse.ArgumentTypeError("Str value seperated by ', ' expected.") - - -def list2str(list_value): - """convert list to string - - Args: - v (list): int list - - Returns: - str: s - """ - if isinstance(list_value, list): - res = "" - for x in list_value: - res = res + x + "," - return res[:-1] - else: - raise NotImplementedError - - -def print_config(args=None, key=""): - """print the configuration of the experiment - - Args: - args (argparse.Namespace): from args - - Returns: - None - """ - logger.debug("=" * 60) - - logger.debug("{:^40}".format("{} Configuration Arguments".format(key))) - logger.debug("{:30}:{}".format("paddle commit id", paddle.version.commit)) - - for k, v in vars(args).items(): - logger.debug("{:30}:{}".format(k, v)) diff --git a/fitness/baselines/RNAERNIE/src/base_classes.py b/fitness/baselines/RNAERNIE/src/base_classes.py deleted file mode 100644 index c20d68c..0000000 --- a/fitness/baselines/RNAERNIE/src/base_classes.py +++ /dev/null @@ -1,628 +0,0 @@ -""" -This module builds base trainer for all pre & downstream tasks. - -Author: wangning(wangning.roci@gmail.com) -Date : 2022/12/8 2:43 PM -""" - -import copy -import os -import shutil -import numpy as np -import abc -import os.path as osp - -from sklearn.metrics import ( - accuracy_score, - precision_score, - recall_score, - f1_score, - matthews_corrcoef, - roc_auc_score, -) - -import paddle -import paddle.nn as nn -import paddle.nn.functional as F -from paddle.fluid.reader import DataLoader, BatchSampler -from paddlenlp.data import Stack -from paddlenlp.transformers import ErnieForMaskedLM - - -# ===================== Common Modules ===================== -class MlpProjector(nn.Layer): - """MLP projection head.""" - - def __init__(self, n_in, output_size=128): - """ - Args: - n_in: - output_size: - """ - super(MlpProjector, self).__init__() - self.dense = nn.Linear(n_in, output_size) - self.activation = nn.ReLU() - self.projection = nn.Linear(output_size, output_size) - self.n_out = output_size - - def forward(self, embeddings): - """ - Args: - embeddings: - - Returns: - - """ - x = self.dense(embeddings) - x = self.activation(x) - x = self.projection(x) - - return x - - -class IndicatorClassifier(nn.Layer): - """This class indicate coarse class after token [IND].""" - - def __init__(self, model_name_or_path): - """ - Args: - model_name_or_path: - """ - super(IndicatorClassifier, self).__init__() - self.indicator = ErnieForMaskedLM.from_pretrained(model_name_or_path) - - def forward(self, input_ids, masked_positions): - """ - Args: - input_ids: - masked_positions: - Returns: - - """ - with paddle.no_grad(): - outputs = self.indicator(input_ids, masked_positions=masked_positions) - return outputs - - -class MajorityVoter(nn.Layer): - """Stack top k logits.""" - - def __init__(self): - """ """ - super(MajorityVoter, self).__init__() - - def forward(self, k_logits, topk_probs): - """ - Forward function. - - Args: - k_logits: - topk_probs: - - Returns: - - """ - ensemble_logits = paddle.einsum("ijk,ij->ik", k_logits, topk_probs) - return ensemble_logits - - -# ===================== Base Classes ===================== -class BaseTrainer(object): - """Base trainer""" - - def __init__( - self, - args, - tokenizer, - model, - pretrained_model=None, - indicator=None, - ensemble=None, - train_dataset=None, - eval_dataset=None, - data_collator=None, - loss_fn=None, - optimizer=None, - compute_metrics=None, - visual_writer=None, - ): - """init function - - Args: - args: training args - tokenizer: convert sequence to ids - model: downstream task model - pretrained_model: pretrained model - indicator: indicator classifier - ensemble: ensemble model - train_dataset: dataset for training - eval_dataset: dataset for evaluation - data_collator: data collator - loss_fn: loss function - optimizer: optimizer for training - compute_metrics: metrics function - visual_writer: visualdl writer - - Returns: - None - """ - self.args = args - self.tokenizer = tokenizer - self.model = model - self.pretrained_model = pretrained_model - self.indicator = indicator - self.ensemble = ensemble - self.train_dataset = train_dataset - self.eval_dataset = eval_dataset - self.data_collator = data_collator - self.loss_fn = loss_fn - self.optimizer = optimizer - self.compute_metrics = compute_metrics - # default name_pbar is the first metric - self.name_pbar = self.compute_metrics.metrics[0] - self.visual_writer = visual_writer - self.max_metric = 0.0 - self.max_model_dir = "" - # init dataloaders - self._prepare_dataloaders() - - def _get_dataloader(self, dataset, sampler): - """get dataloader, private function - Args: - dataset: dataset to be loaded - sampler: data sampler - - Returns: - paddle.io.Dataloader: data loader - """ - return DataLoader( - dataset, - batch_sampler=sampler, - collate_fn=self.data_collator, - num_workers=self.args.dataloader_num_workers, - ) - - def _get_sampler(self, dataset): - """get data sampler, private function - Args: - dataset: dataset to be loaded - - Returns: - paddle.io.BatchSampler: data sampler - """ - return BatchSampler( - dataset=dataset, - shuffle=True, - batch_size=self.args.batch_size, - drop_last=self.args.dataloader_drop_last, - ) - - def _prepare_dataloaders(self): - """prepare dataloaders, private function - Returns: - None - """ - if self.train_dataset: - train_sampler = self._get_sampler(self.train_dataset) - self.train_dataloader = self._get_dataloader( - self.train_dataset, train_sampler - ) - - if self.eval_dataset: - eval_sampler = self._get_sampler(self.eval_dataset) - self.eval_dataloader = self._get_dataloader(self.eval_dataset, eval_sampler) - - def save_model(self, metrics_dataset, epoch): - """ - Save model after epoch training in save_dir. - Args: - metrics_dataset: metrics of dataset - epoch: training epoch number - - Returns: - None - """ - if metrics_dataset[self.name_pbar] > self.max_metric: - self.max_metric = metrics_dataset[self.name_pbar] - if os.path.exists(self.max_model_dir): - print("Remove old max model dir:", self.max_model_dir) - shutil.rmtree(self.max_model_dir) - - self.max_model_dir = osp.join(self.args.output, "epoch_" + str(epoch)) - os.makedirs(self.max_model_dir) - save_model_path = osp.join(self.max_model_dir, "model_state.pdparams") - paddle.save(self.model.state_dict(), save_model_path) - print("Model saved at:", save_model_path) - - def get_input_ids_ind_topk(self, input_ids, seq_lens, indicator): - """Get top k input ids with indications and return them. - - Args: - input_ids: (B, L) - seq_lens: (B) or (B, Ch) - indicator: - - Returns: - (token ids, probabilities) - """ - input_ids = paddle.unsqueeze(input_ids, axis=0) - (B, max_seq_len) = input_ids.shape - - token_to_idx = self.tokenizer.vocab.token_to_idx - ind_id = token_to_idx[self.tokenizexr.ind_token] - sep_id = token_to_idx[self.tokenizer.sep_token] - - is_cross = seq_lens <= max_seq_len - 2 # remove [SEP] & [LABEL] token - ind_positions = paddle.where(is_cross, x=seq_lens - 1, y=max_seq_len - 3) - label_positions = ind_positions + 1 - sep_positions = ind_positions + 2 - for b in range(B): - input_ids[b, ind_positions[b]] = ind_id - input_ids[b, sep_positions[b]] = sep_id - - masked_positions = copy.deepcopy(label_positions) - for i in range(1, B): - masked_positions[i] += i * max_seq_len - with paddle.no_grad(): - ind_logtis = indicator( - input_ids=input_ids, masked_positions=masked_positions - ) - ind_logits = ind_logtis.detach() # [B, vocab_size] - - # remove special tokens and bases (A, T, C, G) - ind_logits[:, :7] = float("-inf") - ind_logits[:, 35:] = float("-inf") - ind_probs = F.softmax(ind_logits, axis=-1) - # (B, k), (B) - topk_probs, topk_ind_ids = paddle.topk( - ind_probs, self.args.top_k, axis=-1, largest=True - ) - # (B*Ch, k, max_seq_len) - input_ids_inds = paddle.tile( - input_ids.unsqueeze(axis=1), repeat_times=(1, self.args.top_k, 1) - ) - - for b in range(B): - input_ids_inds[b, :, label_positions[b]] = paddle.t(topk_ind_ids[b]) - # tested, add it has more accuracy - topk_probs = topk_probs / paddle.sum(topk_probs, axis=1, keepdim=True) - - return input_ids_inds, topk_probs, topk_ind_ids - - def train(self, epoch): - """ - Args: - epoch: training epoch - - Returns: - None - """ - raise NotImplementedError("Must implement train method.") - - def eval(self, epoch): - """ - Args: - epoch: eval epoch - - Returns: - None - """ - raise NotImplementedError("Must implement eval method.") - - -class BaseCollator(object): - """Data collator that will dynamically pad the inputs to the longest sequence in the batch and process them.""" - - def __init__(self): - """ """ - self.stack_fn = Stack() - - def __call__(self, data): - """ - Args: - data: instance for stack - - Returns: - dict for trainer - """ - raise NotImplementedError("Must implement __call__ method.") - - -class BaseInstance(object): - """A single instance for data collator.""" - - def __init__(self): - """ """ - pass - - def __call__(self): - """ - Returns: - class properties - """ - return vars(self).items() - - -class BaseMetrics(abc.ABC): - """Base class for functional tasks metrics""" - - def __init__(self, metrics): - """ - Args: - metrics: names in list - """ - self.metrics = [x.lower() for x in metrics] - - @abc.abstractmethod - def __call__(self, outputs, labels): - """ - Args: - kwargs: required args of model (dict) - - Returns: - metrics in dict - """ - preds = paddle.argmax(outputs, axis=-1) - preds = paddle.cast(preds, "int32") - preds = preds.numpy() - - labels = paddle.cast(labels, "int32") - labels = labels.numpy() - - res = {} - for name in self.metrics: - func = getattr(self, name) - if func: - if func == self.auc: - # given two neural outputs, calculate their logits - # and then calculate auc - logits = F.sigmoid(outputs).cpu().numpy() - m = func(logits, labels) - else: - m = func(preds, labels) - res[name] = m - else: - raise NotImplementedError - return res - - @staticmethod - def accuracy(preds, labels): - """ - All args have same shapes. - Args: - preds: predictions of model, (batch_size, 1) - labels: ground truth, (batch_size, 1) - - Returns: - accuracy - """ - return accuracy_score(labels, preds) - - @staticmethod - def precision(preds, labels): - """ - All args have same shapes. - Args: - preds: predictions of model, (batch_size, 1) - labels: ground truth, (batch_size, 1) - - Returns: - precision - """ - return precision_score(labels, preds, average="macro") - - @staticmethod - def recall(preds, labels): - """ - All args have same shapes. - Args: - preds: predictions of model, (batch_size, 1) - labels: ground truth, (batch_size, 1) - - Returns: - precision - """ - return recall_score(labels, preds, average="macro") - - @staticmethod - def f1s(preds, labels): - """ - All args have same shapes. - Args: - preds: predictions of model, (batch_size, 1) - labels: ground truth, (batch_size, 1) - - Returns: - precision - """ - return f1_score(labels, preds, average="macro") - - @staticmethod - def mcc(preds, labels): - """ - All args have same shapes. - Args: - preds: predictions of model, (batch_size, 1) - labels: ground truth, (batch_size, 1) - - Returns: - precision - """ - return matthews_corrcoef(labels, preds) - - @staticmethod - def auc(preds, labels): - """ - All args have same shapes. - Args: - preds: predictions of model, (batch_size, 1) - labels: ground truth, (batch_size, 1) - - Returns: - precision - """ - labels += 1 - preds = preds[:, 1] - return roc_auc_score(labels, preds) - - -class BaseStructMetrics(abc.ABC): - """Base class for evaluation metrics""" - - def __init__(self, metrics): - """ - Args: - metrics: names in list - """ - self.metrics = [x.lower() for x in metrics] - - @abc.abstractmethod - def __call__(self, **kwargs): - """ - Args: - kwargs: required args of model (dict) - - Returns: - metrics in dict - """ - raise NotImplementedError("Must implement __call__ method.") - - @staticmethod - def accuracy(logits, labels, mask=None): - """ - All args have same shapes. - Args: - logits: predictions of model, (batch_size, *) - labels: ground truth, (batch_size, *) - mask: valid sequence length masking, (batch_size, *) - - Returns: - accuracy - """ - if mask is not None: - tp = np.sum( - np.logical_and( - np.logical_and(np.equal(labels, 1), np.equal(logits, 1)), mask - ) - ) - tn = np.sum( - np.logical_and( - np.logical_and(np.equal(labels, 0), np.equal(logits, 0)), mask - ) - ) - return (tp + tn) / np.sum(np.equal(mask, 1)) - else: - tp = np.sum(np.logical_and(np.equal(labels, 1), np.equal(logits, 1))) - tn = np.sum( - np.logical_and(np.equal(labels, 0), np.equal(logits, 0)), - ) - return (tp + tn) / labels.shape[0] - - @staticmethod - def precision(logits, labels, mask=None): - """ - All args have same shapes. - Args: - logits: predictions of model, (batch_size, *) - labels: ground truth, (batch_size, *) - mask: valid sequence length masking, (batch_size, *) - - Returns: - precision - """ - if mask is not None: - tp = np.sum( - np.logical_and( - np.logical_and(np.equal(labels, 1), np.equal(logits, 1)), mask - ) - ) - fp = np.sum( - np.logical_and( - np.logical_and(np.equal(labels, 0), np.equal(logits, 1)), mask - ) - ) - else: - tp = np.sum(np.logical_and(np.equal(labels, 1), np.equal(logits, 1))) - fp = np.sum( - np.logical_and(np.equal(labels, 0), np.equal(logits, 1)), - ) - return tp / (tp + fp) - - @staticmethod - def recall(logits, labels, mask=None): - """ - All args have same shapes. - Args: - logits: predictions of model, (batch_size, *) - labels: ground truth, (batch_size, *) - mask: valid sequence length masking, (batch_size, *) - - Returns: - recall - """ - if mask is not None: - tp = np.sum( - np.logical_and( - np.logical_and(np.equal(labels, 1), np.equal(logits, 1)), mask - ) - ) - fn = np.sum( - np.logical_and( - np.logical_and(np.equal(labels, 1), np.equal(logits, 0)), mask - ) - ) - else: - tp = np.sum(np.logical_and(np.equal(labels, 1), np.equal(logits, 1))) - fn = np.sum( - np.logical_and(np.equal(labels, 1), np.equal(logits, 0)), - ) - return tp / (tp + fn) - - def f1s(self, logits, labels, mask=None): - """ - All args have same shapes. - Args: - logits: predictions of model, (batch_size, *) - labels: ground truth, (batch_size, *) - mask: valid sequence length masking, (batch_size, *) - - Returns: - f1s - """ - p = self.precision(logits, labels, mask) - r = self.recall(logits, labels, mask) - metric = 2 * p * r / (p + r) - return metric - - -class FuncMetrics(object): - """Metrics for classification""" - - def __init__(self, metrics): - """ - Args: - metrics: names in string - """ - self.metrics = [x.lower() for x in metrics] - - def __call__(self, outputs, labels): - """ - Args: - outputs: output of model - labels: ground truth of data - - Returns: - metrics in dict - """ - preds = paddle.concat(outputs, axis=0) - labels = paddle.concat(labels, axis=0) - - res = {} - for name in self.metrics: - func = getattr(self, name) - if func: - m = func(preds, labels) - res[name] = m - else: - raise NotImplementedError("Metric {} is not implemented.".format(name)) - return res diff --git a/fitness/baselines/RNAERNIE/src/config.json b/fitness/baselines/RNAERNIE/src/config.json deleted file mode 100644 index 08b75c7..0000000 --- a/fitness/baselines/RNAERNIE/src/config.json +++ /dev/null @@ -1,25 +0,0 @@ -{ - "_name_or_path": "outputs/init", - "architectures": [ - "BertForMaskedLM" - ], - "attention_probs_dropout_prob": 0.1, - "classifier_dropout": null, - "hidden_act": "gelu", - "hidden_dropout_prob": 0.1, - "hidden_size": 768, - "initializer_range": 0.02, - "intermediate_size": 3072, - "layer_norm_eps": 1e-05, - "max_position_embeddings": 2048, - "model_type": "bert", - "num_attention_heads": 12, - "num_hidden_layers": 12, - "pad_token_id": 0, - "position_embedding_type": "absolute", - "torch_dtype": "float32", - "transformers_version": "4.41.2", - "type_vocab_size": 2, - "use_cache": true, - "vocab_size": 11 -} diff --git a/fitness/baselines/RNAERNIE/src/dataset_utils.py b/fitness/baselines/RNAERNIE/src/dataset_utils.py deleted file mode 100644 index 99a6234..0000000 --- a/fitness/baselines/RNAERNIE/src/dataset_utils.py +++ /dev/null @@ -1,26 +0,0 @@ -def seq2kmer(seq, k_mer, max_length=None): - kmer_text = "" - i = 0 - upper = len(seq) - k_mer + 1 - if max_length: - upper = min(upper, max_length) - while i < upper: - kmer_text += seq[i : i + k_mer] + " " - i += 1 - kmer_text = kmer_text.strip() - return kmer_text - - -def seq2input_ids(seq, tokenizer): - k_mer = tokenizer.k_mer - - seq += seq[-1] * (k_mer - 1) - kmer_text = "" - i = 0 - while i < len(seq) - k_mer + 1: - kmer_text += seq[i : i + k_mer] + " " - i += 1 - kmer_text = kmer_text.strip() - tokens = tokenizer(text=kmer_text, return_token_type_ids=False) - input_ids = tokens["input_ids"] - return input_ids diff --git a/fitness/baselines/RNAERNIE/src/model_config.json b/fitness/baselines/RNAERNIE/src/model_config.json deleted file mode 100644 index 3c7498c..0000000 --- a/fitness/baselines/RNAERNIE/src/model_config.json +++ /dev/null @@ -1,19 +0,0 @@ -{ - "init_args": [ - { - "attention_probs_dropout_prob": 0.1, - "hidden_act": "relu", - "hidden_dropout_prob": 0.1, - "hidden_size": 768, - "initializer_range": 0.02, - "max_position_embeddings": 513, - "num_attention_heads": 12, - "num_hidden_layers": 12, - "type_vocab_size": 2, - "vocab_size": 39, - "pad_token_id": 0, - "init_class": "ErnieModel" - } - ], - "init_class": "ErnieForMaskedLM" -} \ No newline at end of file diff --git a/fitness/baselines/RNAERNIE/src/rna_ernie.py b/fitness/baselines/RNAERNIE/src/rna_ernie.py deleted file mode 100644 index 4f8f9f1..0000000 --- a/fitness/baselines/RNAERNIE/src/rna_ernie.py +++ /dev/null @@ -1,117 +0,0 @@ -""" -This module inference sequences embeddings. -""" - -from Bio import SeqIO - -import paddle -from paddlenlp.utils.log import logger -from paddlenlp.data import Stack -from paddlenlp.transformers import ErnieModel - -from src.dataset_utils import seq2input_ids -from src.tokenizer_nuc import NUCTokenizer - - -class BatchConverter(object): - """Convert sequences to batch inputs.""" - - def __init__( - self, - k_mer=1, - vocab_path="./vocab_1MER.txt", - batch_size=256, - max_seq_len=512, - is_pad=True, - st_pos=0, - ): - """this class predicts embeddings from RNA sequences list - - Args: - k_mer (int): k for splitting RNA sequences - vocab_path (str): root path to vocab file - batch_size (int): batch size - max_seq_len (int): max sequence to input sequences - st_pos (int): start position of the sequence - """ - self.tokenizer = NUCTokenizer( - k_mer=k_mer, - vocab_file=vocab_path, - ) - self.stack_fn = Stack() - - self.batch_size = batch_size - self.max_seq_len = max_seq_len - self.is_pad = is_pad - self.st_pos = st_pos - - def __call__(self, data): - """call function to convert sequences to batch inputs. - - Args: - data (List[tuple(name, sequence)], str): data or .fasta file to be converted - """ - - if type(data) == list: - # convert sequences to batch inputs - self.data = data - elif type(data) == str and ( - data.split(".")[-1] == "fasta" or data.split(".")[-1] == "fa" - ): - # load .fasta file with SeqIO - records = list(SeqIO.parse(data, "fasta")) - self.data = [(str(x.description), str(x.seq)) for x in records] - # return generator to iterate data by step batch_size - for d in range(0, len(self.data), self.batch_size): - raw_data = self.data[d : d + self.batch_size] - names = [x[0] for x in raw_data] - seqs = [x[1] for x in raw_data] - seqs = [x[self.st_pos : self.st_pos + self.max_seq_len - 2] for x in seqs] - seqs = [x.upper().replace("U", "T") for x in seqs] - # 2 means [CLS] and [SEP] - input_ids = [seq2input_ids(x, self.tokenizer) for x in seqs] - if self.is_pad: - input_ids = [x + [0] * (self.max_seq_len - len(x)) for x in input_ids] - input_ids = self.stack_fn(input_ids) - input_ids = paddle.to_tensor(input_ids) - yield names, seqs, input_ids - - -if __name__ == "__main__": - # ========== Set device - logger.debug("Set device.") - paddle.set_device("gpu") - - # ========== Prepare Data - data = [ - ("RNA1", "GGGUGCGAUCAUACCAGCACUAAUGCCCUCCUGGGAAGUCCUCGUGUUGCACCCCU"), - ( - "RNA2", - "GGGUGUCGCUCAGUUGGUAGAGUGCUUGCCUGGCAUGCAAGAAACCUUGGUUCAAUCCCCAGCACUGCA", - ), - ("RNA3", "CGAUUCNCGUUCCC--CCGCCUCCA"), - ] - data = "./data/ft/seq_cls/nRC/test.fa" - - # ========== Batch Converter - logger.debug("Loading converter.") - batch_converter = BatchConverter( - k_mer=1, - vocab_path="./data/vocab/vocab_1MER.txt", - batch_size=256, - max_seq_len=512, - ) - - # ========== RNAErnie Model - rna_ernie = ErnieModel.from_pretrained( - "./output/BERT,ERNIE,MOTIF,PROMPT/checkpoint_final" - ) - - # call batch_converter to convert sequences to batch inputs - for names, _, inputs_ids in batch_converter(data): - with paddle.no_grad(): - # extract whole sequence embeddings - embeddings = rna_ernie(inputs_ids)[0].detach() - # extract [CLS] token embedding - embeddings = embeddings[:, 0, :] - print(embeddings.shape) diff --git a/fitness/baselines/RNAERNIE/src/tokenizer_config.json b/fitness/baselines/RNAERNIE/src/tokenizer_config.json deleted file mode 100644 index b1ae07e..0000000 --- a/fitness/baselines/RNAERNIE/src/tokenizer_config.json +++ /dev/null @@ -1,64 +0,0 @@ -{ - "added_tokens_decoder": { - "0": { - "content": "[PAD]", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false, - "special": true - }, - "1": { - "content": "[UNK]", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false, - "special": true - }, - "2": { - "content": "[CLS]", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false, - "special": true - }, - "3": { - "content": "[EOS]", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false, - "special": true - }, - "4": { - "content": "[SEP]", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false, - "special": true - }, - "5": { - "content": "[MASK]", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false, - "special": true - } - }, - "bos_token": "[CLS]", - "clean_up_tokenization_spaces": true, - "cls_token": "[CLS]", - "eos_token": "[EOS]", - "mask_token": "[MASK]", - "model_max_length": 2048, - "pad_token": "[PAD]", - "padding_side": "right", - "sep_token": "[SEP]", - "tokenizer_class": "RNAErnieTokenizer", - "unk_token": "[UNK]", - "use_fast": true -} diff --git a/fitness/baselines/RNAERNIE/src/tokenizer_nuc.py b/fitness/baselines/RNAERNIE/src/tokenizer_nuc.py deleted file mode 100644 index 4446ab5..0000000 --- a/fitness/baselines/RNAERNIE/src/tokenizer_nuc.py +++ /dev/null @@ -1,363 +0,0 @@ -""" -This module builds RNA nucleotide tokenizer. -""" - -# built-in modules -import os -import io - -# paddle modules -from paddlenlp.transformers import BasicTokenizer, PretrainedTokenizer -from paddlenlp.data.vocab import Vocab - - -class NUCTokenizer(PretrainedTokenizer): - """ - Constructs an ERNIE tokenizer. It uses a basic tokenizer to do punctuation - splitting, lower casing and so on, and follows a WordPiece tokenizer to - tokenize as subwords. - - This tokenizer inherits from :class:`~paddlenlp.transformers.tokenizer_utils.PretrainedTokenizer` - which contains most of the main methods. For more information regarding those methods, - please refer to this superclass. - - Args: - vocab_file (str): - The vocabulary file path (ends with '.txt') required to instantiate - a `WordpieceTokenizer`. - do_lower_case (str, optional): - Whether or not to lowercase the input when tokenizing. - Defaults to`True`. - unk_token (str, optional): - A special token representing the *unknown (out-of-vocabulary)* token. - An unknown token is set to be `unk_token` inorder to be converted to an ID. - Defaults to "[UNK]". - sep_token (str, optional): - A special token separating two different sentences in the same input. - Defaults to "[SEP]". - pad_token (str, optional): - A special token used to make arrays of tokens the same size for batching purposes. - Defaults to "[PAD]". - cls_token (str, optional): - A special token used for sequence classification. It is the last token - of the sequence when built with special tokens. Defaults to "[CLS]". - mask_token (str, optional): - A special token representing a masked token. This is the token used - in the masked language modeling task which the model tries to predict the original unmasked ones. - Defaults to "[MASK]". - del_token (str, optional): - A special token representing a deleted token. Defaults to "[DEL]". - ind_token (str, optional): - A special token representing a indication token. Defaults to "[IND]". - - Examples: - .. code-block:: - - from paddlenlp.transformers import ErnieTokenizer - tokenizer = ErnieTokenizer.from_pretrained('ernie-1.0') - - encoded_inputs = tokenizer('He was a puppeteer') - # encoded_inputs: - # { 'input_ids': [1, 4444, 4385, 1545, 6712, 10062, 9568, 9756, 9500, 2], - # 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]} - # } - - """ - - # resource_files_names = {"vocab_file": "vocab.txt"} # for save_pretrained - # pretrained_resource_files_map = None - # pretrained_init_configuration = None - - def __init__( - self, - k_mer, - vocab_file, - do_lower_case=False, - unk_token="[UNK]", - pad_token="[PAD]", - cls_token="[CLS]", - sep_token="[SEP]", - mask_token="[MASK]", - del_token="[DEL]", - ind_token="[IND]", - label_tokens=None, - normal_tokens_start=35, - ): - """init tokenizer - - Args: - k_mer (int): k_mer for seperating RNA sequence - vocab_file (str): vocabulary file path - do_lower_case (bool, optional): whether convert to lower cases. Defaults to False. - unk_token (str, optional): unknown tokens. - pad_token (str, optional): padding tokens. - cls_token (str, optional): begin og sentence tokens. Defaults to "[CLS]". - sep_token (str, optional): end of sentence tokens. - mask_token (str, optional): mask tokens. Defaults to "[MASK]". - del_token (str, optional): deletion tokens. Defaults to "[DEL]". - label_tokens (str, optional): label tokens. - ind_token (str, optional): indication tokens. Defaults to "[IND]". - normal_tokens_start (int, optional): sentences tokens starting index. Defaults to 35. - - Raises: - ValueError: _description_ - """ - - if not os.path.isfile(vocab_file): - raise ValueError( - "Can't find a vocabulary file at path '{}'. " - "To load the vocabulary from a pretrained model please use " - "`tokenizer = ErnieTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`".format( - vocab_file - ) - ) - self.k_mer = k_mer - self.vocab_file = vocab_file - # self.do_lower_case = do_lower_case - - extra_label_tokens = [] - if label_tokens: - for k, v in label_tokens.items(): - if v == "-": - extra_label_tokens.append(k.replace("_", "")) - - self.vocab = self.load_vocabulary( - vocab_file, - unk_token=unk_token, - pad_token=pad_token, - eos_token=sep_token, - label_tokens=extra_label_tokens, - ) - - self.vocab_id_list = list(self.vocab.idx_to_token.keys()) - self.normal_vocab_id_list = self.vocab_id_list[normal_tokens_start:] - self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case) - # self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, - # unk_token=unk_token) - self.unk_token = unk_token - self.del_token = del_token - self.ind_token = ind_token - self.sep_token = sep_token - - @property - def vocab_size(self): - """ - Return the size of vocabulary. - - Returns: - int: The size of vocabulary. - """ - return len(self.vocab) - - def load_vocabulary( - self, - filepath, - unk_token=None, - pad_token=None, - bos_token=None, - eos_token=None, - label_tokens=None, - **kwargs, - ): - """load vocabulary from file - - Args: - filepath (str, optional): vocabulary file path - unk_token (str, optional): unknown tokens. - pad_token (str, optional): padding tokens. - bos_token (str, optional): begin of sentence tokens. - eos_token (str, optional): end of sentence tokens. - label_tokens (str, optional): label tokens. - - Returns: - Vocab: vocabulary instance - """ - token_to_idx = {} - with io.open(filepath, "r", encoding="utf-8") as f: - for index, line in enumerate(f): - token = line.rstrip("\n") - token_to_idx[token] = int(index) - - for extra_label in label_tokens: - index += 1 - token_to_idx[extra_label] = index - - vocab = Vocab.from_dict( - token_to_idx, - unk_token=unk_token, - pad_token=pad_token, - bos_token=bos_token, - eos_token=eos_token, - **kwargs, - ) - return vocab - - def _tokenize(self, text): - """ - End-to-end tokenization for ERNIE models. - - Args: - text (str): The text to be tokenized. - - Returns: - List[str]: A list of string representing converted tokens. - """ - split_tokens = [] - for token in self.basic_tokenizer.tokenize(text): - split_tokens.append(token) - - return split_tokens - - # def convert_tokens_to_string(self, tokens): - # r""" - # Converts a sequence of tokens (list of string) in a single string. Since - # the usage of WordPiece introducing `##` to concat subwords, also remove - # `##` when converting. - - # Args: - # tokens (List[str]): A list of string representing tokens to be converted. - - # Returns: - # str: Converted string from tokens. - - # Examples: - # .. code-block:: - - # from paddlenlp.transformers import ErnieTokenizer - # tokenizer = ErnieTokenizer.from_pretrained('ernie-1.0') - - # tokens = tokenizer.tokenize('He was a puppeteer') - # strings = tokenizer.convert_tokens_to_string(tokens) - # #he was a puppeteer - - # """ - # out_string = " ".join(tokens).replace(" ##", "").strip() - # return out_string - - # def num_special_tokens_to_add(self, pair=False): - # r""" - # Returns the number of added tokens when encoding a sequence with special tokens. - - # Note: - # This encodes inputs and checks the number of added tokens, and is therefore not efficient. - # Do not put this inside your training loop. - - # Args: - # pair (bool, optional): - # Whether the input is a sequence pair or a single sequence. - # Defaults to `False` and the input is a single sequence. - - # Returns: - # int: Number of tokens added to sequences - # """ - # token_ids_0 = [] - # token_ids_1 = [] - # return len(self.build_inputs_with_special_tokens(token_ids_0, token_ids_1 if pair else None)) - - def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): - r""" - Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and - adding special tokens. - - An Ernie sequence has the following format: - - - single sequence: ``[CLS] X [SEP]`` - - pair of sequences: ``[CLS] A [SEP] B [SEP]`` - - Args: - token_ids_0 (List[int]): - List of IDs to which the special tokens will be added. - token_ids_1 (List[int], optional): - Optional second list of IDs for sequence pairs. - Defaults to `None`. - - Returns: - List[int]: List of input_id with the appropriate special tokens. - """ - if token_ids_1 is None: - return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] - _cls = [self.cls_token_id] - _sep = [self.sep_token_id] - return _cls + token_ids_0 + _sep + token_ids_1 + _sep - - # def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False): - # r""" - # Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding - # special tokens using the tokenizer ``encode`` methods. - - # Args: - # token_ids_0 (List[int]): - # List of ids of the first sequence. - # token_ids_1 (List[int], optinal): - # Optional second list of IDs for sequence pairs. - # Defaults to `None`. - # already_has_special_tokens (str, optional): - # Whether or not the token list is already formatted with special tokens for the model. - # Defaults to `False`. - - # Returns: - # List[int]: - # The list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. - # """ - - # if already_has_special_tokens: - # if token_ids_1 is not None: - # raise ValueError("You should not supply a second sequence if the provided sequence of " - # "ids is already formatted with special tokens for the model.") - # return list(map(lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0)) - - # if token_ids_1 is not None: - # return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1] - # return [1] + ([0] * len(token_ids_0)) + [1] - - # def build_offset_mapping_with_special_tokens(self, offset_mapping_0, offset_mapping_1=None): - # r""" - # Build offset map from a pair of offset map by concatenating and adding offsets of special tokens. - - # An ERNIE offset_mapping has the following format: - - # - single sequence: ``(0,0) X (0,0)`` - # - pair of sequences: ``(0,0) A (0,0) B (0,0)`` - - # Args: - # offset_mapping_ids_0 (List[tuple]): - # List of char offsets to which the special tokens will be added. - # offset_mapping_ids_1 (List[tuple], optional): - # Optional second list of wordpiece offsets for offset mapping pairs. - # Defaults to `None`. - - # Returns: - # List[tuple]: A list of wordpiece offsets with the appropriate offsets of special tokens. - # """ - # if offset_mapping_1 is None: - # return [(0, 0)] + offset_mapping_0 + [(0, 0)] - - # return [(0, 0)] + offset_mapping_0 + [(0, 0)] + offset_mapping_1 + [(0, 0)] - - # def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None): - # r""" - # Create a mask from the two sequences passed to be used in a sequence-pair classification task. - - # A ERNIE sequence pair mask has the following format: - # :: - - # 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 - # | first sequence | second sequence | - - # If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s). - - # Args: - # token_ids_0 (List[int]): - # A list of `inputs_ids` for the first sequence. - # token_ids_1 (List[int], optional): - # Optional second list of IDs for sequence pairs. - # Defaults to `None`. - - # Returns: - # List[int]: List of token_type_id according to the given sequence(s). - # """ - # _sep = [self.sep_token_id] - # _cls = [self.cls_token_id] - # if token_ids_1 is None: - # return len(_cls + token_ids_0 + _sep) * [0] - # return len(_cls + token_ids_0 + _sep) * [0] + len(token_ids_1 + _sep) * [1] diff --git a/fitness/baselines/RNAERNIE/src/vocab.txt b/fitness/baselines/RNAERNIE/src/vocab.txt deleted file mode 100644 index 8f7d552..0000000 --- a/fitness/baselines/RNAERNIE/src/vocab.txt +++ /dev/null @@ -1,11 +0,0 @@ -[PAD] -[UNK] -[CLS] -[EOS] -[SEP] -[MASK] -A -U -C -G -N diff --git a/fitness/baselines/RNAGenesis/score_rnagenesis.sh b/fitness/baselines/RNAGenesis/score_rnagenesis.sh deleted file mode 100644 index f938480..0000000 --- a/fitness/baselines/RNAGenesis/score_rnagenesis.sh +++ /dev/null @@ -1,21 +0,0 @@ -#!/usr/bin/env bash -set -euo pipefail - -script_dir=$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd) - -# The gated checkpoint requires deepspeed and omits quantization.py. Copy that -# file from biomap-research/xtrimopglm-1b-mlm into the local checkpoint -export model_dir="path/to/local/RNAGenesis" - -export reference_sheet="reference_sheet.csv" -export output_scores_dir="path/to/model_predictions/rnagenesis_4fill" -export dms_data_dir="path/to/dms/data/dir" - -DMS_index=${SLURM_ARRAY_TASK_ID:-0} - -python "$script_dir/score_rnagenesis_single_dms.py" \ - --row_id "$DMS_index" \ - --ref_sheet "$reference_sheet" \ - --dms_dir_path "$dms_data_dir" \ - --output_dir_path "$output_scores_dir" \ - --model_name "$model_dir" diff --git a/fitness/baselines/RNA_FM/score_rna_fm.sh b/fitness/baselines/RNA_FM/score_rna_fm.sh deleted file mode 100644 index 0b0d88f..0000000 --- a/fitness/baselines/RNA_FM/score_rna_fm.sh +++ /dev/null @@ -1,20 +0,0 @@ -#!/usr/bin/env bash -set -euo pipefail - -script_dir=$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd) - -# RNA-FM requires the rna-fm package and RNA-FM_pretrained.pth -export checkpoint_path="path/to/RNA-FM_pretrained.pth" - -export reference_sheet="reference_sheet.csv" -export output_scores_dir="path/to/model_predictions/rna_fm_4fill" -export dms_data_dir="path/to/dms/data/dir" - -DMS_index=${SLURM_ARRAY_TASK_ID:-0} - -python "$script_dir/score_rna_fm_single_dms.py" \ - --row_id "$DMS_index" \ - --ref_sheet "$reference_sheet" \ - --dms_dir_path "$dms_data_dir" \ - --output_dir_path "$output_scores_dir" \ - --checkpoint_path "$checkpoint_path" diff --git a/fitness/baselines/RiNALMo/score_rinalmo.sh b/fitness/baselines/RiNALMo/score_rinalmo.sh deleted file mode 100644 index 73f2933..0000000 --- a/fitness/baselines/RiNALMo/score_rinalmo.sh +++ /dev/null @@ -1,21 +0,0 @@ -#!/usr/bin/env bash -set -euo pipefail - -script_dir=$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd) - -# RiNALMo giga-v1 requires the official package, checkpoint and a CUDA device -# for its stored flash-attention modules -export checkpoint_path="path/to/rinalmo_giga_pretrained.pt" - -export reference_sheet="reference_sheet.csv" -export output_scores_dir="path/to/model_predictions/rinalmo_4fill" -export dms_data_dir="path/to/dms/data/dir" - -DMS_index=${SLURM_ARRAY_TASK_ID:-0} - -python "$script_dir/score_rinalmo_single_dms.py" \ - --row_id "$DMS_index" \ - --ref_sheet "$reference_sheet" \ - --dms_dir_path "$dms_data_dir" \ - --output_dir_path "$output_scores_dir" \ - --checkpoint_path "$checkpoint_path" diff --git a/fitness/baselines/masked_lm/README.md b/fitness/baselines/masked_lm/README.md deleted file mode 100644 index 4272f45..0000000 --- a/fitness/baselines/masked_lm/README.md +++ /dev/null @@ -1,86 +0,0 @@ -# Masked-marginal scoring - -Shared scoring code for the benchmark's masked RNA language models. A model's -script in `fitness/baselines//` supplies an alphabet, a tokenization and -one forward pass. Everything else lives here. - -## The four fill strategies - -Every masked-marginal score computes a log-odds at each mutated position of a -variant and sums over its mutations. The strategies differ in exactly one thing: -what fills the variant's OTHER mutated positions while the scored position is -masked. With `M` the mutated positions, `x^wt` and `x^mt` the wild-type and -variant sequences, `x_-i` a mask at `i` and `x_-M` masks at every position in `M` -([Meier et al. 2021][0], supplement Appendix A): - -| strategy | fill at the other mutated sites | score | -|:--|:--|:--| -| `wt-fill` | wild-type bases | `sum_i log p(mt_i \| x^wt_-i) - log p(wt_i \| x^wt_-i)` | -| `mask-fill` | masks | `sum_i log p(mt_i \| x^wt_-M) - log p(wt_i \| x^wt_-M)` | -| `mut-fill` | mutant bases | `sum_i log p(mt_i \| x^mt_-i) - log p(wt_i \| x^mt_-i)` | -| `match-fill` | the allele being scored | `sum_i log p(mt_i \| x^mt_-i) - log p(wt_i \| x^wt_-i)` | - -Appendix A defines `mask-fill`, `match-fill` and `mut-fill`. `wt-fill` is the -closely related convention implemented under the name `masked-marginals` by the -[ESM example][1] and [ProteinGym baseline][2]. -The name "masked marginals" is overloaded. `wt-marginals` is a different method -again: one unmasked forward pass with no masking. - -All four are identical on single mutants, because a variant with one mutation has -no other mutated positions. They diverge on multi-mutants, which are 99.4% of the -ncRNA benchmark. `match-fill` alone mixes two contexts, so it is a -difference of two conditionals rather than a log-odds ratio. - -## Cost - -One run computes all four. Their contexts overlap, so on the 31 ncRNA assays -the four together need 2,929,196 unique context examples against 2,458,521 for -`mut-fill` alone, about 19% more. Computing each strategy separately would cost -more because their shared contexts would be rerun. `match-fill` must be -accumulated directly because it cannot be reconstructed from the final -`wt-fill` and `mut-fill` scores. - -## Files - -| file | contents | -|:--|:--| -| `strategies.py` | mutation parsing, wild-type recovery, per-variant validation, the four strategies as contexts and terms | -| `engine.py` | windowing, batching, the gather, and accumulation | -| `adapter.py` | the model interface | -| `runner.py` | command line, assay input, wild-type cross-check, output and manifest | - -## Sequence length - -RNA-FM and RiNALMo cap the number of positions they accept. The ncRNA assays -are 45 to 425 nucleotides and never reach it, but the mRNA constructs are -kilobases. Each long context is windowed around its masked span. A context cannot -be scored when its masked positions span more than the model's sequence limit. - -## Adding a model - -Subclass `MaskedLMAdapter`, declare the alphabet, output column, special-token -count and batching defaults, implement `add_arguments`, `load` and `logits_at`, -and call `runner.main`. `fitness/baselines/RNA_FM/score_rna_fm_single_dms.py` is -the shortest example. Two rules the engine enforces rather than assumes: the -declared special-token count must match the loaded tokenizer, and context -positions must map to token positions by a constant shift. - -## Output - -One CSV per assay, holding the assay dataframe plus `{column}_{strategy}` for -each strategy computed, and a `{DMS_ID}.manifest.json` recording the strategies, -alphabet, model arguments, hardware, counts and a hash of the scoring source. -Requesting a single strategy also writes the model's historical bare column, so -existing commands keep producing the files they used to. - -## Tests - -`tests/test_fitness.py` runs the scorer, manifest writer, prediction merge, -fill-strategy analysis and benchmark aggregation on three complete released -ribozyme, tRNA and aptamer assays. A deterministic stand-in model keeps the test -CPU-only and checkpoint-free. The concrete checkpoint environments remain -model-specific and are tracked as a TODO in `fitness/pixi.toml`. - -[0]: https://papers.nips.cc/paper_files/paper/2021/hash/f51338d736f95dd42427296047067694-Abstract.html -[1]: https://github.com/facebookresearch/esm/blob/2b369911bb5b4b0dda914521b9475cad1656b2ac/examples/variant-prediction/predict.py#L186-L225 -[2]: https://github.com/OATML-Markslab/ProteinGym/blob/144fe22b07dfaeec2b366f2346203a9838a55b4c/proteingym/baselines/esm/compute_fitness.py#L486-L514 diff --git a/fitness/baselines/masked_lm/runner.py b/fitness/baselines/masked_lm/runner.py deleted file mode 100644 index b401576..0000000 --- a/fitness/baselines/masked_lm/runner.py +++ /dev/null @@ -1,403 +0,0 @@ -""" -Command line entry point shared by the masked language model scorers. - -Handles the reference sheet and assay input, the wild-type cross-check, the -strategy selection, and the output file, so that a model's script is only an -adapter. -""" - -import argparse -import hashlib -import json -import shlex -import subprocess -import sys -from pathlib import Path - -import numpy as np -import pandas as pd -from scipy.stats import spearmanr - -from .engine import accumulate_scores, pad_contexts, window_contexts -from .strategies import STRATEGIES, build_tasks, normalize_strategy, recover_wild_type - - -def build_parser(adapter) -> argparse.ArgumentParser: - """Assemble the common arguments plus the adapter's own.""" - parser = argparse.ArgumentParser( - description=f"Run {adapter.name} masked-marginal inference on DMS assay sequences." - ) - parser.add_argument( - "--row_id", - type=int, - required=True, - help="Row ID in the reference sheet to process", - ) - parser.add_argument( - "--ref_sheet", - type=str, - required=True, - help="Path to reference sheet containing DMS_ID and RAW_CONSTRUCT_SEQ columns", - ) - parser.add_argument( - "--dms_dir_path", - type=str, - required=True, - help="Directory containing DMS CSV files", - ) - parser.add_argument( - "--output_dir_path", - type=str, - required=True, - help="Directory to save output files", - ) - parser.add_argument( - "--strategies", - type=str, - nargs="+", - default=list(STRATEGIES), - help="Masked-marginal fill strategies to compute. The fill named is what " - "the model sees at the variant's OTHER mutated positions while one " - "position is masked: wt-fill (wild-type bases, what the ESM and " - "ProteinGym code implement), mask-fill (masks, the ESM paper's " - "formula), mut-fill (mutant bases), match-fill (the allele being " - "scored). All four agree on single mutants. Default: all four, which " - "costs about 19%% more unique context examples than mut-fill alone, " - "because the contexts are shared", - ) - parser.add_argument( - "--device", - type=str, - default=None, - help="Device to run inference on (default: cuda:0 if available, else cpu)", - ) - parser.add_argument( - "--batch_size", - type=int, - default=adapter.default_batch_size, - help=f"Maximum masked contexts scored per forward pass (default: {adapter.default_batch_size})", - ) - parser.add_argument( - "--max_batch_tokens", - type=int, - default=adapter.default_max_batch_tokens, - help="Cap on batch_size x sequence length per forward pass, so that long " - f"assays automatically use a smaller batch (default: {adapter.default_max_batch_tokens})", - ) - if adapter.max_tokens is not None: - parser.add_argument( - "--max_tokens", - type=int, - default=adapter.max_tokens, - help=f"{adapter.name} position limit including special tokens. Contexts " - f"longer than this are windowed around the masked span (default: {adapter.max_tokens})", - ) - parser.add_argument( - "--quiet_skips", - action="store_true", - help="Summarize skipped variants instead of printing one line each", - ) - adapter.add_arguments(parser) - return parser - - -def load_reference_row(ref_sheet_path: str, row_id: int): - """Return one reference-sheet DMS ID and its optional construct.""" - try: - ref_df = pd.read_csv(ref_sheet_path, encoding="utf-8-sig") - except FileNotFoundError: - raise FileNotFoundError(f"Reference sheet not found: {ref_sheet_path}") - if "DMS_ID" not in ref_df.columns: - raise KeyError("Reference sheet must contain 'DMS_ID' column") - if not 0 <= row_id < len(ref_df): - raise ValueError( - f"Row ID {row_id} is outside the reference sheet's " - f"0..{len(ref_df) - 1} range" - ) - - dms_id = ref_df.loc[row_id, "DMS_ID"] - if pd.isna(dms_id): - raise ValueError(f"DMS_ID is missing for row {row_id}") - - construct = None - if "RAW_CONSTRUCT_SEQ" in ref_df.columns: - value = ref_df.loc[row_id, "RAW_CONSTRUCT_SEQ"] - if not pd.isna(value): - construct = str(value) - return str(dms_id), construct - - -def load_dms_data(dms_dir_path: str, dms_id: str) -> pd.DataFrame: - """Load and validate one processed assay.""" - dms_file = Path(dms_dir_path) / f"{dms_id}.csv" - if not dms_file.exists(): - raise FileNotFoundError(f"DMS file not found: {dms_file}") - - df = pd.read_csv(dms_file) - required_cols = ["mutant", "DMS_score", "sequence"] - missing_cols = [col for col in required_cols if col not in df.columns] - if missing_cols: - raise ValueError(f"Missing required columns in DMS file: {missing_cols}") - - return df - - -def resolve_wild_type(adapter, mutants, sequences, construct) -> str: - """Recover the assay wild type and check it against the reference sheet.""" - recovered = recover_wild_type(mutants, sequences, adapter.bases) - if construct is None: - print("Reference sheet has no RAW_CONSTRUCT_SEQ. Using the recovered wild type") - return recovered - folded = adapter.canonicalize_sequence(construct) - if folded != recovered: - raise ValueError( - "The wild type recovered from the assay disagrees with the reference " - f"sheet's RAW_CONSTRUCT_SEQ (lengths {len(recovered)} and {len(folded)}). " - "The wild-type-background strategies need one agreed background" - ) - return recovered - - -def window_budget(adapter, max_tokens: int) -> int: - """Return the nucleotide budget after declared special tokens.""" - budget = max_tokens - adapter.n_special_tokens - if budget < 1: - raise ValueError( - f"max_tokens={max_tokens} leaves no sequence positions after " - f"{adapter.n_special_tokens} special tokens" - ) - return budget - - -def needs_windowing(contexts, budget: int) -> bool: - """Whether any context is too long for the model's position limit.""" - return any(len(c) > budget for c in contexts) - - -def code_revision() -> dict: - """Return the git revision and a hash of the scoring sources.""" - here = Path(__file__).resolve().parent - revision, dirty = "unknown", None - try: - revision_command = f"git -C {shlex.quote(str(here))} rev-parse HEAD" - revision = subprocess.run( - shlex.split(revision_command), - capture_output=True, - text=True, - check=True, - ).stdout.strip() - status_command = f"git -C {shlex.quote(str(here))} status --porcelain" - status = subprocess.run( - shlex.split(status_command), - capture_output=True, - text=True, - check=True, - ).stdout.strip() - dirty = bool(status) - except (subprocess.CalledProcessError, FileNotFoundError): - pass - - digest = hashlib.sha256() - sources = sorted(here.glob("*.py")) - entry = Path(sys.argv[0]).resolve() - if entry.is_file(): - sources.append(entry) - for path in sources: - digest.update(path.name.encode()) - digest.update(path.read_bytes()) - return { - "git_head": revision, - "git_tree_dirty": dirty, - "scoring_source_sha256": digest.hexdigest(), - "scoring_source_files": [p.name for p in sources], - } - - -def runtime_environment(device) -> dict: - """Return the Torch, CUDA, and GPU versions used for scoring.""" - import torch - - device = torch.device(device) - device_name = torch.cuda.get_device_name(device) if device.type == "cuda" else None - return { - "torch": torch.__version__, - "cuda": getattr(torch.version, "cuda", None), - "device": str(device), - "gpu": device_name, - } - - -def write_manifest(path, dms_id, adapter, args, strategies, table, wild_type): - """Write prediction provenance next to an assay output.""" - manifest = { - "dms_id": dms_id, - "environment": runtime_environment(adapter.device), - "model": adapter.name, - "score_column_stem": adapter.score_column, - "strategies": [s.replace("_", "-") for s in strategies], - "columns": { - s.replace("_", "-"): f"{adapter.score_column}_{s}" for s in strategies - }, - "alphabet": adapter.bases, - "wild_type_length": len(wild_type), - "context_length": len(table.contexts[0]) if table.contexts else None, - "context_pad_char": adapter.context_pad_char, - "contexts": len(table.contexts), - "terms": table.n_terms(), - "scorable_variants": int(table.scorable.sum()), - "code_revision": code_revision(), - "arguments": { - k: (str(v) if isinstance(v, Path) else v) for k, v in vars(args).items() - }, - } - Path(path).write_text(json.dumps(manifest, indent=2, sort_keys=True) + "\n") - - -def main(adapter): - """Run one assay with one model under the requested strategies.""" - parser = build_parser(adapter) - args = parser.parse_args() - - if args.batch_size < 1: - parser.error(f"--batch_size must be positive, got {args.batch_size}") - if args.max_batch_tokens < 1: - parser.error( - f"--max_batch_tokens must be positive, got {args.max_batch_tokens}" - ) - - try: - strategies = tuple(normalize_strategy(s) for s in args.strategies) - except ValueError as error: - parser.error(str(error)) - if len(set(strategies)) != len(strategies): - parser.error(f"Duplicate strategies requested: {args.strategies}") - output_dir = Path(args.output_dir_path) - output_dir.mkdir(parents=True, exist_ok=True) - - try: - dms_id, construct = load_reference_row(args.ref_sheet, args.row_id) - print(f"Processing DMS ID: {dms_id}") - dms_df = load_dms_data(args.dms_dir_path, dms_id) - missing_sequences = dms_df["sequence"].isna() - if missing_sequences.any(): - rows = dms_df.index[missing_sequences].tolist()[:5] - raise ValueError(f"DMS file has missing sequences at rows {rows}") - - print(f"Preprocessing sequences into the {adapter.bases} alphabet...") - sequences = [ - adapter.canonicalize_sequence(s) for s in dms_df["sequence"].tolist() - ] - mutants = dms_df["mutant"].tolist() - wild_type = resolve_wild_type(adapter, mutants, sequences, construct) - print(f"Wild type: {len(wild_type)} nt, cross-checked against the assay") - - print( - f"Building contexts for: {', '.join(s.replace('_', '-') for s in strategies)}" - ) - table = build_tasks( - mutants, - sequences, - wild_type, - adapter.bases, - strategies, - verbose=not args.quiet_skips, - ) - n_scorable = int(table.scorable.sum()) - print( - f"Prepared {table.n_terms()} log-probability terms across {n_scorable} " - f"scorable variants (of {len(sequences)} total), deduplicated to " - f"{len(table.contexts)} unique contexts" - ) - if not table.contexts: - raise ValueError("No scorable variants found") - - max_tokens = getattr(args, "max_tokens", None) - if max_tokens is not None: - # The declared count, not the loaded one: this guard runs before the - # model is loaded so that an unsupported request fails cheaply - budget = window_budget(adapter, max_tokens) - if needs_windowing(table.contexts, budget): - print( - f"Windowing contexts to {budget} positions around the masked span" - ) - window_contexts(table, budget) - - device = args.device - if device is None: - import torch - - device = "cuda:0" if torch.cuda.is_available() else "cpu" - if adapter.requires_cuda and not device.startswith("cuda"): - raise ValueError( - f"{adapter.name} has no working CPU path. Pass a CUDA device" - ) - args.device = device - print(f"Initializing {adapter.name} on {device}...") - adapter.load(args) - adapter.check_alphabet() - - pad_contexts(table, adapter) - print("Running inference...") - scores = accumulate_scores( - adapter, - table, - n_rows=len(dms_df), - batch_size=args.batch_size, - max_batch_tokens=args.max_batch_tokens, - ) - - for i, strategy in enumerate(strategies): - dms_df[f"{adapter.score_column}_{strategy}"] = scores[i] - if len(strategies) == 1: - dms_df[adapter.score_column] = scores[0] - - print("\nSummary:") - print(f"Number of sequences: {len(sequences)}") - for i, strategy in enumerate(strategies): - correlation, pvalue = spearmanr( - dms_df["DMS_score"], scores[i], nan_policy="omit" - ) - print( - f" {strategy.replace('_', '-'):10s} Spearman {correlation:+.4f} " - f"(p {pvalue:.2e})" - ) - if len(strategies) > 1: - identical = np.allclose( - scores[:, table.scorable], scores[0, table.scorable] - ) - n_multi = 0 - for mutant in np.asarray(mutants)[table.scorable]: - if str(mutant).count(",") > 0: - n_multi += 1 - if n_multi == 0 and not identical: - raise ValueError( - "Every variant is a single mutant, so the strategies must be " - "identical, but they are not. This is a bug" - ) - print( - f" strategies identical: {identical} " - f"({n_multi} multi-mutants among {n_scorable} scorable variants)" - ) - - output_file = output_dir / f"{dms_id}.csv" - temporary = output_file.with_name(f".{output_file.name}.tmp") - manifest_file = output_dir / f"{dms_id}.manifest.json" - manifest_temporary = manifest_file.with_name(f".{manifest_file.name}.tmp") - dms_df.to_csv(temporary, index=False) - write_manifest( - manifest_temporary, - dms_id, - adapter, - args, - strategies, - table, - wild_type, - ) - temporary.replace(output_file) - manifest_temporary.replace(manifest_file) - print(f"Output saved to: {output_file}") - - # Model libraries raise unrelated exception types at this CLI boundary - except Exception as err: # noqa: BLE001 - print(f"Error: {err}", file=sys.stderr) - sys.exit(1) diff --git a/fitness/merge_scoring_files.py b/fitness/merge_scoring_files.py deleted file mode 100755 index 2e8de20..0000000 --- a/fitness/merge_scoring_files.py +++ /dev/null @@ -1,279 +0,0 @@ -#!/usr/bin/env python3 -"""Merge processed DMS assay CSVs with model prediction scores.""" - -import argparse -import logging -import tempfile -from pathlib import Path - -import pandas as pd - -if __package__: - from .model_registry import ( - ALL_MODELS, - ASSAY_GROUPS, - SCORE_COLS, - resolve_source, - ) -else: - from model_registry import ALL_MODELS, ASSAY_GROUPS, SCORE_COLS, resolve_source - -logging.basicConfig( - level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s" -) -logger = logging.getLogger(__name__) - - -def get_mutation_column(df): - """Find the mutation column name in a DataFrame.""" - for col in ["mutant", "mutation", "Mutation", "mutations"]: - if col in df.columns: - return col - raise ValueError("Couldn't find a mutation column in the dataframe") - - -def standardize_mutation(mutation): - """Replace T with U and strip spaces (DNA -> RNA notation).""" - return str(mutation).replace("T", "U").replace(" ", "") - - -def combine_csv_data( - processed_folder, - model_predictions_folder, - output_folder, - model_list, - score_cols_dict, - allow_incomplete=False, - assay_types=None, - assay_group="all", -): - """ - Merge each assay CSV with model prediction scores via inner join on mutations. - - Every requested model must cover every assay in the selected group. - The coverage check runs before any output is written, and all merged files - are staged before they replace prior outputs. - """ - if len(model_list) != len(set(model_list)): - raise ValueError(f"Model list contains duplicates: {model_list}") - processed_files = sorted(Path(processed_folder).glob("*.csv")) - if not processed_files: - raise FileNotFoundError(f"No assay CSVs found under {processed_folder}") - if assay_group != "all" and assay_group not in ASSAY_GROUPS: - raise ValueError(f"Unknown assay group: {assay_group!r}") - if assay_types is not None: - unknown = sorted( - path.stem for path in processed_files if path.stem not in assay_types - ) - if unknown: - raise ValueError( - f"Processed assays are absent from the reference sheet: {unknown[:5]}" - ) - if assay_group != "all": - if assay_types is None: - raise ValueError("assay_types is required when selecting an assay group") - selected_types = ASSAY_GROUPS[assay_group] - processed_files = [ - path for path in processed_files if assay_types[path.stem] in selected_types - ] - if not processed_files: - raise FileNotFoundError( - f"No {assay_group} assay CSVs found under {processed_folder}" - ) - processed_names = {path.name for path in processed_files} - - expected = {} - for model_name in model_list: - if model_name == "EVmutation": - expected[model_name] = set() - else: - expected[model_name] = processed_names - - missing = {} - for model_name, assay_names in expected.items(): - folder, _ = resolve_source(score_cols_dict, model_name) - absent = sorted( - name - for name in assay_names - if not (Path(model_predictions_folder) / folder / name).is_file() - ) - if absent: - missing[model_name] = absent - if missing and not allow_incomplete: - detail = " | ".join( - f"{model}: {len(files)} missing, including {files[:3]}" - for model, files in sorted(missing.items()) - ) - raise FileNotFoundError( - f"Prediction coverage is incomplete under {model_predictions_folder}: {detail}" - ) - - output_path = Path(output_folder) - output_path.parent.mkdir(parents=True, exist_ok=True) - contributed = {model: set() for model in model_list} - - with tempfile.TemporaryDirectory( - dir=output_path.parent, prefix=f".{output_path.name}-" - ) as staging_dir: - staging_path = Path(staging_dir) - for processed_file in processed_files: - csv_file = processed_file.name - - df = pd.read_csv(processed_file) - mutation_col = get_mutation_column(df) - df = df.dropna(subset=[mutation_col]) - df[mutation_col] = df[mutation_col].apply(standardize_mutation) - - for model_name in model_list: - folder, score_col = resolve_source(score_cols_dict, model_name) - model_path = Path(model_predictions_folder) / folder / csv_file - if not model_path.exists(): - logger.warning(f"Model file {csv_file} not found in {model_name}") - continue - - model_df = pd.read_csv(model_path) - model_mutation_col = get_mutation_column(model_df) - if score_col not in model_df.columns: - raise KeyError( - f"{model_path} has no column {score_col!r}. It holds " - f"{list(model_df.columns)}" - ) - model_df = model_df[[model_mutation_col, score_col]] - model_df.columns = [mutation_col, f"{model_name}_score"] - model_df = model_df.dropna(subset=[mutation_col]) - model_df[mutation_col] = model_df[mutation_col].apply( - standardize_mutation - ) - duplicated = model_df[model_df.duplicated(mutation_col, keep=False)] - if not duplicated.empty: - conflicting = duplicated.groupby(mutation_col, dropna=False)[ - f"{model_name}_score" - ].nunique(dropna=False) - conflicting = conflicting[conflicting > 1] - if not conflicting.empty: - raise ValueError( - f"{model_path} has conflicting scores for duplicate " - f"mutations: {list(conflicting.index[:5])}" - ) - model_df = model_df.drop_duplicates(subset=[mutation_col], keep="first") - - original_row_count = len(df) - df = df.merge(model_df, on=mutation_col, how="inner") - if len(df) != original_row_count: - raise ValueError( - f"Row count mismatch in {csv_file} after merging {model_name}: " - f"Expected {original_row_count}, but got {len(df)}" - ) - contributed[model_name].add(csv_file) - - df.to_csv(staging_path / csv_file, index=False) - - absent = sorted(model for model, assays in contributed.items() if not assays) - if absent and not allow_incomplete: - raise FileNotFoundError( - f"No predictions found for {absent} under {model_predictions_folder}. " - "Check the folder names against SCORE_COLS, or pass --allow_incomplete " - "to merge without them." - ) - - output_path.mkdir(parents=True, exist_ok=True) - staged_names = {path.name for path in staging_path.iterdir()} - for stale_file in output_path.glob("*.csv"): - if stale_file.name not in staged_names: - stale_file.unlink() - for staged_file in staging_path.iterdir(): - staged_file.replace(output_path / staged_file.name) - - for model_name, assays in sorted(contributed.items()): - logger.info(f"{model_name}: {len(assays)} assays merged") - - -def main(): - parser = argparse.ArgumentParser( - description="Combine processed CSV data with model predictions." - ) - parser.add_argument( - "--processed_folder", - required=True, - help="Path to the folder containing processed CSV files.", - ) - parser.add_argument( - "--model_predictions_folder", - required=True, - help="Path to the folder containing model prediction subfolders.", - ) - parser.add_argument( - "--output_folder", - required=True, - help="Path to the folder where combined CSV files will be saved.", - ) - parser.add_argument( - "--reference_file", - type=str, - default=str(Path(__file__).with_name("reference_sheet_final.csv")), - help="Reference sheet used to identify the assays", - ) - parser.add_argument( - "--models", - nargs="+", - default=None, - help="Model entries to merge (default: every entry in ALL_MODELS). Use " - "this to merge a subset, such as one masked model's four fill " - "strategies: rna_fm_wt_fill rna_fm_mask_fill rna_fm_mut_fill rna_fm_match_fill", - ) - parser.add_argument( - "--type", - default="ncRNA", - choices=["all", *ASSAY_GROUPS], - help="Assay group to merge (default: ncRNA)", - ) - parser.add_argument( - "--allow_incomplete", - action="store_true", - help="Merge even when a requested model has no predictions at all. Off by " - "default, so that a misspelled prediction folder fails instead of " - "producing a merge with that model silently missing", - ) - parser.add_argument( - "--assays_with_MSAs_only", - action="store_true", - help="Focus on assays with MSAs only (i.e., EVmutation)", - ) - args = parser.parse_args() - - if args.assays_with_MSAs_only: - model_list = ["EVmutation"] - else: - model_list = args.models if args.models else ALL_MODELS - unknown = [m for m in model_list if m not in SCORE_COLS] - if unknown: - parser.error(f"No score column configured for: {unknown}") - - reference = pd.read_csv(args.reference_file, encoding="utf-8-sig") - required = {"DMS_ID", "RNA_TYPE"} - missing = sorted(required - set(reference.columns)) - if missing: - parser.error(f"Reference sheet is missing columns: {missing}") - if reference[list(required)].isna().any().any(): - parser.error("Reference sheet has missing DMS_ID or RNA_TYPE values") - duplicated = sorted( - reference.loc[reference["DMS_ID"].duplicated(), "DMS_ID"].astype(str) - ) - if duplicated: - parser.error(f"Reference sheet repeats DMS_ID values: {duplicated[:5]}") - assay_types = dict(zip(reference["DMS_ID"], reference["RNA_TYPE"])) - - combine_csv_data( - args.processed_folder, - args.model_predictions_folder, - args.output_folder, - model_list, - SCORE_COLS, - allow_incomplete=args.allow_incomplete, - assay_types=assay_types, - assay_group=args.type, - ) - - -if __name__ == "__main__": - main() diff --git a/fitness/performance_fitness.py b/fitness/performance_fitness.py deleted file mode 100755 index 666ca80..0000000 --- a/fitness/performance_fitness.py +++ /dev/null @@ -1,392 +0,0 @@ -#!/usr/bin/env python3 -"""Evaluate fitness prediction models across RNA assays.""" - -import argparse -import logging -import os -from pathlib import Path - -import numpy as np -import pandas as pd -from scipy import stats -from sklearn.metrics import matthews_corrcoef, roc_auc_score - -if __package__: - from .model_registry import ALL_MODELS, ASSAY_GROUPS -else: - from model_registry import ALL_MODELS, ASSAY_GROUPS - -logging.basicConfig(level=logging.WARNING) -logger = logging.getLogger(__name__) - -DEPTHS = ("single", "multiple") -METRICS = ("Spearman", "AUC", "MCC") -RNA_TYPES = ("mRNA-splicing", "mRNA-coding", "tRNA", "Aptamer", "Ribozyme") - - -def calculate_metrics( - assay_scores: np.ndarray, model_scores: np.ndarray -) -> dict[str, float]: - """Calculate directed Spearman, AUC, and MCC. - - AUC binarizes assay scores at their median. MCC binarizes both score vectors - at their respective medians. - """ - binary_assay = assay_scores > np.median(assay_scores) - binary_model = model_scores > np.median(model_scores) - return { - "Spearman": stats.spearmanr(assay_scores, model_scores).correlation, - "AUC": roc_auc_score(binary_assay, model_scores), - "MCC": matthews_corrcoef(binary_assay, binary_model), - } - - -def get_performance_dataset( - data: pd.DataFrame, assay_column: str, score_columns: list[str] -) -> dict[str, dict[str, float]]: - """Calculate each model's metrics from complete finite values.""" - data = data.dropna(subset=["mutant"]) - missing_metrics = dict.fromkeys(METRICS, np.nan) - results = {} - - for score_column in score_columns: - pairs = data[[assay_column, score_column]].apply(pd.to_numeric, errors="coerce") - invalid = (~np.isfinite(pairs)).sum() - invalid = invalid[invalid > 0].astype(int).to_dict() - if invalid: - raise ValueError(f"Missing or nonfinite metric values: {invalid}") - if pairs.empty: - results[score_column] = missing_metrics.copy() - continue - - try: - results[score_column] = calculate_metrics( - pairs[assay_column].to_numpy(), pairs[score_column].to_numpy() - ) - except (TypeError, ValueError) as error: - logger.warning("Could not calculate %s: %s", score_column, error) - results[score_column] = missing_metrics.copy() - - return results - - -def _add_depth(data: pd.DataFrame) -> None: - """Classify mutations without changing the source mutation column.""" - mutation_count = data["mutant"].astype("string").str.count(",") + 1 - data["Depth"] = np.where(mutation_count == 1, "single", "multiple") - data.loc[mutation_count.isna(), "Depth"] = pd.NA - - -def _metric_rows( - dms_id: str, - rna_type: str, - results: dict[str, dict[str, float]], - depth: str | None = None, -) -> list[dict]: - """Convert one assay's metric dictionaries to rows.""" - rows = [] - for model, metrics in results.items(): - row = {"DMS_ID": dms_id, "RNA_TYPE": rna_type, "Model": model} - if depth is not None: - row["Depth"] = depth - rows.append(row | metrics) - return rows - - -def filter_msa_assays( - reference: pd.DataFrame, combined_dir: str | Path -) -> pd.DataFrame: - """Keep assays with at least ten EVmutation predictions.""" - combined_dir = Path(combined_dir) - available = [] - - for dms_id in reference["DMS_ID"]: - assay_path = combined_dir / f"{dms_id}.csv" - if not assay_path.exists(): - continue - assay = pd.read_csv(assay_path) - if ( - "EVmutation_score" in assay - and assay["EVmutation_score"].notna().sum() >= 10 - ): - available.append(dms_id) - - dropped = len(reference) - len(available) - if dropped: - print(f"Dropped {dropped} datasets missing EVmutation scores") - return reference[reference["DMS_ID"].isin(available)].copy() - - -def load_assay_metrics( - reference: pd.DataFrame, - combined_dir: str | Path, - score_columns: list[str], - msa_only: bool = False, -) -> tuple[pd.DataFrame, pd.DataFrame]: - """Calculate overall and mutation-depth metrics for every selected assay.""" - assay_rows = [] - depth_rows = [] - combined_dir = Path(combined_dir) - filtered_sizes = [] - - for assay_info in reference.itertuples(index=False): - assay_path = combined_dir / f"{assay_info.DMS_ID}.csv" - if not assay_path.is_file(): - raise FileNotFoundError(f"Selected assay is missing: {assay_path}") - assay = pd.read_csv(assay_path) - missing_columns = [column for column in score_columns if column not in assay] - if missing_columns: - raise KeyError(f"{assay_path} is missing score columns: {missing_columns}") - - if "Depth" not in assay: - _add_depth(assay) - - filtered = assay - if msa_only: - filtered = assay[assay["EVmutation_score"].notna()] - filtered_sizes.append((len(assay), len(filtered))) - - filtered_results = get_performance_dataset(filtered, "DMS_score", score_columns) - assay_rows.extend( - _metric_rows(assay_info.DMS_ID, assay_info.RNA_TYPE, filtered_results) - ) - - depth_rows.extend( - _metric_rows( - assay_info.DMS_ID, - assay_info.RNA_TYPE, - filtered_results, - "overall", - ) - ) - for depth in DEPTHS: - depth_results = get_performance_dataset( - filtered[filtered["Depth"] == depth], "DMS_score", score_columns - ) - depth_rows.extend( - _metric_rows( - assay_info.DMS_ID, - assay_info.RNA_TYPE, - depth_results, - depth, - ) - ) - - if msa_only: - unfiltered = sum(size[0] for size in filtered_sizes) - filtered = sum(size[1] for size in filtered_sizes) - if not unfiltered: - raise ValueError("No assay rows remain after MSA filtering") - print(f"Filtered dataset sizes: {[size[1] for size in filtered_sizes]}") - print( - f"{filtered / unfiltered * 100:.1f}% of samples remain after " - "filtering for EVmutation scores" - ) - - return pd.DataFrame(assay_rows), pd.DataFrame(depth_rows) - - -def aggregate_by_depth(depth_metrics: pd.DataFrame, models: list[str]) -> pd.DataFrame: - """Average assay metrics within each mutation depth.""" - means = {} - for model in models: - means[model] = {} - for depth in DEPTHS: - assays = depth_metrics[ - (depth_metrics["Model"] == model) & (depth_metrics["Depth"] == depth) - ] - means[model][depth] = { - metric: np.nanmean(assays[metric]) for metric in METRICS - } - means[model]["All"] = { - metric: np.nanmean([means[model][depth][metric] for depth in DEPTHS]) - for metric in METRICS - } - - rows = [] - for depth in ("All", "multiple", "single"): - for model in models: - row = {"Depth": depth, "Model": model} - row.update( - {f"{metric}_Mean": means[model][depth][metric] for metric in METRICS} - ) - rows.append(row) - return pd.DataFrame(rows) - - -def aggregate_by_rna_type( - assay_metrics: pd.DataFrame, - models: list[str], - rna_types: list[str], -) -> pd.DataFrame: - """Average assay metrics by RNA type and equally across RNA types.""" - grouped = assay_metrics.groupby(["Model", "RNA_TYPE"], sort=False)[ - list(METRICS) - ].mean() - - rows = [] - for model in models: - row = {"Model": model} - for metric in METRICS: - type_means = [] - for rna_type in rna_types: - mean = grouped.loc[(model, rna_type), metric] - row[f"{metric}_{rna_type}_Mean"] = mean - type_means.append(mean) - row[f"{metric}_All_Mean"] = np.mean(type_means) - rows.append(row) - - columns = ["Model"] - for metric in METRICS: - for rna_type in [*rna_types, "All"]: - columns.append(f"{metric}_{rna_type}_Mean") - return pd.DataFrame(rows, columns=columns) - - -def aggregate_by_type_and_depth( - depth_metrics: pd.DataFrame, models: list[str], rna_types: list[str] -) -> pd.DataFrame: - """Average metrics by RNA type and mutation depth.""" - grouped = depth_metrics.groupby(["Model", "Depth", "RNA_TYPE"], sort=False)[ - list(METRICS) - ].mean() - rows = [] - - for model in models: - for depth in (*DEPTHS, "overall"): - type_values = [] - for rna_type in rna_types: - values = grouped.loc[(model, depth, rna_type)] - row = {"Model": model, "Depth": depth, "RNA_TYPE": rna_type} - row.update({f"{metric}_Mean": values[metric] for metric in METRICS}) - rows.append(row) - type_values.append(values) - - all_values = pd.DataFrame(type_values).mean() - row = {"Model": model, "Depth": depth, "RNA_TYPE": "All"} - row.update({f"{metric}_Mean": all_values[metric] for metric in METRICS}) - rows.append(row) - - return pd.DataFrame(rows) - - -def save_results( - output_dir: str | Path, - assay_metrics: pd.DataFrame, - models: list[str], - by_rna_type: pd.DataFrame, - by_depth: pd.DataFrame, - by_type_and_depth: pd.DataFrame, -) -> None: - """Write the five benchmark result tables.""" - output_dir = Path(output_dir) - output_dir.mkdir(parents=True, exist_ok=True) - tables = { - "assay_level_results.csv": assay_metrics, - "results_by_mutation_depth.csv": by_depth, - "results_by_rna_type.csv": by_rna_type, - "results_by_rna_type_and_depth.csv": by_type_and_depth, - } - for filename, table in tables.items(): - table.to_csv(output_dir / filename, index=False) - - transposed = assay_metrics[["DMS_ID", "RNA_TYPE"]].drop_duplicates().copy() - for metric in METRICS: - metric_table = assay_metrics.pivot( - index="DMS_ID", columns="Model", values=metric - ) - for model in models: - transposed[f"{metric}_{model}"] = transposed["DMS_ID"].map( - metric_table[model] - ) - transposed.to_csv(output_dir / "assay_level_results_transposed.csv", index=False) - - -def select_assays(reference: pd.DataFrame, assay_type: str) -> pd.DataFrame: - """Select an assay group.""" - if assay_type == "all": - return reference.copy() - return reference[reference["RNA_TYPE"].isin(ASSAY_GROUPS[assay_type])].copy() - - -def main(args) -> None: - """Run the fitness performance workflow.""" - reference = select_assays(pd.read_csv(args.reference_file), args.type) - if reference.empty: - raise ValueError(f"No assays found for type {args.type!r}") - if args.msa_only: - reference = filter_msa_assays(reference, args.combined_dir) - - models = list(args.models or ALL_MODELS) - if args.msa_only and "EVmutation" not in models: - models.append("EVmutation") - - score_columns = [f"{model}_score" for model in models] - rna_types = [ - rna_type for rna_type in RNA_TYPES if (reference["RNA_TYPE"] == rna_type).any() - ] - assay_metrics, depth_metrics = load_assay_metrics( - reference, args.combined_dir, score_columns, args.msa_only - ) - by_rna_type = aggregate_by_rna_type(assay_metrics, score_columns, rna_types) - by_depth = aggregate_by_depth(depth_metrics, score_columns) - by_type_and_depth = aggregate_by_type_and_depth( - depth_metrics, score_columns, rna_types - ) - save_results( - args.performance_dir, - assay_metrics, - score_columns, - by_rna_type, - by_depth, - by_type_and_depth, - ) - - print("\nMetrics by RNA Type:") - print(by_rna_type) - print("\nMetrics by Mutation Depth:") - print(by_depth) - print("\nMetrics by RNA Type and Mutation Depth:") - print(by_type_and_depth) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser( - description="Analyze fitness predictions across RNA assays" - ) - parser.add_argument( - "--reference_file", - default="reference_sheet_final.csv", - help="Path to the reference CSV file", - ) - parser.add_argument( - "--combined_dir", - default="merged", - help="Directory containing merged assay CSV files", - ) - parser.add_argument( - "--performance_dir", - default=None, - help="Directory for performance tables", - ) - parser.add_argument( - "--msa_only", - action="store_true", - help="Only evaluate assays with EVmutation scores", - ) - parser.add_argument( - "--models", - nargs="+", - default=None, - help="Merged model names to evaluate instead of the defaults", - ) - parser.add_argument( - "--type", - default="ncRNA", - choices=["all", *ASSAY_GROUPS], - help="Assay group. The default is ncRNA. Non-coding includes mRNA-splicing", - ) - arguments = parser.parse_args() - if arguments.performance_dir is None: - arguments.performance_dir = os.getcwd() - main(arguments) diff --git a/fitness/pixi.lock b/fitness/pixi.lock deleted file mode 100644 index 5ffe7c8..0000000 --- a/fitness/pixi.lock +++ /dev/null @@ -1,1443 +0,0 @@ -version: 7 -platforms: -- name: linux-64 - virtual-packages: - - __unix=0=0 - - __linux=4.18 - - __glibc=2.28 - - __archspec=0=x86_64 -environments: - default: - channels: - - url: https://conda.anaconda.org/pytorch/ - - url: https://conda.anaconda.org/conda-forge/ - indexes: - - https://pypi.org/simple - packages: - linux-64: - - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-7_kmp_llvm.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/blas-2.116-mkl.tar.bz2 - - conda: https://conda.anaconda.org/conda-forge/linux-64/blas-devel-3.9.0-16_linux64_mkl.tar.bz2 - - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_10.conda - 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-[dependencies] -cpuonly = { version = "*", channel = "pytorch" } -go-shfmt = "==3.13.1" -numpy = "2.*" -pandas = "2.*" -pytest = "==8.4.2" -pytest-cov = "==7.0.0" -python = "3.11.*" -pytorch = { version = "2.*", channel = "pytorch" } -ruff = "==0.15.21" -scikit-learn = "1.*" -scipy = "1.*" -shellcheck = "==0.11.0" -taplo = "==0.10.0" -tqdm = "==4.69.0" - -[pypi-dependencies] -rnagym = { path = "..", editable = true } - -[tasks] -coverage = "bash sh/coverage.sh" -fmt = "bash sh/format.sh" -lint = "bash sh/lint.sh" -test = "cd .. && python -m pytest -q tests/test_fitness.py" - -# TODO(MCA): Add isolated environments for each fitness model adapter diff --git a/fitness/sh/coverage.sh b/fitness/sh/coverage.sh deleted file mode 100644 index 2042ffc..0000000 --- a/fitness/sh/coverage.sh +++ /dev/null @@ -1,15 +0,0 @@ -#!/usr/bin/env bash -set -euo pipefail - -export COVERAGE_FILE="$PWD/.pixi/.coverage" -cd .. -python -m pytest -q tests/test_fitness.py \ - --cov=fitness.analyze_fill_strategies \ - --cov=fitness.baselines.Evo.score_evo2_single_dms \ - --cov=fitness/baselines/Nucleotide_Transformer \ - --cov=fitness/baselines/masked_lm \ - --cov=fitness.merge_scoring_files \ - --cov=fitness.model_registry \ - --cov=fitness.performance_fitness \ - --cov-fail-under=80 \ - --cov-report=term-missing diff --git a/fitness/sh/format.sh b/fitness/sh/format.sh deleted file mode 100644 index 624cb65..0000000 --- a/fitness/sh/format.sh +++ /dev/null @@ -1,23 +0,0 @@ -#!/usr/bin/env bash -set -euo pipefail - -taplo format pixi.toml -ruff check --fix --extend-select I \ - analyze_fill_strategies.py \ - baselines/Evo/score_evo2_single_dms.py \ - baselines/{AIDO_RNA,Nucleotide_Transformer,Orthrus,RNAGenesis,RNA_FM,RiNALMo}/*.py \ - baselines/masked_lm \ - merge_scoring_files.py \ - model_registry.py \ - performance_fitness.py \ - ../tests/test_fitness.py -ruff format \ - analyze_fill_strategies.py \ - baselines/Evo/score_evo2_single_dms.py \ - baselines/{AIDO_RNA,Nucleotide_Transformer,Orthrus,RNAGenesis,RNA_FM,RiNALMo}/*.py \ - baselines/masked_lm \ - merge_scoring_files.py \ - model_registry.py \ - performance_fitness.py \ - ../tests/test_fitness.py -shfmt -w sh baselines/{AIDO_RNA,Nucleotide_Transformer,Orthrus,RNAGenesis,RNA_FM,RiNALMo}/*.sh diff --git a/fitness/sh/lint.sh b/fitness/sh/lint.sh deleted file mode 100644 index e2a65e9..0000000 --- a/fitness/sh/lint.sh +++ /dev/null @@ -1,24 +0,0 @@ -#!/usr/bin/env bash -set -euo pipefail - -taplo lint pixi.toml -ruff check --extend-select I \ - analyze_fill_strategies.py \ - baselines/Evo/score_evo2_single_dms.py \ - baselines/{AIDO_RNA,Nucleotide_Transformer,Orthrus,RNAGenesis,RNA_FM,RiNALMo}/*.py \ - baselines/masked_lm \ - merge_scoring_files.py \ - model_registry.py \ - performance_fitness.py \ - ../tests/test_fitness.py -ruff format --check \ - analyze_fill_strategies.py \ - baselines/Evo/score_evo2_single_dms.py \ - baselines/{AIDO_RNA,Nucleotide_Transformer,Orthrus,RNAGenesis,RNA_FM,RiNALMo}/*.py \ - baselines/masked_lm \ - merge_scoring_files.py \ - model_registry.py \ - performance_fitness.py \ - ../tests/test_fitness.py -shfmt -d sh baselines/{AIDO_RNA,Nucleotide_Transformer,Orthrus,RNAGenesis,RNA_FM,RiNALMo}/*.sh -shellcheck sh/*.sh baselines/{AIDO_RNA,Nucleotide_Transformer,Orthrus,RNAGenesis,RNA_FM,RiNALMo}/*.sh diff --git a/fitness/utils/convert_table.py b/fitness/utils/convert_table.py deleted file mode 100644 index 85b9f02..0000000 --- a/fitness/utils/convert_table.py +++ /dev/null @@ -1,324 +0,0 @@ -import csv -import argparse -import re -from pathlib import Path -from Bio import Data -from Bio.Data import CodonTable - - -def parse_fasta(fasta_file): - """Parse a FASTA file and return the sequence.""" - sequence = "" - with open(fasta_file, "r") as f: - for line in f: - if not line.startswith(">"): # Skip header lines - sequence += line.strip() - return sequence.upper() - - -def get_codon_table(table_id=1): - """Get the codon table based on NCBI table ID. - - Args: - table_id (int): NCBI genetic code table ID - 1 = Standard (default) - 2 = Vertebrate Mitochondrial - 3 = Yeast Mitochondrial - 4 = Mold, Protozoan, Coelenterate Mitochondrial and Mycoplasma/Spiroplasma - 5 = Invertebrate Mitochondrial - 6 = Ciliate Nuclear and Dasycladacean - 9 = Echinoderm Mitochondrial and Flatworm Mitochondrial - 10 = Euplotid Nuclear - 11 = Bacterial, Archaeal and Plant Plastid - 12 = Alternative Yeast Nuclear - 13 = Ascidian Mitochondrial - 14 = Alternative Flatworm Mitochondrial - 16 = Chlorophycean Mitochondrial - 21 = Trematode Mitochondrial - 22 = Scenedesmus obliquus Mitochondrial - 23 = Thraustochytrium Mitochondrial - 24 = Rhabdopleuridae Mitochondrial - 25 = Candidate Division SR1 and Gracilibacteria - 26 = Pachysolen tannophilus Nuclear - 27 = Karyorelict Nuclear - 28 = Condylostoma Nuclear - 29 = Mesodinium Nuclear - 30 = Peritrich Nuclear - 31 = Blastocrithidia Nuclear - 33 = Cephalodiscidae Mitochondrial - """ - biopython_table = CodonTable.unambiguous_dna_by_id[table_id] - - # Create our own codon table dictionary from the Biopython table - codon_table = {} - for codon, aa in biopython_table.forward_table.items(): - codon_table[codon] = aa - - # Add stop codons - for stop_codon in biopython_table.stop_codons: - codon_table[stop_codon] = "*" - - return codon_table - - -def get_reverse_codon_table(codon_table): - """Create reverse codon table (amino acid to possible codons).""" - reverse_codon_table = {} - for codon, aa in codon_table.items(): - if aa not in reverse_codon_table: - reverse_codon_table[aa] = [] - reverse_codon_table[aa].append(codon) - return reverse_codon_table - - -def list_available_codon_tables(): - """List all available codon tables with names and IDs.""" - print("Available codon tables:") - print("ID | Name") - print("---|-------------------") - for table_id in sorted(CodonTable.unambiguous_dna_by_id.keys()): - table = CodonTable.unambiguous_dna_by_id[table_id] - print(f"{table_id:2d} | {table.names[0]}") - - -def translate_dna(dna_sequence, codon_table): - """Translate DNA sequence to protein using specified codon table.""" - protein = "" - for i in range(0, len(dna_sequence), 3): - if i + 3 <= len(dna_sequence): - codon = dna_sequence[i : i + 3] - protein += codon_table.get(codon, "X") - return protein - - -def parse_mutation(mutation_code): - """Parse mutation code like 'H24S' into original AA, position, and new AA.""" - mutation_code = mutation_code.strip('" ') # Remove quotes and spaces - if not re.match(r"^[A-Z]\d+[A-Z]$", mutation_code): - raise ValueError(f"Invalid mutation format: {mutation_code}") - - orig_aa = mutation_code[0] - # Parse the position but keep it as 1-based for validation against the protein sequence - pos_1based = int(mutation_code[1:-1]) - new_aa = mutation_code[-1] - - return orig_aa, pos_1based, new_aa - - -def apply_mutation_and_get_nucleotide_change( - original_dna, position_1based, new_aa, original_protein, reverse_codon_table -): - """Apply a protein mutation to DNA and return both the nucleotide changes and the new DNA.""" - # Convert to 0-based for internal use - pos_0based = position_1based - 1 - - # Check position validity - if pos_0based < 0 or pos_0based >= len(original_protein): - raise ValueError( - f"Position {position_1based} is out of range for protein length {len(original_protein)}" - ) - - # Find the corresponding DNA position (3 nucleotides per amino acid) - dna_pos = pos_0based * 3 - - # Get the original codon at this position - original_codon = original_dna[dna_pos : dna_pos + 3] - - # Check if the new_aa is in the reverse_codon_table - if new_aa not in reverse_codon_table: - raise ValueError(f"Amino acid '{new_aa}' not found in the codon table") - - # For consistency with the DMS data, use the same codon pattern when possible - # We'll try to change the minimum number of nucleotides - best_codon = None - min_changes = 3 - - for new_codon in reverse_codon_table[new_aa]: - changes = sum(1 for a, b in zip(original_codon, new_codon) if a != b) - if changes <= min_changes: - min_changes = changes - best_codon = new_codon - - if best_codon is None: - # This shouldn't happen now with the <= operator, but let's keep it as a safety check - print( - f"Warning: No suitable codon found for {new_aa} at position {position_1based}" - ) - best_codon = reverse_codon_table[new_aa][0] # Use first available codon - - # Identify specific nucleotide changes - nt_changes = [] - for i in range(3): - if original_codon[i] != best_codon[i]: - nt_change = f"{original_codon[i]}{dna_pos+i+1}{best_codon[i]}" - nt_changes.append(nt_change) - - # Create the new DNA sequence - new_dna = original_dna[:dna_pos] + best_codon + original_dna[dna_pos + 3 :] - - return nt_changes, new_dna - - -def process_mutations_file( - dms_file, output_file, original_dna, original_protein, reverse_codon_table -): - """Process the mutations file and convert protein mutations to DNA sequences.""" - # Create output file - with open(output_file, "w", newline="") as outfile: - writer = csv.writer(outfile) - writer.writerow(["mutant", "DMS_score", "sequence"]) - - # Read the input file - with open(dms_file, "r") as infile: - reader = csv.DictReader(infile) - - # Track progress - total_rows = sum(1 for _ in open(dms_file)) - 1 # Subtract header - processed = 0 - errors = 0 - - for row in reader: - try: - protein_mutant = row.get("mutant", "").strip('" ') - dms_score = row.get("DMS_score", "") - - # If no mutation, skip - if not protein_mutant: - continue - - # Split mutations - now handling both comma and colon separators - protein_mutations = [] - if "," in protein_mutant: - protein_mutations = protein_mutant.split(",") - elif ":" in protein_mutant: - protein_mutations = protein_mutant.split(":") - else: - # Single mutation - protein_mutations = [protein_mutant] - - current_dna = original_dna - all_nt_changes = [] - - for mut in protein_mutations: - mut = mut.strip('" ') # Remove quotes and spaces - if not mut: # Skip empty mutations - continue - - orig_aa, pos_1based, new_aa = parse_mutation(mut) - pos_0based = pos_1based - 1 - - # Verify the original amino acid matches - if ( - 0 <= pos_0based < len(original_protein) - and original_protein[pos_0based] != orig_aa - ): - print( - f"Mismatch at position {pos_1based}: expected {orig_aa}, found {original_protein[pos_0based]}" - ) - raise ValueError( - f"Mismatch at position {pos_1based}: expected {orig_aa}, found {original_protein[pos_0based]}" - ) - - # Apply the mutation to DNA and get nucleotide changes - nt_changes, current_dna = ( - apply_mutation_and_get_nucleotide_change( - current_dna, - pos_1based, - new_aa, - original_protein, - reverse_codon_table, - ) - ) - all_nt_changes.extend(nt_changes) - - # Convert nucleotide changes to comma-separated string - nt_mutant = ",".join(all_nt_changes) - - # Write the output row with nucleotide mutations - writer.writerow([nt_mutant, dms_score, current_dna]) - - except Exception as e: - print( - f"Error processing mutation {row.get('mutant', 'unknown')}: {e}" - ) - # Write the row with ERROR in sequence field - writer.writerow([protein_mutant, dms_score, "ERROR"]) - errors += 1 - - processed += 1 - if processed % 100 == 0: - print( - f"Processed {processed}/{total_rows} mutations ({processed/total_rows*100:.1f}%)" - ) - - print(f"Completed processing {processed} mutations with {errors} errors.") - - -def main(): - # Set up command line arguments - parser = argparse.ArgumentParser( - description="Convert protein mutations to nucleotide sequences." - ) - parser.add_argument("fasta_file", help="Input FASTA file with nucleotide sequence") - parser.add_argument("dms_file", help="Input DMS file with mutations") - parser.add_argument( - "--output", - "-o", - default=None, - help="Output CSV file (default: input_with_nucleotides.csv)", - ) - parser.add_argument( - "--table", - "-t", - type=int, - default=1, - help="NCBI genetic code table ID (default: 1, Standard)", - ) - parser.add_argument( - "--list-tables", - action="store_true", - help="List all available codon tables and exit", - ) - args = parser.parse_args() - - # List tables if requested - if args.list_tables: - list_available_codon_tables() - return - - # Determine output filename if not specified - if args.output is None: - output_file = Path(args.dms_file).stem + "_with_sequences.csv" - else: - output_file = args.output - - # Get the appropriate codon table - codon_table = get_codon_table(args.table) - reverse_codon_table = get_reverse_codon_table(codon_table) - - # Print the selected table - table_name = CodonTable.unambiguous_dna_by_id[args.table].names[0] - print(f"Using codon table {args.table}: {table_name}") - - print(f"Reading nucleotide sequence from {args.fasta_file}") - original_dna = parse_fasta(args.fasta_file) - print(f"Nucleotide sequence length: {len(original_dna)} bp") - - # Translate to protein - original_protein = translate_dna(original_dna, codon_table) - print(original_protein) - print(f"Translated protein length: {len(original_protein)} amino acids") - print(f"First 10 amino acids: {original_protein[:10]}") - print(f"Last 10 amino acids: {original_protein[-10:]}") - - # Process the mutations file - print(f"\nProcessing mutations from {args.dms_file}") - process_mutations_file( - args.dms_file, output_file, original_dna, original_protein, reverse_codon_table - ) - print(original_dna) - print(f"Processing complete. Output saved to {output_file}") - - -if __name__ == "__main__": - main() diff --git a/fitness/utils/cv_split.py b/fitness/utils/cv_split.py deleted file mode 100644 index 880c9e1..0000000 --- a/fitness/utils/cv_split.py +++ /dev/null @@ -1,111 +0,0 @@ -import os -import pandas as pd -import numpy as np -from Bio import pairwise2 -from sklearn.model_selection import train_test_split -import tqdm - - -def load_dms_data(dms_location, reference_file, assay_filename_col="DMS_ID"): - """Load all DMS data from CSV files.""" - reference_df = pd.read_csv(reference_file) - all_data = {} - - for filename in reference_df[assay_filename_col]: - file_path = os.path.join(dms_location, filename + ".csv") - df = pd.read_csv(file_path) - all_data[filename] = df - - return all_data - - -def random_split_assay(assay_data, test_size=0.2, random_state=42): - """Perform random train-validation split for a single assay.""" - return train_test_split(assay_data, test_size=test_size, random_state=random_state) - - -def sequence_similarity(seq1, seq2): - """Calculate sequence similarity between two sequences.""" - alignment = pairwise2.align.globalxx(seq1, seq2, one_alignment_only=True)[0] - return alignment.score / max(len(seq1), len(seq2)) - - -def minimize_similarity_split_assay(assay_data, test_size=0.2, max_iterations=10000): - """Perform train-validation split minimizing sequence similarity for a single assay.""" - train, val = random_split_assay(assay_data, test_size) - - for _ in tqdm.tqdm(range(max_iterations)): - improved = False - for i, row_val in val.iterrows(): - for j, row_train in train.iterrows(): - if ( - sequence_similarity(row_val["sequence"], row_train["sequence"]) - > 0.8 - ): # Threshold - val = pd.concat( - [val.drop(i), pd.DataFrame([row_train])], ignore_index=True - ) - train = pd.concat( - [train.drop(j), pd.DataFrame([row_val])], ignore_index=True - ) - improved = True - break - if improved: - break - if not improved: - break - - return train, val - - -def save_splits(splits, output_dir, split_type): - """Save train and validation splits to CSV files, one file per assay.""" - split_dir = os.path.join(output_dir, split_type) - train_dir = os.path.join(split_dir, "train") - val_dir = os.path.join(split_dir, "val") - - os.makedirs(train_dir, exist_ok=True) - os.makedirs(val_dir, exist_ok=True) - - for assay, (train, val) in splits.items(): - train_file = os.path.join(train_dir, f"{assay.replace('.csv', '')}_train.csv") - val_file = os.path.join(val_dir, f"{assay.replace('.csv', '')}_val.csv") - - train.to_csv(train_file, index=False) - val.to_csv(val_file, index=False) - - print(f"Saved {split_type} split files:") - print(f" Train directory: {train_dir}") - print(f" Validation directory: {val_dir}") - - -def main(dms_location, reference_file, output_dir): - # Load data - data = load_dms_data(dms_location, reference_file) - - # Random split for each assay - random_splits = {} - for assay, assay_data in data.items(): - train_random, val_random = random_split_assay(assay_data) - random_splits[assay] = (train_random, val_random) - print( - f"Random split for {assay} - Train size: {len(train_random)}, Validation size: {len(val_random)}" - ) - save_splits(random_splits, output_dir, "random") - - # Minimize similarity split for each assay - sim_splits = {} - for assay, assay_data in data.items(): - train_sim, val_sim = minimize_similarity_split_assay(assay_data) - sim_splits[assay] = (train_sim, val_sim) - print( - f"Similarity-based split for {assay} - Train size: {len(train_sim)}, Validation size: {len(val_sim)}" - ) - save_splits(sim_splits, output_dir, "min_similarity") - - -if __name__ == "__main__": - DMS_location = "Path to preprocessed DMS files" # Replace with actual path - reference_file = "../reference_sheet.csv" # Replace with actual path - output_dir = "./fitness_CV_splits" # Replace with desired output directory - main(DMS_location, reference_file, output_dir) diff --git a/leaderboard/2d/README.md b/leaderboard/2d/README.md index ff00dd8..f95ec6c 100644 --- a/leaderboard/2d/README.md +++ b/leaderboard/2d/README.md @@ -6,9 +6,8 @@ probabilities (`1 − Σj Pij`). For neural models, highly confident residues ar clipped to 1 following [Arnie's official RibonanzaNet inference](https://github.com/WaymentSteeleLab/arnie/blob/660de8139bd2198bbe115adadd5bc5f12183f9f4/src/arnie/pk_predictors.py#L111-L116) when summed pair probabilities exceed one at any given residue. DMS is scored -over A/C, CMCT over G/U, and other modalities over all bases. Spearman avoids -directly comparing reactivity magnitudes with probabilities or choosing a -classification threshold. The headline score averages 1M7, 2A3, DMS, and NMIA. +over A/C, CMCT over G/U, and other modalities over all bases. The headline score +averages 1M7, 2A3, DMS, and NMIA. BzCN (low replicate agreement), CMCT (only 11 replicate-bearing clusters), and degradation assays are reported only in [`leaderboard.csv`](leaderboard.csv). Constant predictions receive zero. diff --git a/leaderboard/3d/README.md b/leaderboard/3d/README.md index 5efc56d..de1c686 100644 --- a/leaderboard/3d/README.md +++ b/leaderboard/3d/README.md @@ -8,7 +8,7 @@ when both structures lack that interaction class and zero when only one does. Monomer metrics take the best match across experimental structures with the same sequence. Monomer sequence scores and individual multimer target scores are then averaged within each 40% sequence identity cluster, -then across clusters. Missing predictions score zero, and all metrics are +then across clusters. Failed predictions score 0, and all metrics are better when higher. Models are ranked by TM score. diff --git a/leaderboard/fitness/README.md b/leaderboard/fitness/README.md index 7a56818..ea4965c 100644 --- a/leaderboard/fitness/README.md +++ b/leaderboard/fitness/README.md @@ -1,190 +1,65 @@ -# RNAGym Fitness Leaderboard +# Fitness leaderboard -**Metric (updated in v0.1.1):** signed Spearman correlation between each model's score and the -measured DMS fitness, macro-averaged over the 3 ncRNA categories (ribozyme, tRNA, aptamer), with -each category weighted equally. This replaces the previous metric (absolute Spearman averaged over -all assays, including mRNA coding and splicing). - -Two changes from before: -1. Signed, not absolute: a negative value means the model's score is negatively correlated with fitness. -2. ncRNA only: the mRNA coding and splicing assays are excluded (see rationale below). - -## Leaderboard: signed Spearman, macro-mean over 3 ncRNA categories +Signed Spearman correlation with experimental fitness on 31 ncRNA assays. +Columns average assays within each category. Macro weights the three categories +equally. Higher is better. + | Rank | Model | Ribozyme (n=26) | tRNA (n=3) | Aptamer (n=2) | Macro (3 ncRNA) | |---:|:--|--:|--:|--:|--:| -| 1 | AIDO.RNA (650M) | 0.0660 | 0.4894 | 0.0934 | 0.2163 | -| 2 | Evo 2 (20B) | 0.1105 | 0.4345 | 0.0937 | 0.2129 | -| 3 | Evo 2 (40B) | 0.1081 | 0.4310 | 0.0970 | 0.2120 | -| 4 | AIDO.RNA (1.6B) | 0.0609 | 0.4884 | 0.0718 | 0.2070 | -| 5 | Evo 2 (1B base) | 0.0087 | 0.4466 | 0.1334 | 0.1962 | -| 6 | RNA-ERNIE | 0.1343 | 0.4161 | 0.0306 | 0.1937 | -| 7 | Evo 2 (7B) | 0.0651 | 0.3867 | 0.1192 | 0.1904 | -| 8 | RNAGenesis | 0.0750 | 0.4381 | 0.0343 | 0.1825 | -| 9 | RiNALMo | -0.0243 | 0.4856 | 0.0459 | 0.1690 | -| 10 | AIDO.RNA (300M) | -0.0256 | 0.4551 | 0.0372 | 0.1556 | -| 11 | AIDO.RNA (25M) | -0.0299 | 0.4569 | 0.0348 | 0.1540 | -| 12 | Evo 1.5 | 0.0278 | 0.3850 | 0.0007 | 0.1378 | -| 13 | Nucleotide Transformer v3 (8M) | -0.0111 | 0.3053 | 0.0988 | 0.1310 | -| 14 | RNA-FM | -0.0225 | 0.4147 | -0.0043 | 0.1293 | -| 15 | Nucleotide Transformer v3 (650M) | -0.0240 | 0.3518 | 0.0436 | 0.1238 | -| 16 | Nucleotide Transformer v3 (100M) | -0.0174 | 0.2544 | 0.1001 | 0.1124 | -| 17 | AIDO.RNA (1M) | 0.0014 | 0.1777 | 0.0626 | 0.0806 | -| 18 | Orthrus | -0.0349 | 0.0922 | 0.1578 | 0.0717 | -| 19 | Evo 1 | -0.0216 | 0.0948 | 0.0058 | 0.0263 | -| 20 | GenSLM | -0.0045 | -0.0934 | -0.0036 | -0.0338 | - -The top four models span 0.0093. With only 31 assays, this difference should not be treated as a -resolved ordering. Evo 2 1B is a base-pretrained 8k-context checkpoint, while the 7B, 20B and 40B -checkpoints are mid-trained at 1M context, so it is not a like-for-like size point. - -All five released AIDO.RNA checkpoints are listed as separate entries rather than in a side table, -since they are separate models scored the same way. Their scores rise steeply to 650M and then stop: -the 650M scores above the 1.6B under `wt-fill` and `match-fill`, though not under `mask-fill` or -`mut-fill`, so the shape of that curve depends on the scoring convention and should be read with that -in mind. - -Per category columns are the mean signed Spearman over the assays in that category. Macro is the -unweighted average of the three category means. Underlying values: -[`leaderboard_signed_3ncRNA.csv`][0]. - -The masked language models other than RNA-ERNIE are scored with `wt-fill`, the convention used by -the ESM and ProteinGym reference implementations. RNA-ERNIE retains its original scorer: one -unmasked wild-type forward pass followed by a sum of raw probability differences at mutated -positions. It is a separate zero-shot method, not a masked-marginal score. Orthrus uses its official -[MLM variant-scoring interface][1]. -Its optional CDS and splice channels stay zero because the DMS assays do not provide those -annotations. - -NTv3 requires sequence lengths divisible by 128. Following the [upstream guidance][5], its inputs -are right-padded with `N` to the next valid length. - -## Scoring convention - -The masked language models are scored with the masked-marginal log-likelihood ratio: mask a mutated -position, take `log p(mutant) - log p(wild type)` there, and sum over the variant's mutated -positions. Meier et al. 2021 (ESM-1v), supplement Appendix A defines three context choices. A fourth, -closely related convention is implemented by the authors' released code. They differ in exactly one -thing: **what fills the variant's OTHER mutated positions while one position is masked.** With `M` -the mutated positions, `x_-i` a mask at `i` and `x_-M` masks at every position in `M`: - -| name | fill at the other mutated sites | score | -|:--|:--|:--| -| **`wt-fill`** | wild-type bases | `sum_i log p(mt_i \| x^wt_-i) - log p(wt_i \| x^wt_-i)` | -| `mask-fill` | masks | `sum_i log p(mt_i \| x^wt_-M) - log p(wt_i \| x^wt_-M)` | -| `mut-fill` | mutant bases | `sum_i log p(mt_i \| x^mt_-i) - log p(wt_i \| x^mt_-i)` | -| `match-fill` | the allele being scored | `sum_i log p(mt_i \| x^mt_-i) - log p(wt_i \| x^wt_-i)` | - -**The leaderboard uses `wt-fill`.** It is what the ESM authors' own -[ESM example][2] and [ProteinGym baseline][3] -implement under the option name `masked-marginals`, so it is the convention the surrounding zero-shot -literature is calibrated on. The choice is on that provenance, not on which scores best. - -All four are computed and published, see the sensitivity section below. `fitness/baselines/masked_lm` -computes them in a single pass per checkpoint: the four share most of their masked contexts, so all -four together cost about 19% more unique context examples than `mut-fill` alone (2,929,196 against -2,458,521 over the 31 assays). These are batched inputs, not model invocations. Every scoring script -accepts `--strategies`. - -Two properties worth knowing. **On single mutants all four are identical**, because a variant with -one mutation has no other mutated positions. That is used as a fixture test. And **99.4% of the ncRNA -variants are multi-mutants**, so the conventions diverge on essentially everything here. - -The name is overloaded and the overloading has caused real errors. The ESM code option called -`masked-marginals` is `wt-fill`, while the formula written in the ESM paper is `mask-fill`, and -`wt-marginals` is a different method again: one unmasked forward pass with no masking at all. - -RiNALMo and RNA-FM were also rescored to fix outright bugs, not just the convention. The earlier -RiNALMo scores looked the mutant base up in an RNA-form sequence while its alphabet is DNA, so -mutations to U scored against the unknown-token logit. The earlier RNA-FM scores match a single -unmasked wild-type pass (`wt-marginals`) rather than the masked strategy its run script requested. - -## Sensitivity to the fill strategy - -Every masked model is scored under all four conventions, so the effect of the choice is visible -rather than assumed. Macro over the 3 ncRNA categories. Regenerate this table, the per-category -values and the category spreads with `fitness/analyze_fill_strategies.py`. - -| Checkpoint | `wt-fill` | `mask-fill` | `mut-fill` | `match-fill` | -|:--|--:|--:|--:|--:| -| AIDO.RNA-650M | 0.2163 | 0.1993 | 0.1767 | 0.2017 | -| AIDO.RNA-1.6B | 0.2070 | 0.2010 | 0.1824 | 0.1989 | -| RNAGenesis | 0.1825 | 0.1802 | 0.1497 | 0.1760 | -| RiNALMo | 0.1690 | 0.1643 | 0.1391 | 0.1611 | -| AIDO.RNA-300M-MARS | 0.1556 | 0.1514 | 0.1369 | 0.1539 | -| AIDO.RNA-25M-MARS | 0.1540 | 0.1380 | 0.1026 | 0.1393 | -| RNA-FM | 0.1293 | 0.1155 | 0.1014 | 0.1267 | -| AIDO.RNA-1M-MARS | 0.0806 | 0.0904 | 0.0800 | 0.0855 | -| Orthrus | 0.0717 | 0.0589 | 0.0384 | 0.0356 | - -`wt-fill` has the highest observed macro on 8 of the 9 checkpoints, and it is the convention the -leaderboard uses. The table above is published so the choice can be checked rather than taken on -trust. - -Two things to keep in mind when reading it: - -- **The differences are concentrated in tRNA.** Mean spread across the four strategies is 0.094 in - tRNA against 0.021 in ribozyme and 0.007 in aptamer, because the conventions are identical on - single mutants and the tRNA assays are the deepest (4.07 mutations per variant against 2.91 and - 1.68). Since the macro weights 3 tRNA assays as heavily as 26 ribozyme assays, that effect is - amplified: under an unweighted mean over all 31 assays the ordering is much less clear cut. -- **Model ranking is far more stable than the absolute numbers.** Across the nine checkpoints the - only ordering change between strategies is that AIDO.RNA-650M and AIDO.RNA-1.6B trade places. - -## Why we now exclude the mRNA assays - -We restrict evaluation to assays whose DMS readout is a direct measure of the RNA molecule's own -function (ribozyme cleavage, tRNA function, aptamer binding). - -1. mRNA-coding (37 assays): the readout is protein function rather than mRNA fitness. -2. mRNA-splicing (2 assays, `Julien_2016_mRNA` and `Ke_2017_mRNA`): the readout is exon or alternative splicing efficiency in human cells, a regulatory phenotype rather than the molecular fitness of the mRNA itself. - -Signed Spearman on the excluded assays is kept for reference in -[`mRNA/leaderboard_signed_mRNA.csv`][4]. Those numbers predate the -RiNALMo and RNA-FM rescoring, so their rows there still use the earlier scoring, and RiNALMo's -position in that table in particular should not be read as current. - -## Notes - -Categories: ribozyme (26 assays), tRNA (3), aptamer (2). Because the macro-mean weights each category -equally, the small tRNA and aptamer sets carry outsized, higher-variance weight. Treat differences of -a few hundredths as unresolved rather than as an ordering. - -## Scoring scripts - -Scoring scripts, paths relative to the repository root: -- Evo 2: `fitness/baselines/Evo/score_evo2_single_dms.py` and `score_evo2.sh` -- Nucleotide Transformer v3: `fitness/baselines/Nucleotide_Transformer/compute_fitness.py` and `run.sh` -- Orthrus: `fitness/baselines/Orthrus/score_orthrus_single_dms.py` and `score_orthrus.sh` -- AIDO.RNA: `fitness/baselines/AIDO_RNA/score_aido_rna_single_dms.py` and `score_aido_rna.sh` -- RNAGenesis: `fitness/baselines/RNAGenesis/score_rnagenesis_single_dms.py` and `score_rnagenesis.sh` -- RNA-FM: `fitness/baselines/RNA_FM/score_rna_fm_single_dms.py` and `score_rna_fm.sh` -- RiNALMo: `fitness/baselines/RiNALMo/score_rinalmo_single_dms.py` and `score_rinalmo.sh` - -The six masked model adapters share one scoring engine, `fitness/baselines/masked_lm`, which implements the -four fill strategies, the context deduplication and the batching. Each model's script is a thin -adapter supplying its alphabet, tokenization and forward pass. `tests/test_fitness.py` covers the -checkpoint-free scoring, merge and analysis workflow on fixtures from real assays. - -All are registered in `fitness/model_registry.py`. - -Reproduce the aggregate with `performance_fitness.py --type ncRNA`, whose Spearman is now signed. -It previously reported the absolute value, which credited a model whose scores anti-correlate with -fitness exactly as much as one that correlates, so it could not produce the numbers this page -publishes. Its per-category means now match the columns above, and the macro is their unweighted -mean. AUC and MCC are directed too: an AUC below 0.5 or an MCC below 0 means the model ranks variants -the wrong way round, where both were previously folded onto their better side. - -`fitness/analyze_fill_strategies.py` regenerates the sensitivity table and category spreads from the -prediction files. - -Scores computed in bfloat16 depend on the GPU: the same code and checkpoint on an L40S and an H100 -differ by up to 0.2 in score and about 0.001 in per-assay Spearman. Every prediction file is written -with a manifest recording the strategies, alphabet, model arguments and hardware. +| 1 | EVmutation* | 0.2441 | 0.4602 | 0.1371 | 0.2805 | +| 2 | AIDO.RNA (650M) | 0.0660 | 0.4894 | 0.0934 | 0.2163 | +| 3 | Evo 2 (20B) | 0.1105 | 0.4345 | 0.0937 | 0.2129 | +| 4 | Evo 2 (40B) | 0.1081 | 0.4310 | 0.0970 | 0.2120 | +| 5 | AIDO.RNA (1.6B) | 0.0609 | 0.4884 | 0.0718 | 0.2070 | +| 6 | Evo 2 (1B base) | 0.0087 | 0.4466 | 0.1334 | 0.1962 | +| 7 | RNA-ERNIE | 0.1343 | 0.4161 | 0.0306 | 0.1937 | +| 8 | Evo 2 (7B) | 0.0651 | 0.3867 | 0.1192 | 0.1904 | +| 9 | RNAGenesis | 0.0750 | 0.4381 | 0.0343 | 0.1825 | +| 10 | RiNALMo | -0.0243 | 0.4856 | 0.0459 | 0.1690 | +| 11 | AIDO.RNA (300M) | -0.0256 | 0.4551 | 0.0372 | 0.1556 | +| 12 | AIDO.RNA (25M) | -0.0299 | 0.4569 | 0.0348 | 0.1540 | +| 13 | Evo 1.5 | 0.0291 | 0.3849 | -0.0010 | 0.1377 | +| 14 | Nucleotide Transformer v3 (8M) | -0.0111 | 0.3053 | 0.0988 | 0.1310 | +| 15 | RNA-FM | -0.0225 | 0.4147 | -0.0043 | 0.1293 | +| 16 | Nucleotide Transformer v3 (650M) | -0.0240 | 0.3518 | 0.0436 | 0.1238 | +| 17 | Nucleotide Transformer v3 (100M) | -0.0174 | 0.2544 | 0.1001 | 0.1124 | +| 18 | AIDO.RNA (1M) | 0.0014 | 0.1777 | 0.0626 | 0.0806 | +| 19 | Orthrus | -0.0349 | 0.0922 | 0.1578 | 0.0717 | +| 20 | GenSLM (2.5B) | -0.0134 | 0.1002 | 0.0620 | 0.0496 | +| 21 | Evo 1 | -0.0275 | 0.0989 | 0.0300 | 0.0338 | + +\* EVmutation scores 29/31 assays and 510,230/856,628 variants (59.6%). Its row uses those variants. [Coverage by assay][5]. [Compare all models on those variants][6]. + + +[Full precision CSV][0]. Small differences should be read cautiously, especially +with only three tRNA and two aptamer assays. Coding and splicing assays are +excluded because they measure protein function or splicing efficiency. + +## Scoring + +Masked language models use `wt-fill`: mask each mutated position in the wild-type +sequence and sum `log p(mutant) - log p(wild type)`. This follows the [ESM][1] +and [ProteinGym][2] implementations. All four [fill strategies][3] are available +in the prediction files. RNA-ERNIE uses unmasked wild-type probability changes. +GenSLM uses mean next-codon log likelihood, excluding padding. + +## Reproduce + +Follow the [fitness setup][4], then run from `rnagym/fitness/`: + +```bash +pixi run merge +pixi run leaderboard +pixi run --locked -e default check-published +``` + +`pixi run analyze-fill-strategies` compares the four scoring conventions. [0]: leaderboard_signed_3ncRNA.csv -[1]: https://huggingface.co/antichronology/orthrus-mlm-6-track/blob/5f0dc87d51065035fc28e71972c69f9c84f4deae/orthrus_hf.py#L290-L325 -[2]: https://github.com/facebookresearch/esm/blob/2b369911bb5b4b0dda914521b9475cad1656b2ac/examples/variant-prediction/predict.py#L186-L225 -[3]: https://github.com/OATML-Markslab/ProteinGym/blob/144fe22b07dfaeec2b366f2346203a9838a55b4c/proteingym/baselines/esm/compute_fitness.py#L486-L514 -[4]: mRNA/leaderboard_signed_mRNA.csv -[5]: https://github.com/instadeepai/nucleotide-transformer/blob/2dc37b86e16a6970fbc731751f7719d9f676f7f9/docs/nucleotide_transformer_v3.md#L255-L260 +[1]: https://github.com/facebookresearch/esm/blob/2b369911bb5b4b0dda914521b9475cad1656b2ac/examples/variant-prediction/predict.py#L186-L225 +[2]: https://github.com/OATML-Markslab/ProteinGym/blob/144fe22b07dfaeec2b366f2346203a9838a55b4c/proteingym/baselines/esm/compute_fitness.py#L486-L514 +[3]: ../../rnagym/fitness/baselines/masked_lm/README.md +[4]: ../../rnagym/fitness/README.md +[5]: evmutation_coverage.csv +[6]: leaderboard_evmutation.csv diff --git a/leaderboard/fitness/evmutation_coverage.csv b/leaderboard/fitness/evmutation_coverage.csv new file mode 100644 index 0000000..ae8f6e7 --- /dev/null +++ b/leaderboard/fitness/evmutation_coverage.csv @@ -0,0 +1,32 @@ +DMS_ID,RNA_TYPE,variants,scored_variants,evaluated_variants +Andreasson_2020_ribozyme,Ribozyme,7342,6822,6822 +Beck_2022_ribozyme,Ribozyme,21321,17205,17205 +Domingo_2018_tRNA,tRNA,4175,4175,4175 +Guy_2014_tRNA,tRNA,25491,22510,22510 +Janzen_2022_fam1a1_ribozyme,Ribozyme,1953,1953,1953 +Janzen_2022_fam1b1_ribozyme,Ribozyme,1953,1953,1953 +Janzen_2022_fam21_ribozyme,Ribozyme,1953,1596,1596 +Janzen_2022_fam22_ribozyme,Ribozyme,1953,1953,1953 +Janzen_2022_fam31_ribozyme,Ribozyme,1953,1953,1953 +Kobori_2015_ribozyme_j12,Ribozyme,255,255,255 +Kobori_2015_ribozyme_p4,Ribozyme,255,255,255 +Kobori_2015_ribozyme_tw,Ribozyme,1023,1023,1023 +Kobori_2018_ribozyme,Ribozyme,16383,63,63 +Li_2016_tRNA,tRNA,65536,65536,65536 +Peri_2022_ribozyme,Ribozyme,16383,1023,1023 +Pitt_2010_ribozyme,Ribozyme,186,99,99 +Roberts_2023_cepeb3_ribozyme,Ribozyme,21321,17205,17205 +Roberts_2023_HDV_ribozyme,Ribozyme,33930,33153,33153 +Roberts_2023_hh_ribozyme,Ribozyme,9045,9045,9045 +Roberts_2023_hp_ribozyme,Ribozyme,22578,11628,11628 +Roberts_2023_tw_ribozyme,Ribozyme,10296,6786,6786 +Soo_2021_ribozyme,Ribozyme,63430,0,0 +Tome_2014_GFP_aptamer,Aptamer,417,0,0 +Tome_2014_NELFE_aptamer,Aptamer,2652,1817,1817 +Zhang_2020_cpeb3_ribozyme,Ribozyme,111417,40712,40712 +Zhang_2024_line1_mini_ribozyme,Ribozyme,149710,135953,135953 +Zhang_2024_line1_full_ribozyme,Ribozyme,69583,23499,23499 +Zhang_2024_OR4K15_ribozyme,Ribozyme,61393,57209,57209 +McRae_2024_5tu_ribozyme,Ribozyme,74942,22235,22235 +McRae_2024_t1_ribozyme,Ribozyme,47503,14739,14739 +Kobori_2016_osa_ribozyme,Ribozyme,10296,7875,7875 diff --git a/leaderboard/fitness/leaderboard_evmutation.csv b/leaderboard/fitness/leaderboard_evmutation.csv new file mode 100644 index 0000000..f591abf --- /dev/null +++ b/leaderboard/fitness/leaderboard_evmutation.csv @@ -0,0 +1,22 @@ +model,Ribozyme,tRNA,Aptamer,macro_3ncRNA +EVmutation,0.2440755336251732,0.46023832391174485,0.1370765459660463,0.28046346783432147 +RNAErnie,0.12818127963560078,0.4147490499064001,0.17841523714364266,0.24044852222854785 +aido_rna,0.0707075715800152,0.490143040185095,0.021408233136557295,0.19408628163388916 +evo2_20b,0.1266620626732742,0.4344699577324014,0.02109268383880974,0.19407490141482844 +aido_rna_650m,0.0662907234734648,0.4915142363579288,0.018471452942482022,0.19209213759129184 +evo2_40b,0.12118133659602001,0.430921954473341,0.022139428871089245,0.19141423998015009 +rinalmo,0.005633834822653851,0.48881102863810694,0.03986487041732329,0.17810324462602803 +evo2,0.07641837297844005,0.3864270363074905,-0.012984068450590884,0.14995378027844655 +evo2_1b_base,0.022121896277985754,0.44783974896911266,-0.022624326220476228,0.14911243967554075 +rnagenesis,0.0977265438536851,0.4397098879350387,-0.09127304225558788,0.1487211298443786 +RNA-FM,-0.03916185185981722,0.4151848844981643,0.06966330278279369,0.14856211180704693 +aido_rna_300m,-0.030946390661983392,0.4563127192851651,-0.02955625356620814,0.13193669168565786 +aido_rna_25m,-0.041594931551018934,0.4565081044209765,-0.0277660373930302,0.12904904515897578 +evo1.5,0.008261241359846672,0.38683369401933826,-0.031019982919294364,0.12135831748663019 +ntv3_650m,-0.03125915670520608,0.3536618369717301,0.009273179928343422,0.11055862006495581 +ntv3_8m,-0.011557796012157959,0.30711066438466594,0.00400169888673593,0.09985152241974797 +ntv3_100m,-0.006405987950380707,0.25521797235350796,-0.020686896636198913,0.07604169592230944 +GenSLM,-0.02456166560766989,0.10261689783868821,0.13141154562243548,0.0698222592844846 +aido_rna_1m,-0.006038301332977887,0.18051534648407533,-0.037934875774833746,0.04551405645875456 +orthrus,-0.028663958154675696,0.09191845565698502,0.024730010768467058,0.029328169423592124 +evo1,-0.01954101436772075,0.09744314077754458,-0.0040302035641527414,0.02462397428189036 diff --git a/leaderboard/fitness/leaderboard_signed_3ncRNA.csv b/leaderboard/fitness/leaderboard_signed_3ncRNA.csv index 584af80..1936f24 100644 --- a/leaderboard/fitness/leaderboard_signed_3ncRNA.csv +++ b/leaderboard/fitness/leaderboard_signed_3ncRNA.csv @@ -1,21 +1,22 @@ model,Ribozyme,tRNA,Aptamer,macro_3ncRNA -aido_rna_650m,0.0660332868987325,0.4893565654410639,0.0934325517551516,0.216274134698316 -evo2_20b,0.1104601596335257,0.4345270738070895,0.0937093504359844,0.2128988612921999 -evo2_40b,0.1081020224381099,0.4309848333293184,0.09695482733614616,0.21201389436785814 -aido_rna,0.0608547145460644,0.4883787106096389,0.0718036455462622,0.2070123569006552 -evo2_1b_base,0.0086596951000284,0.4466199808506075,0.1334226383526211,0.1962341047677523 -RNAErnie,0.13427241327809655,0.416115138737617,0.030641733377440288,0.1936764284643846 +EVmutation,0.2440755336251732,0.46023832391174485,0.1370765459660463,0.28046346783432147 +aido_rna_650m,0.06603328689873253,0.48935656544106393,0.09343255175515162,0.21627413469831602 +evo2_20b,0.11046015963352573,0.4345270738070895,0.09370935043598444,0.2128988612921999 +evo2_40b,0.10810202243810993,0.4309848333293184,0.09695482733614616,0.2120138943678582 +aido_rna,0.06085471454606447,0.4883787106096389,0.07180364554626228,0.20701235690065523 +evo2_1b_base,0.008659695100028446,0.4466199808506075,0.1334226383526211,0.19623410476775235 +RNAErnie,0.13427241327809658,0.416115138737617,0.030641733377440288,0.19367642846438463 evo2,0.06511905079221095,0.3867345488695757,0.11920502912796216,0.1903528762632496 -rnagenesis,0.0750044159987944,0.4380555746849195,0.0343114577905016,0.1824571494914051 -rinalmo,-0.0243482728829925,0.4855667318003715,0.0458576152000779,0.1690253580391523 -aido_rna_300m,-0.0255811990086168,0.4550919102078445,0.0371789292494538,0.1555632134828938 -aido_rna_25m,-0.0298765175467791,0.456947919154572,0.0347915567069773,0.1539543194382567 -evo1.5,0.02777228452283577,0.384997183239441,0.0007072149253433031,0.13782556089587336 -ntv3_8m,-0.011143627794088702,0.3052878533730738,0.09879528686142668,0.13097983748013725 -RNA-FM,-0.0224760788032156,0.4146672587305333,-0.0042742403435094,0.129305646527936 -ntv3_650m,-0.02400801845378366,0.35176296869145113,0.04360222670762418,0.12378572564843056 -ntv3_100m,-0.017411307436819852,0.2544237962973999,0.1001368736984453,0.11238312085300846 -aido_rna_1m,0.0013833725774681,0.1777039407912451,0.0625990558827026,0.0805621230838053 -orthrus,-0.034851722441808085,0.09222411475539294,0.1577698751166801,0.07171408914342164 -evo1,-0.021640697016188013,0.09484040594853649,0.0057677958977196755,0.026322501610022715 -GenSLM,-0.004542860367691017,-0.09337311344747723,-0.0036277512402647302,-0.033847908351810986 +rnagenesis,0.07500441599879443,0.4380555746849195,0.034311457790501626,0.1824571494914052 +rinalmo,-0.024348272882992513,0.4855667318003715,0.04585761520007796,0.16902535803915233 +aido_rna_300m,-0.025581199008616898,0.4550919102078445,0.03717892924945382,0.15556321348289381 +aido_rna_25m,-0.029876517546779165,0.45694791915457206,0.034791556706977395,0.15395431943825674 +evo1.5,0.029124344498996584,0.3848767184626376,-0.0010420422565921537,0.13765300690168067 +ntv3_8m,-0.011143627794088697,0.30528785337307374,0.09879528686142668,0.13097983748013725 +RNA-FM,-0.022476078803215633,0.4146672587305333,-0.004274240343509472,0.12930564652793605 +ntv3_650m,-0.024008018453783656,0.35176296869145113,0.04360222670762418,0.12378572564843056 +ntv3_100m,-0.017411307436819855,0.2544237962973999,0.1001368736984453,0.11238312085300846 +aido_rna_1m,0.0013833725774681483,0.17770394079124516,0.0625990558827026,0.08056212308380531 +orthrus,-0.03485172244180809,0.09222411475539292,0.1577698751166801,0.07171408914342163 +GenSLM,-0.013394211604770691,0.10020974930877817,0.06204427969003089,0.04961993913134612 +evo1,-0.02753926367420387,0.09885488335511416,0.029950014386063754,0.03375521135565802 diff --git a/pyproject.toml b/pyproject.toml index a9cdc1b..7faa0c5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -8,8 +8,12 @@ version = "0.2.0" requires-python = ">=3.9" [tool.setuptools] -packages = [ +include-package-data = false + +[tool.setuptools.packages.find] +include = [ "rnagym", + "rnagym.fitness*", "rnagym.s2d", "rnagym.s2d.models", "rnagym.s2d.tasks", @@ -21,5 +25,47 @@ packages = [ "rnagym.tasks", ] +[tool.setuptools.package-data] +"rnagym.fitness" = [ + "README.md", + "baselines/*/*.sh", + "baselines/masked_lm/README.md", + "pixi.lock", + "pixi.toml", + "sh/*.sh", +] +"rnagym.fitness.baselines.RNAERNIE.src" = ["*.json", "*.txt"] + [tool.distutils.egg_info] egg-base = "rnagym/.egg" + +[tool.ruff.lint.isort] +known-first-party = ["rnagym"] + +[tool.coverage.run] +parallel = true +patch = ["subprocess"] +source = ["rnagym.fitness"] + +[tool.pyrefly] +preset = "strict" +project-includes = ["rnagym/fitness"] +python-interpreter-path = "rnagym/fitness/.pixi/envs/default/bin/python" +# Model libraries live in separate Pixi environments and are tested by reproduction +ignore-missing-imports = [ + "evcouplings.*", + "evo.*", + "evo2", + "flash_attn.*", + "fm", + "genslm", + "huggingface_hub", + "modelgenerator.*", + "paddle", + "paddlenlp.*", + "pandas", + "rinalmo.*", + "safetensors.*", + "stripedhyena.*", + "transformers", +] diff --git a/rnagym/config.py b/rnagym/config.py index 6aa83e3..916e2dc 100644 --- a/rnagym/config.py +++ b/rnagym/config.py @@ -4,7 +4,7 @@ from pathlib import Path _REPO_DIR = Path(__file__).resolve().parents[1] -_DATA_DIR = _REPO_DIR / "data" +_DATA_DIR = Path(os.environ.get("RNAGYM_DATA_DIR", _REPO_DIR / "data")) _DATABASE_DIR = Path( os.environ.get("RNAGYM_DATABASE_DIR", "/n/lw_groups/marks/databases") ) @@ -42,7 +42,6 @@ class Config2D(_Config): EFOLD_VIRAL_FILE = RAW_DIR / "viral_fragments.json" PSEUDOBASE_FILE = RAW_DIR / "pseudobase.csv" MODEL_SOURCE_DIR = DIR / ".pixi" / "model-sources" - MODEL_WEIGHT_DIR = DIR / ".pixi" / "model-weights" MC_ANNOTATE = MODEL_SOURCE_DIR / "RNA_assessment" / "MC-Annotate" MIN_SEQUENCE_IDENTITY = 0.40 @@ -69,10 +68,15 @@ class Config2D(_Config): class ConfigFitness(_Config): """Fitness benchmark configuration.""" - DIR = _REPO_DIR / "fitness" + DIR = _REPO_DIR / "rnagym" / "fitness" + LEADERBOARD_DIR = _REPO_DIR / "leaderboard" / "fitness" DATA_DIR = _DATA_DIR / "fitness" + ASSAY_DIR = DATA_DIR / "assays" + COMBINED_DIR = DATA_DIR / "merged" MSA_DIR = DATA_DIR / "msa" - REFERENCE_FILE = DIR / "reference_sheet_final.csv" + PREDICTION_DIR = DATA_DIR / "model_predictions" + REFERENCE_FILE = DATA_DIR / "reference_sheet_final.csv" + REPORT_DIR = DATA_DIR / "reports" class ConfigRiboseek(_Config): diff --git a/rnagym/fitness/README.md b/rnagym/fitness/README.md new file mode 100644 index 0000000..ecd7082 --- /dev/null +++ b/rnagym/fitness/README.md @@ -0,0 +1,90 @@ +# Fitness benchmark + +RNA fitness predictions evaluated against 70 experimental assays. The +[leaderboard][0] uses signed Spearman correlation on 31 ncRNA assays, averaged +within ribozyme, tRNA and aptamer categories, then equally across categories. + +## Setup + +Complete the [shared data setup][1], then run from the repository root: + +```bash +cd rnagym/fitness +pixi install +``` + +Fitness data lives under `data/fitness/`. + +## Run + +From `rnagym/fitness/`: + +```bash +pixi run -e predict +``` + +Replace `` with a model environment from [pixi.toml][2]: + +| Environment | Model | +| --- | --- | +| `aido-rna` | AIDO.RNA | +| `evmutation` | EVmutation | +| `evo` | Evo 1 / 1.5 | +| `evo2` | Evo 2 | +| `genslm` | GenSLM | +| `ntv3` | Nucleotide Transformer v3 | +| `orthrus` | Orthrus | +| `rna-ernie` | RNA-ERNIE | +| `rna-fm` | RNA-FM | +| `rnagenesis` | RNAGenesis | +| `rinalmo` | RiNALMo | + +`predict` defaults to all assays. To select reference-sheet rows, append an +index or range, for example `predict 12` or `predict 0-8,12`. Slurm array jobs +use `SLURM_ARRAY_TASK_ID` unless a selection is passed explicitly. + +To evaluate saved predictions: + +```bash +pixi run merge +pixi run leaderboard +``` + +`leaderboard` writes both ncRNA tables under `leaderboard/fitness/`. The +supplemental comparison uses the variants scored by EVmutation. Repeated +experimental measurements are retained. Incomplete or conflicting predictions +fail the merge. + +Sources and weights are fetched automatically, except for [GenSLM 2.5B][3]. +Download its checkpoint through authenticated Globus access to +`$RNAGYM_CHECKPOINT_DIR/genslm/2.5B/`. EVmutation uses Riboseek MSAs under +`data/fitness/msa/by_assay/` and leaves uncovered variants unscored. + +GenSLM reports mean next-codon log likelihood, excluding padding. Masked models +compute all four [fill strategies][4], with `wt-fill` used for the leaderboard. + +## Quality checks + +```bash +pixi run test +pixi run lint +pixi run --locked -e default check-published +``` + +The fast tests use real assay fixtures and need no checkpoints. The full check +rebuilds both tables and scores 128 variants from each of the 31 ncRNA assays +with all 20 checkpoints. Score ranks must have Spearman correlation of at least +0.95 with the stored predictions. Install environments and weights first. +The full run has a one-hour limit and needs two 80 GB GPUs for Evo2 40B. + +Use `pixi run -e check-published` to check one family, or add +`--leaderboard-only` to the default command to check aggregates. + +Run `pixi run coverage` before merging. It runs the tests and full reproduction +and requires 80% coverage. + +[0]: ../../leaderboard/fitness/ +[1]: ../../README.md#getting-started +[2]: pixi.toml +[3]: https://g-e71d1.fd635.8443.data.globus.org/models/2.5B/patric_2.5b_epoch00_val_los_0.29_bias_removed.pt +[4]: baselines/masked_lm/README.md diff --git a/fitness/__init__.py b/rnagym/fitness/__init__.py similarity index 100% rename from fitness/__init__.py rename to rnagym/fitness/__init__.py diff --git a/rnagym/fitness/baselines/AIDO_RNA/__init__.py b/rnagym/fitness/baselines/AIDO_RNA/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/rnagym/fitness/baselines/AIDO_RNA/score_aido_rna.sh b/rnagym/fitness/baselines/AIDO_RNA/score_aido_rna.sh new file mode 100644 index 0000000..5cdd475 --- /dev/null +++ b/rnagym/fitness/baselines/AIDO_RNA/score_aido_rna.sh @@ -0,0 +1,20 @@ +#!/usr/bin/env bash +set -euo pipefail +umask 002 + +checkpoint_root=${RNAGYM_CHECKPOINT_DIR:-/n/lw_groups/marks/ckpt} +export HF_HUB_CACHE=${HF_HUB_CACHE:-"$checkpoint_root/aido-rna/hub"} + +script_dir=$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd) +repository=$(cd -- "$script_dir/../../../.." && pwd) +data_dir=${RNAGYM_DATA_DIR:-"$repository/data"}/fitness + +model_name=${AIDO_RNA_MODEL_NAME:-genbio-ai/AIDO.RNA-1.6B} +prediction_name=${AIDO_RNA_PREDICTION_NAME:-aido_rna_4fill} +rows=${1:-${SLURM_ARRAY_TASK_ID:-all}} + +cd "$repository" +python -m rnagym.fitness.baselines.AIDO_RNA.score_aido_rna_single_dms \ + --rows "$rows" \ + --output "$data_dir/model_predictions/$prediction_name" \ + --model "$model_name" diff --git a/fitness/baselines/AIDO_RNA/score_aido_rna_single_dms.py b/rnagym/fitness/baselines/AIDO_RNA/score_aido_rna_single_dms.py similarity index 57% rename from fitness/baselines/AIDO_RNA/score_aido_rna_single_dms.py rename to rnagym/fitness/baselines/AIDO_RNA/score_aido_rna_single_dms.py index efb6c88..cbb2dd7 100644 --- a/fitness/baselines/AIDO_RNA/score_aido_rna_single_dms.py +++ b/rnagym/fitness/baselines/AIDO_RNA/score_aido_rna_single_dms.py @@ -5,15 +5,14 @@ DNA alphabet. """ -import os -import sys +import argparse from pathlib import Path import torch +from typing_extensions import override -sys.path.insert(0, str(Path(__file__).resolve().parents[1])) - -from masked_lm import MaskedLMAdapter, main # noqa: E402 +from rnagym.fitness.baselines.masked_lm import MaskedLMAdapter, main +from rnagym.fitness.tasks.model_registry import checkpoint_revision class AIDORNAAdapter(MaskedLMAdapter): @@ -26,40 +25,36 @@ class AIDORNAAdapter(MaskedLMAdapter): default_max_batch_tokens = 49152 @staticmethod - def add_arguments(parser): + @override + def add_arguments(parser: argparse.ArgumentParser) -> None: parser.add_argument( - "--model_name", + "--model", + dest="model_name", type=str, default="genbio-ai/AIDO.RNA-1.6B", - help="HuggingFace model id or local path of the AIDO.RNA checkpoint " - "(default: genbio-ai/AIDO.RNA-1.6B)", - ) - parser.add_argument( - "--dtype", - type=str, - default="bfloat16", - choices=["bfloat16", "float32"], - help="Torch dtype for the model weights. bfloat16 is the default and " - "is what the released predictions were produced with. fp32 changed " - "the aptamer Spearman by at most 0.0006 when it was checked", + help="Checkpoint ID or local path", ) - def load(self, args): + @override + def load(self, args: argparse.Namespace) -> None: + # The tokenizer vocabulary ships with modelgenerator + import modelgenerator from modelgenerator.huggingface_models.rnabert import ( RNABertForMaskedLM, RNABertTokenizer, ) - # The tokenizer vocabulary ships with modelgenerator - vocab_file = os.path.join( - os.path.dirname(__import__("modelgenerator").__file__), - "huggingface_models", - "rnabert", - "vocab.txt", + if modelgenerator.__file__ is None: + raise ImportError("modelgenerator has no package location") + vocab_file = ( + Path(modelgenerator.__file__).parent + / "huggingface_models/rnabert/vocab.txt" ) - tokenizer = RNABertTokenizer(vocab_file, version="v2") + tokenizer = RNABertTokenizer(str(vocab_file), version="v2") self.model = RNABertForMaskedLM.from_pretrained( - args.model_name, torch_dtype=getattr(torch, args.dtype) + args.model_name, + revision=checkpoint_revision(args.model_name), + torch_dtype=torch.bfloat16, ) self.model = self.model.to(args.device).eval() self.device = args.device @@ -71,7 +66,14 @@ def load(self, args): self.prefix_ids = [tokenizer.cls_token_id] self.suffix_ids = [tokenizer.sep_token_id] - def logits_at(self, input_ids, attention_mask, rows, cols): + @override + def logits_at( + self, + input_ids: torch.Tensor, + attention_mask: torch.Tensor, + rows: torch.Tensor, + cols: torch.Tensor, + ) -> torch.Tensor: outputs = self.model(input_ids=input_ids, attention_mask=attention_mask) return outputs.logits[rows, cols] diff --git a/fitness/baselines/EVmutation/.gitignore b/rnagym/fitness/baselines/EVmutation/.gitignore similarity index 100% rename from fitness/baselines/EVmutation/.gitignore rename to rnagym/fitness/baselines/EVmutation/.gitignore diff --git a/rnagym/fitness/baselines/EVmutation/__init__.py b/rnagym/fitness/baselines/EVmutation/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/rnagym/fitness/baselines/EVmutation/compute_fitness.py b/rnagym/fitness/baselines/EVmutation/compute_fitness.py new file mode 100755 index 0000000..6a0eed1 --- /dev/null +++ b/rnagym/fitness/baselines/EVmutation/compute_fitness.py @@ -0,0 +1,356 @@ +"""Score fitness assays with EVmutation and their released Riboseek MSAs.""" + +from __future__ import annotations + +import argparse +import re +import tempfile +from pathlib import Path +from typing import Any + +import numpy as np +import pandas as pd +import polars as pl +from evcouplings.couplings import CouplingsModel +from evcouplings.mutate import extract_mutations, predict_mutation_table +from evcouplings.utils import BailoutException +from evcouplings.utils.pipeline import execute + +from rnagym.config import ConfigFitness +from rnagym.fitness.data import read_reference, write_csv_atomically + +MUTATION_PATTERN = re.compile(r"^[ACGU]\d+[ACGU](?:,[ACGU]\d+[ACGU])*$") +RNA_TO_DNA = str.maketrans("Uu", "Tt") +SCORE_COLUMN = "prediction_epistatic" + + +def build_config( + name: str, + alignment_file: Path, + query_file: Path, + sequence_id: str, + work_dir: Path, + cpu_count: int, +) -> dict[str, Any]: + """Build the EVcouplings existing-alignment pipeline configuration.""" + return { + "stages": ["align", "couplings"], + "pipeline": "protein_monomer", + "global": { + "alphabet": "dna", + "prefix": str(work_dir / name), + "theta": 0.9, + "cpu": cpu_count, + "region": None, + "sequence_id": sequence_id, + "sequence_file": str(query_file), + }, + "align": { + "alphabet": "dna", + "protocol": "existing", + "input_alignment": str(alignment_file), + "first_index": 1, + "sequence_id": sequence_id, + "seqid_filter": None, + "focus_sequence": sequence_id, + "compute_num_effective_seqs": True, + "minimum_sequence_coverage": 50, + "minimum_column_coverage": 70, + "extract_annotation": True, + "sequence_weights": "nogaps", + }, + "couplings": { + "protocol": "standard", + "iterations": "100", + "alphabet": "dna", + "ignore_gaps": True, + "lambda_J": 0.01, + "lambda_J_times_Lq": True, + "lambda_h": 0.01, + "lambda_group": None, + "scale_clusters": None, + "reuse_ecs": False, + "min_sequence_distance": 4, + "scoring_model": "logistic_regression", + "save_model": True, + }, + "tools": { + "jackhmmer": "jackhmmer", + "plmc": "plmc", + "hmmbuild": "hmmbuild", + "hmmsearch": "hmmsearch", + "hhfilter": "hhfilter", + "psipred": "psipred", + "cns": "cns", + "maxcluster": "maxcluster64bit", + "usalign": "USalign", + }, + "databases": { + "uniprot": "", + "uniref100": "", + "uniref90": "", + "uniref50": "", + "sequence_download_url": "", + "sifts_mapping_table": "", + "sifts_sequence_db": "", + }, + } + + +def first_fasta_record(path: Path) -> tuple[str, str]: + """Read the identifier and sequence from the first FASTA record.""" + identifier = None + sequence = [] + with path.open() as handle: + for line in handle: + line = line.strip() + if not line: + continue + if line.startswith(">"): + if identifier is not None: + break + identifier = line[1:].split(maxsplit=1)[0] + elif identifier is None: + raise ValueError(f"{path} has sequence data before its first header") + else: + sequence.append(line) + if identifier is None or not sequence: + raise ValueError(f"{path} has no FASTA records") + return identifier, "".join(sequence) + + +def infer_model(config: dict[str, Any]) -> CouplingsModel: + """Fit DNA couplings with the alignment's coverage mask and query coordinates.""" + alignment = execute(**(config | {"stages": ["align"]})) + positions = prepare_dna_alignment( + Path(alignment["alignment_file"]), alignment["focus_sequence"] + ) + # Preserve upstream (L - 1)(q - 1) scaling after replacing masked bases with gaps + couplings = config["couplings"] | { + "lambda_J": config["couplings"]["lambda_J"] * (len(positions) - 1) * 3, + "lambda_J_times_Lq": False, + } + output = execute(**(config | {"stages": ["couplings"], "couplings": couplings})) + model = CouplingsModel(output["model_file"]) + if not np.array_equal(model.index_list, positions): + raise ValueError("PLMC positions disagree with the alignment coverage mask") + return model + + +def main() -> None: + """Score selected reference-sheet assays with released Riboseek MSAs.""" + args = parse_args() + msa_dir = ConfigFitness.MSA_DIR / "by_assay" + work_dir = ConfigFitness.DATA_DIR / "tmp/evmutation" + if args.cpu < 1: + raise ValueError("--cpu must be positive") + if not msa_dir.is_dir(): + raise FileNotFoundError(f"Riboseek MSA directory is missing: {msa_dir}") + + reference = read_reference(ConfigFitness.REFERENCE_FILE, args.rows) + args.output.mkdir(parents=True, exist_ok=True) + work_dir.mkdir(parents=True, exist_ok=True) + completed = 0 + rejected = 0 + for name, raw_sequence in reference.select( + "DMS_ID", "RAW_CONSTRUCT_SEQ" + ).iter_rows(): + alignment_file = msa_dir / f"{name}.a3m" + query_file = msa_dir / f"{name}.fa" + if not alignment_file.is_file() and not query_file.is_file(): + continue + if not alignment_file.is_file() or not query_file.is_file(): + raise FileNotFoundError(f"Incomplete Riboseek MSA files for {name}") + assay_file = ConfigFitness.ASSAY_DIR / f"{name}.csv" + if not assay_file.is_file(): + raise FileNotFoundError(f"Fitness assay is missing: {assay_file}") + output_file = args.output / assay_file.name + if output_file.exists(): + continue + if raw_sequence is None: + raise ValueError(f"{name} has no reference sequence") + + print(f"Scoring {name}", flush=True) + try: + with tempfile.TemporaryDirectory(prefix=f"{name}-", dir=work_dir) as path: + result = score_assay( + assay_file, + alignment_file, + query_file, + normalize_sequence(raw_sequence), + name, + Path(path), + args.cpu, + ) + except BailoutException as error: + print(f"Skipped {name}: {error}", flush=True) + rejected += 1 + continue + write_csv_atomically(result, output_file) + completed += 1 + print(f"Scored {completed} assay(s), EVcouplings rejected {rejected}") + + +def normalize_sequence(sequence: str) -> str: + """Normalize an RNA sequence for reference and MSA consistency checks.""" + return sequence.upper().replace("T", "U").replace("-", "").replace(".", "") + + +def parse_args() -> argparse.Namespace: + """Parse EVmutation input and output locations.""" + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + "--rows", default="all", help="Reference rows: all (default), 12 or 0-8,12" + ) + parser.add_argument( + "--output", type=Path, default=ConfigFitness.PREDICTION_DIR / "EVmutation" + ) + parser.add_argument("--cpu", type=int, default=4) + return parser.parse_args() + + +def parse_mutations(mutant: str | None, wild_type: str) -> list[tuple[int, str, str]]: + """Parse one variant and verify its reference bases and positions.""" + if mutant is None or not mutant.strip() or mutant.strip().lower() in {"wt", "wild"}: + return [] + cleaned = mutant.upper().replace("T", "U").replace(" ", "") + if MUTATION_PATTERN.fullmatch(cleaned) is None: + raise ValueError(f"Invalid mutation string: {mutant!r}") + mutations = extract_mutations(cleaned) + positions = [position for position, _, _ in mutations] + if len(positions) != len(set(positions)): + raise ValueError(f"Mutation string repeats a position: {mutant!r}") + for position, source, _ in mutations: + if position < 1 or position > len(wild_type): + raise ValueError( + f"Mutation position {position} is outside a {len(wild_type)} nt construct" + ) + if wild_type[position - 1] != source: + raise ValueError( + f"Mutation {mutant!r} expects {source} at position {position}, " + f"but the reference has {wild_type[position - 1]}" + ) + return mutations + + +def prepare_dna_alignment(path: Path, focus_id: str) -> list[int]: + """Convert protein A2M gaps and focus masking to PLMC's DNA representation.""" + # PLMC handles dot gaps and lowercase focus masking only for its protein alphabet + # https://github.com/debbiemarkslab/plmc/blob/9152c483e18263a90cbe79082a61e83cf6dd70a1/src/plm.c#L248-L258 + # https://github.com/debbiemarkslab/plmc/blob/9152c483e18263a90cbe79082a61e83cf6dd70a1/src/plm.c#L395-L413 + destination = path.with_suffix(".dna.a2m") + focus = False + query = [] + with path.open() as source, destination.open("w") as output: + for line in source: + if line.startswith(">"): + focus = line[1:].split()[0] == focus_id + output.write(line) + else: + if focus: + query.extend(line.strip()) + # Gap-reduced PLMC excludes query gaps while retaining original indices + line = "".join("-" if base.islower() else base for base in line) + output.write(line.upper().replace(".", "-")) + positions = [i + 1 for i, base in enumerate(query) if base in "ACGT"] + if not positions: + raise ValueError("Alignment has no covered query positions") + destination.replace(path) + return positions + + +def score_assay( + assay_file: Path, + alignment_file: Path, + query_file: Path, + wild_type: str, + name: str, + work_dir: Path, + cpu_count: int, +) -> pl.DataFrame: + """Score covered variants, retaining nulls for mutations outside modeled sites.""" + sequence_id, aligned_query = first_fasta_record(alignment_file) + query_id, query_sequence = first_fasta_record(query_file) + if sequence_id != query_id: + raise ValueError(f"The Riboseek MSA and query identifiers differ for {name}") + aligned_query = normalize_sequence(aligned_query) + query_sequence = normalize_sequence(query_sequence) + if aligned_query != wild_type or query_sequence != wild_type: + raise ValueError(f"The Riboseek MSA query does not match {name}") + + dna_alignment_file = work_dir / "alignment.a3m" + dna_query_file = work_dir / "query.fa" + write_dna_fasta(alignment_file, dna_alignment_file) + write_dna_fasta(query_file, dna_query_file) + config = build_config( + name, + dna_alignment_file, + dna_query_file, + sequence_id, + work_dir, + cpu_count, + ) + model = infer_model(config) + + assay = pl.read_csv(assay_file) + required_columns = {"mutant", "sequence"} + missing_columns = sorted(required_columns - set(assay.columns)) + if missing_columns: + raise ValueError(f"{assay_file} is missing columns: {missing_columns}") + + normalized_mutants = [] + covered = [] + for row, (mutant, sequence) in enumerate( + assay.select("mutant", "sequence").iter_rows() + ): + mutations = parse_mutations(mutant, wild_type) + variant = list(wild_type) + for position, _, target in mutations: + variant[position - 1] = target + if sequence is None or normalize_sequence(sequence) != "".join(variant): + raise ValueError(f"{assay_file} row {row} sequence disagrees with mutant") + normalized_mutants.append( + ( + ",".join( + f"{source}{position}{target}" + for position, source, target in mutations + ) + or "wt" + ).translate(RNA_TO_DNA) + ) + covered.append(all(position in model.index_map for position, _, _ in mutations)) + + # predict_mutation_table is the upstream scoring API and requires pandas + prediction_input = pd.DataFrame( + { + "mutant": [ + mutant for mutant, keep in zip(normalized_mutants, covered) if keep + ] + } + ) + if prediction_input.empty: + raise BailoutException("No assay variants fall within modeled positions") + predictions = predict_mutation_table(model, prediction_input, SCORE_COLUMN) + values = predictions[SCORE_COLUMN].to_numpy(dtype=float) + if not np.isfinite(values).all(): + count = int((~np.isfinite(values)).sum()) + raise FloatingPointError(f"{assay_file} produced {count} non-finite scores") + scores = np.full(assay.height, np.nan) + scores[covered] = values + print(f"{name}: scored {sum(covered)}/{assay.height} variants within modeled sites") + return assay.with_columns(pl.Series(SCORE_COLUMN, scores, nan_to_null=True)) + + +def write_dna_fasta(source: Path, destination: Path) -> None: + """Write a FASTA copy with uracil represented as thymine.""" + # Existing-alignment parsing uses the protein alphabet, which omits U + # https://github.com/debbiemarkslab/EVcouplings/blob/14c83457c6cfca8156aabe0615067447a2169791/evcouplings/align/protocol.py#L702 + with source.open() as input_handle, destination.open("w") as output_handle: + for line in input_handle: + output_handle.write( + line if line.startswith(">") else line.translate(RNA_TO_DNA) + ) + + +if __name__ == "__main__": + main() diff --git a/rnagym/fitness/baselines/EVmutation/run.sh b/rnagym/fitness/baselines/EVmutation/run.sh new file mode 100755 index 0000000..586bba9 --- /dev/null +++ b/rnagym/fitness/baselines/EVmutation/run.sh @@ -0,0 +1,10 @@ +#!/usr/bin/env bash +set -euo pipefail + +script_dir=$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd) +repository=$(cd -- "$script_dir/../../../.." && pwd) + +cd "$repository" +python -m rnagym.fitness.baselines.EVmutation.compute_fitness \ + --rows "${1:-${SLURM_ARRAY_TASK_ID:-all}}" \ + --cpu "${SLURM_CPUS_PER_TASK:-8}" diff --git a/rnagym/fitness/baselines/Evo/__init__.py b/rnagym/fitness/baselines/Evo/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/rnagym/fitness/baselines/Evo/checkpoints.py b/rnagym/fitness/baselines/Evo/checkpoints.py new file mode 100644 index 0000000..ef04adb --- /dev/null +++ b/rnagym/fitness/baselines/Evo/checkpoints.py @@ -0,0 +1,115 @@ +"""Load Evo weights from the revisions used for benchmark reproduction.""" + +from __future__ import annotations + +import fcntl +import hashlib +import json +import pkgutil +import shutil +from pathlib import Path +from tempfile import NamedTemporaryFile +from types import SimpleNamespace + +from rnagym.config import ConfigFitness +from rnagym.fitness.tasks.model_registry import checkpoint_revision + +# The 40B checkpoint is released as two byte ranges of one PyTorch file +EVO2_40B_PARTS = ( + (41126745847, "3b74fa4e6158d49265e3e270ba8869390d064358f8bf3d2af0b3e1772728f485"), + (41126745847, "bdc4a76e0f23f8295e7061c2f0deff24f723bd916dc4cdc4d9216cac9c2d49d5"), +) + + +def _verify_parts(path: Path) -> None: + """Check the merged 40B file against its pinned upstream shard hashes.""" + with path.open("rb") as handle: + for size, expected in EVO2_40B_PARTS: + digest = hashlib.sha256() + remaining = size + while remaining: + chunk = handle.read(min(8 * 1024 * 1024, remaining)) + if not chunk: + raise ValueError(f"Truncated Evo 2 checkpoint: {path}") + digest.update(chunk) + remaining -= len(chunk) + if digest.hexdigest() != expected: + raise ValueError(f"Evo 2 checkpoint checksum mismatch: {path}") + if handle.read(1): + raise ValueError(f"Unexpected trailing checkpoint data: {path}") + + +def evo2_checkpoint(model_name: str) -> str: + """Fetch a pinned Evo 2 file, checking both shards when assembling 40B.""" + from huggingface_hub import hf_hub_download + + repo_id = f"arcinstitute/{model_name}" + revision = checkpoint_revision(repo_id) + filename = f"{model_name}.pt" + if model_name != "evo2_40b": + return hf_hub_download(repo_id, filename, revision=revision) + + destination = ConfigFitness.CHECKPOINT_DIR / "evo2" / filename + destination.parent.mkdir(parents=True, exist_ok=True) + with destination.with_suffix(".lock").open("a") as lock: + fcntl.flock(lock, fcntl.LOCK_EX) + if destination.is_file(): + _verify_parts(destination) + return str(destination) + with NamedTemporaryFile(dir=destination.parent, delete=False) as output: + temporary = Path(output.name) + try: + for index in range(len(EVO2_40B_PARTS)): + part = hf_hub_download( + repo_id, f"{filename}.part{index}", revision=revision + ) + with Path(part).open("rb") as source: + shutil.copyfileobj(source, output, length=8 * 1024 * 1024) + output.flush() + _verify_parts(temporary) + temporary.chmod(0o664) + temporary.replace(destination) + finally: + temporary.unlink(missing_ok=True) + return str(destination) + + +def load_evo(model_name: str) -> SimpleNamespace: + """Load pinned safetensors with Evo 0.5's config, key mapping and dtype conversion.""" + import yaml + from evo.models import HF_MODEL_NAME_MAP + from evo.tokenizer import CharLevelTokenizer + from huggingface_hub import hf_hub_download + from safetensors.torch import load_file + from stripedhyena.model import StripedHyena + from stripedhyena.utils import dotdict + + # Evo's public constructor does not expose a checkpoint revision + repo_id = HF_MODEL_NAME_MAP[model_name] + revision = checkpoint_revision(repo_id) + index_file = Path( + hf_hub_download( + repo_id, + "model.safetensors.index.json", + revision=revision, + ) + ) + files = sorted(set(json.loads(index_file.read_text())["weight_map"].values())) + state = {} + for filename in files: + path = hf_hub_download(repo_id, filename, revision=revision) + state.update( + (name.removeprefix("backbone."), tensor) + for name, tensor in load_file(path).items() + ) + if "unembed.weight" not in state: + state["unembed.weight"] = state["embedding_layer.weight"] + + context = "131k" if model_name == "evo-1-131k-base" else "8k" + resource = pkgutil.get_data("evo", f"configs/evo-1-{context}-base_inference.yml") + if resource is None: + raise FileNotFoundError(f"Evo {context} inference config is missing") + model = StripedHyena(dotdict(yaml.safe_load(resource), Loader=yaml.FullLoader)) + model.load_state_dict(state, strict=True) + model.to_bfloat16_except_poles_residues() + return SimpleNamespace(model=model, tokenizer=CharLevelTokenizer(512)) diff --git a/rnagym/fitness/baselines/Evo/score_evo.sh b/rnagym/fitness/baselines/Evo/score_evo.sh new file mode 100644 index 0000000..8d48730 --- /dev/null +++ b/rnagym/fitness/baselines/Evo/score_evo.sh @@ -0,0 +1,20 @@ +#!/usr/bin/env bash +set -euo pipefail +umask 002 + +checkpoint_root=${RNAGYM_CHECKPOINT_DIR:-/n/lw_groups/marks/ckpt} +export HF_HUB_CACHE=${HF_HUB_CACHE:-"$checkpoint_root/evo/hub"} + +script_dir=$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd) +repository=$(cd -- "$script_dir/../../../.." && pwd) +data_dir=${RNAGYM_DATA_DIR:-"$repository/data"}/fitness + +model_name=${EVO_MODEL_NAME:-evo-1.5-8k-base} +prediction_name=${EVO_PREDICTION_NAME:-evo1.5} +rows=${1:-${SLURM_ARRAY_TASK_ID:-all}} + +cd "$repository" +python -m rnagym.fitness.baselines.Evo.score_evo_single_dms \ + --rows "$rows" \ + --output "$data_dir/model_predictions/$prediction_name" \ + --model "$model_name" diff --git a/rnagym/fitness/baselines/Evo/score_evo2.sh b/rnagym/fitness/baselines/Evo/score_evo2.sh new file mode 100644 index 0000000..9e5f9e6 --- /dev/null +++ b/rnagym/fitness/baselines/Evo/score_evo2.sh @@ -0,0 +1,27 @@ +#!/usr/bin/env bash +set -euo pipefail +umask 002 + +checkpoint_root=${RNAGYM_CHECKPOINT_DIR:-/n/lw_groups/marks/ckpt} +export HF_HUB_CACHE=${HF_HUB_CACHE:-"$checkpoint_root/evo2/hub"} + +script_dir=$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd) +repository=$(cd -- "$script_dir/../../../.." && pwd) +data_dir=${RNAGYM_DATA_DIR:-"$repository/data"}/fitness + +model_name=${EVO2_MODEL_NAME:-evo2_40b} +prediction_name=${EVO2_PREDICTION_NAME:-$model_name} +rows=${1:-${SLURM_ARRAY_TASK_ID:-all}} +local_path=${EVO2_LOCAL_PATH:-} + +extra_args=() +if [[ -n "$local_path" ]]; then + extra_args+=(--checkpoint "$local_path") +fi + +cd "$repository" +python -m rnagym.fitness.baselines.Evo.score_evo2_single_dms \ + --rows "$rows" \ + --output "$data_dir/model_predictions/$prediction_name" \ + --model "$model_name" \ + "${extra_args[@]}" diff --git a/rnagym/fitness/baselines/Evo/score_evo2_single_dms.py b/rnagym/fitness/baselines/Evo/score_evo2_single_dms.py new file mode 100644 index 0000000..4d9b82c --- /dev/null +++ b/rnagym/fitness/baselines/Evo/score_evo2_single_dms.py @@ -0,0 +1,172 @@ +#!/usr/bin/env python3 +"""Score one or more RNAGym assays with the official Evo 2 predictor. + +Multiple ``--rows`` share one model load. Vortex controls model placement +and sharding across the visible GPUs, so this script never moves the model. +""" + +from __future__ import annotations + +import argparse +import sys +from collections.abc import Callable, Sequence +from pathlib import Path +from typing import Any + +import numpy as np +import polars as pl +import torch +from scipy.stats import spearmanr + +from rnagym.config import ConfigFitness +from rnagym.fitness.data import ( + read_assay, + read_reference, + write_csv_atomically, +) + +# FP8 predictions depend on batch shape, so keep the production token budgets +BATCH_TOKENS = { + "evo2_1b_base": 32768, + "evo2_7b": 16384, + "evo2_20b": 16384, + "evo2_40b": 8192, +} + + +def effective_length(sequence_length: int, prepend_bos: bool, fp8: bool) -> int: + """Return the sequence dimension used by the largest input projection.""" + length = sequence_length + int(prepend_bos) + return ((length + 15) // 16) * 16 if fp8 else length + + +def load_dms_data(dms_dir: Path, dms_id: str) -> pl.DataFrame: + """Load and validate an assay table.""" + return read_assay(dms_dir / f"{dms_id}.csv") + + +def main() -> None: + """Run the Evo 2 scoring command.""" + run(parse_args()) + + +def parse_args(argv: Sequence[str] | None = None) -> argparse.Namespace: + """Parse command-line arguments.""" + parser = argparse.ArgumentParser(description="Score assays with Evo 2") + parser.add_argument( + "--rows", default="all", help="Reference rows: all (default), 12 or 0-8,12" + ) + parser.add_argument("--output", type=Path, required=True) + parser.add_argument( + "--model", dest="model_name", choices=tuple(BATCH_TOKENS), default="evo2_40b" + ) + parser.add_argument("--checkpoint", dest="local_path", help="Local checkpoint path") + return parser.parse_args(argv) + + +def preprocess_sequence(sequence: str) -> str: + """Convert an RNA or DNA sequence to uppercase DNA.""" + return sequence.strip().upper().replace("U", "T") + + +def run( + args: argparse.Namespace, model_factory: Callable[..., Any] | None = None +) -> None: + """Load Evo 2 once and score all requested assays.""" + dms_ids = read_reference(ConfigFitness.REFERENCE_FILE, args.rows)[ + "DMS_ID" + ].to_list() + args.output.mkdir(parents=True, exist_ok=True) + + if not torch.cuda.is_available(): + print("WARNING: Evo 2 requires CUDA", file=sys.stderr) + print(f"Loading {args.model_name} on {torch.cuda.device_count()} visible GPUs") + if model_factory is None: + from evo2 import Evo2 + + from rnagym.fitness.baselines.Evo.checkpoints import evo2_checkpoint + + if args.local_path is None: + args.local_path = evo2_checkpoint(args.model_name) + model_factory = Evo2 + model = model_factory(args.model_name, local_path=args.local_path) + fp8 = bool(model.model.config.get("use_fp8_input_projections", False)) + if not fp8: + raise RuntimeError("The constructed model does not use FP8 input projections") + + failures = [] + for dms_id in dms_ids: + try: + score_assay(model, args, dms_id, fp8) + except Exception as error: + if len(dms_ids) == 1: + raise + print(f"{dms_id} failed: {error}", file=sys.stderr) + failures.append(dms_id) + if failures: + raise RuntimeError(f"Failed assays: {', '.join(failures)}") + + +def score_assay(model: Any, args: argparse.Namespace, dms_id: str, fp8: bool) -> float: + """Score one assay, write its prediction table, and return Spearman rho.""" + data = load_dms_data(ConfigFitness.ASSAY_DIR, dms_id) + raw_sequences = data["sequence"] + invalid_sequences = raw_sequences.is_null() | raw_sequences.str.strip_chars().eq("") + if invalid_sequences.any(): + count = int(invalid_sequences.sum()) + rows = np.flatnonzero(invalid_sequences.to_numpy()).tolist()[:5] + raise ValueError( + f"{dms_id} has {count} missing or empty sequences, including rows {rows}" + ) + sequences = [preprocess_sequence(value) for value in raw_sequences] + if not sequences: + raise ValueError(f"{dms_id} has no nonempty sequences") + + lengths = {len(sequence) for sequence in sequences} + if len(lengths) != 1: + raise ValueError(f"{dms_id} contains mixed sequence lengths: {sorted(lengths)}") + sequence_length = lengths.pop() + length = effective_length(sequence_length, False, fp8) + batch_size = max(1, BATCH_TOKENS[args.model_name] // length) + + print( + f"{dms_id}: scoring {len(sequences)} sequences of length {sequence_length} " + f"in batches of {batch_size}" + ) + scores = np.asarray( + model.score_sequences( + sequences, + batch_size=batch_size, + prepend_bos=False, + reduce_method="mean", + average_reverse_complement=True, + ), + dtype=float, + ) + if scores.shape != (len(sequences),): + raise ValueError( + f"{dms_id} returned score shape {scores.shape}, expected {(len(sequences),)}" + ) + if not np.isfinite(scores).all(): + raise FloatingPointError(f"{dms_id} returned nonfinite model scores") + + assay_scores = data["DMS_score"].cast(pl.Float64, strict=False).to_numpy() + if not np.isfinite(assay_scores).all(): + raise ValueError(f"{dms_id} contains missing or nonfinite DMS scores") + + score_column = f"{args.model_name}_score" + data = data.with_columns(pl.Series(score_column, scores)) + if len(data) < 2: + correlation = pvalue = float("nan") + else: + result = spearmanr(assay_scores, scores) + correlation, pvalue = result.statistic, result.pvalue + + output_file = args.output / f"{dms_id}.csv" + write_csv_atomically(data, output_file) + print(f"{dms_id}: Spearman={correlation:.3f} p={pvalue:.2e} -> {output_file}") + return correlation + + +if __name__ == "__main__": + main() diff --git a/rnagym/fitness/baselines/Evo/score_evo_single_dms.py b/rnagym/fitness/baselines/Evo/score_evo_single_dms.py new file mode 100644 index 0000000..fae0988 --- /dev/null +++ b/rnagym/fitness/baselines/Evo/score_evo_single_dms.py @@ -0,0 +1,123 @@ +"""Score Evo 1 and 1.5 in float32 with a BOS token and mean log probability.""" + +from __future__ import annotations + +import argparse +from collections.abc import Callable, Sequence +from pathlib import Path +from typing import Any + +import numpy as np +import polars as pl +import torch +from scipy.stats import spearmanr +from tqdm.auto import tqdm + +from rnagym.config import ConfigFitness +from rnagym.fitness.data import ( + read_assay, + read_reference, + write_csv_atomically, +) + + +def main() -> None: + """Run the Evo scoring command.""" + run(parse_args()) + + +def parse_args(argv: Sequence[str] | None = None) -> argparse.Namespace: + """Parse the checkpoint, assay and output directory.""" + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + "--rows", default="all", help="Reference rows: all (default), 12 or 0-8,12" + ) + parser.add_argument("--output", type=Path, required=True) + parser.add_argument("--model", dest="model_name", default="evo-1.5-8k-base") + return parser.parse_args(argv) + + +def prepare_model(model: Any) -> Any: + """Use float32 inference to preserve small score differences across GPUs.""" + from flash_attn.modules.mha import CrossAttention, SelfAttention + + # BF16 rounding changes variant rankings, and FP16 overflows for both checkpoints + torch.backends.cuda.matmul.allow_tf32 = False + torch.backends.cudnn.allow_tf32 = False + model.float() + for block in model.blocks: + if hasattr(block, "inner_mha_cls"): + attention = block.inner_mha_cls + attention.use_flash_attn = False + attention.inner_attn = SelfAttention(causal=True) + attention.inner_cross_attn = CrossAttention(causal=True) + return model + + +def run( + args: argparse.Namespace, model_factory: Callable[..., Any] | None = None +) -> None: + """Load one checkpoint and score the selected assays.""" + reference = read_reference(ConfigFitness.REFERENCE_FILE, args.rows) + checkpoint = None + device = "cuda:0" if torch.cuda.is_available() else "cpu" + for name in reference["DMS_ID"]: + assay = read_assay(ConfigFitness.ASSAY_DIR / f"{name}.csv") + raw = assay["sequence"] + if ( + assay.is_empty() + or raw.is_null().any() + or raw.str.strip_chars().eq("").any() + ): + raise ValueError(f"{name} has missing or empty sequences") + sequences = ( + raw.str.strip_chars().str.to_uppercase().str.replace_all("U", "T").to_list() + ) + if len({len(sequence) for sequence in sequences}) != 1: + raise ValueError(f"{name} has mixed sequence lengths") + if checkpoint is None: + if model_factory is None: + from rnagym.fitness.baselines.Evo.checkpoints import load_evo + + model_factory = load_evo + checkpoint = model_factory(args.model_name) + prepare_model(checkpoint.model.to(device)).eval() + model = checkpoint.model + scores = run_inference( + model, checkpoint.tokenizer, sequences, device, 128 + ).mean(axis=1) + if not np.isfinite(scores).all(): + raise FloatingPointError(f"{name} produced nonfinite model scores") + column = f"{args.model_name.replace('-', '_')}_score" + result = assay.with_columns(pl.Series(column, scores)) + write_csv_atomically(result, args.output / f"{name}.csv") + print( + f"{name}: Spearman {spearmanr(assay['DMS_score'].to_numpy(), scores).statistic:.6f}" + ) + + +def run_inference( + model: Any, tokenizer: Any, sequences: list[str], device: str, batch_size: int +) -> np.ndarray: + """Return float32 nucleotide log probabilities in input order, excluding BOS.""" + from evo.scoring import logits_to_logprobs, prepare_batch + + # Bound unfused attention memory for the longer coding assays + batch_size = min(batch_size, max(1, 4096 // (len(sequences[0]) + 1))) + batches = [] + for start in tqdm(range(0, len(sequences), batch_size), desc="Scoring"): + inputs, _ = prepare_batch( + sequences[start : start + batch_size], + tokenizer, + prepend_bos=True, + device=device, + ) + with torch.inference_mode(): + logits, *_ = model(inputs) + values = logits_to_logprobs(logits, inputs, trim_bos=True) + batches.append(values.float().cpu().numpy()) + return np.concatenate(batches) + + +if __name__ == "__main__": + main() diff --git a/rnagym/fitness/baselines/GenSLM/__init__.py b/rnagym/fitness/baselines/GenSLM/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/rnagym/fitness/baselines/GenSLM/compute_fitness.py b/rnagym/fitness/baselines/GenSLM/compute_fitness.py new file mode 100644 index 0000000..639dfa8 --- /dev/null +++ b/rnagym/fitness/baselines/GenSLM/compute_fitness.py @@ -0,0 +1,133 @@ +"""Score GenSLM with mean next-codon log likelihood, excluding padding.""" + +from __future__ import annotations + +import argparse +from collections.abc import Callable, Sequence +from pathlib import Path +from typing import Any + +import numpy as np +import polars as pl +import torch +from torch.utils.data import DataLoader +from tqdm.auto import tqdm + +from rnagym.config import ConfigFitness +from rnagym.fitness.data import ( + read_assay, + read_reference, + write_csv_atomically, +) + + +def main( + args: argparse.Namespace | None = None, + model_factory: Callable[..., Any] | None = None, +) -> None: + """Load GenSLM once and score the selected assays.""" + args = args or parse_args() + reference = read_reference(ConfigFitness.REFERENCE_FILE, args.rows) + if model_factory is None: + from genslm import GenSLM + + model_factory = GenSLM + model = model_factory( + "genslm_2.5B_patric", + model_cache_dir=str(ConfigFitness.CHECKPOINT_DIR / "genslm/2.5B"), + ) + device = "cuda" if torch.cuda.is_available() else "cpu" + model = model.to(device).eval() + for row in reference.iter_rows(named=True): + process_single_row( + row, + model, + device, + ConfigFitness.ASSAY_DIR, + args.output, + "logit_scores", + ) + + +def parse_args(argv: Sequence[str] | None = None) -> argparse.Namespace: + """Parse the assays and output directory.""" + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + "--rows", default="all", help="Reference rows: all (default), 12 or 0-8,12" + ) + parser.add_argument( + "--output", type=Path, default=ConfigFitness.PREDICTION_DIR / "GenSLM" + ) + return parser.parse_args(argv) + + +def process_single_row( + row: dict[str, str], + model: Any, + device: str, + base_dir: Path, + results_dir: Path, + score_column: str, +) -> None: + """Score an assay with higher values indicating greater sequence likelihood.""" + name = row["DMS_ID"] + assay = read_assay(base_dir / f"{name}.csv").drop_nulls("mutant") + raw = assay["sequence"] + if assay.is_empty() or raw.is_null().any() or raw.str.strip_chars().eq("").any(): + raise ValueError(f"{name} has missing or empty sequences") + sequences = ( + raw.str.strip_chars().str.to_uppercase().str.replace_all("U", "T").to_list() + ) + scores = sequence_log_likelihoods(model, sequences, device) + result = assay.with_columns( + pl.Series("mutated_sequence", sequences), pl.Series(score_column, scores) + ) + write_csv_atomically(result, results_dir / f"{name}.csv") + + +def sequence_log_likelihoods( + model: Any, sequences: list[str], device: str, batch_size: int = 4 +) -> np.ndarray: + """Return mean causal log likelihoods using the official codon tokenizer. + + Each logit predicts the following token. The first token has no prediction, + and padding is excluded from both the loss and its denominator. This equals + the negative native model loss for an unpadded sequence. + """ + from genslm import SequenceDataset + + if batch_size < 1 or not sequences: + raise ValueError("Scoring requires sequences and a positive batch size") + if any(not sequence or set(sequence) - set("ACGT") for sequence in sequences): + raise ValueError("GenSLM sequences must contain only A, C, G and T") + max_length = max(map(len, sequences)) + token_count = (max_length + 2) // 3 + model.tokenizer.num_special_tokens_to_add() + if token_count > model.seq_length: + raise ValueError("Sequence exceeds the published GenSLM input length limit") + if token_count < 2: + raise ValueError("GenSLM requires at least two tokens per sequence") + dataset = SequenceDataset(sequences, token_count, model.tokenizer) + batches = [] + with torch.inference_mode(): + for batch in tqdm(DataLoader(dataset, batch_size=batch_size), desc="Scoring"): + input_ids = batch["input_ids"].to(device) + attention_mask = batch["attention_mask"].squeeze(1).to(device) + targets = input_ids[:, 1:].masked_fill(~attention_mask[:, 1:].bool(), -100) + counts = attention_mask[:, 1:].sum(1) + if (counts == 0).any(): + raise ValueError("GenSLM requires at least two tokens per sequence") + output = model(input_ids, attention_mask, output_hidden_states=False) + loss = torch.nn.functional.cross_entropy( + output.logits[:, :-1].float().permute(0, 2, 1), + targets, + reduction="none", + ) + batches.append((-loss.sum(1) / counts).cpu().numpy()) + scores = np.concatenate(batches) + if not np.isfinite(scores).all(): + raise FloatingPointError("GenSLM produced nonfinite model scores") + return scores + + +if __name__ == "__main__": + main() diff --git a/rnagym/fitness/baselines/GenSLM/run_model.sh b/rnagym/fitness/baselines/GenSLM/run_model.sh new file mode 100644 index 0000000..5371464 --- /dev/null +++ b/rnagym/fitness/baselines/GenSLM/run_model.sh @@ -0,0 +1,10 @@ +#!/usr/bin/env bash +set -euo pipefail + +script_dir=$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd) +repository=$(cd -- "$script_dir/../../../.." && pwd) +rows=${1:-${SLURM_ARRAY_TASK_ID:-all}} + +cd "$repository" +python -m rnagym.fitness.baselines.GenSLM.compute_fitness \ + --rows "$rows" diff --git a/rnagym/fitness/baselines/Nucleotide_Transformer/__init__.py b/rnagym/fitness/baselines/Nucleotide_Transformer/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/fitness/baselines/Nucleotide_Transformer/compute_fitness.py b/rnagym/fitness/baselines/Nucleotide_Transformer/compute_fitness.py similarity index 64% rename from fitness/baselines/Nucleotide_Transformer/compute_fitness.py rename to rnagym/fitness/baselines/Nucleotide_Transformer/compute_fitness.py index c20f17e..2e1d3a0 100644 --- a/fitness/baselines/Nucleotide_Transformer/compute_fitness.py +++ b/rnagym/fitness/baselines/Nucleotide_Transformer/compute_fitness.py @@ -7,12 +7,16 @@ not the authors' supervised variant-effect method. """ -import sys -from pathlib import Path +import argparse -sys.path.insert(0, str(Path(__file__).resolve().parents[1])) +import torch +from typing_extensions import override -from masked_lm import MaskedLMAdapter, main # noqa: E402 +from rnagym.fitness.baselines.masked_lm import MaskedLMAdapter, main +from rnagym.fitness.tasks.model_registry import checkpoint_revision + +# Tokenizer, configuration and model code live in InstaDeepAI/ntv3_base_model +CODE_REVISION = "0ecff3637f0d3ba5b686d1095083218157c2ca34" class NTv3Adapter(MaskedLMAdapter): @@ -25,22 +29,36 @@ class NTv3Adapter(MaskedLMAdapter): default_batch_size = 128 @staticmethod - def add_arguments(parser): + @override + def add_arguments(parser: argparse.ArgumentParser) -> None: parser.add_argument( - "--model_name", + "--model", + dest="model_name", default="InstaDeepAI/NTv3_650M_pre", - help="Main pretrained checkpoint ID or local path " - "(default: InstaDeepAI/NTv3_650M_pre)", + help="Pretrained checkpoint ID or local path", ) - def load(self, args): + @override + def context_length_for(self, length: int) -> int: + """Round up to the checkpoint's U-Net block size.""" + multiple = 2**self.num_downsamples + return ((length + multiple - 1) // multiple) * multiple + + @override + def load(self, args: argparse.Namespace) -> None: from transformers import AutoModelForMaskedLM, AutoTokenizer + revision = checkpoint_revision(args.model_name) tokenizer = AutoTokenizer.from_pretrained( - args.model_name, trust_remote_code=True + args.model_name, + revision=revision, + code_revision=CODE_REVISION, + trust_remote_code=True, ) self.model = AutoModelForMaskedLM.from_pretrained( args.model_name, + revision=revision, + code_revision=CODE_REVISION, trust_remote_code=True, ) self.model = self.model.to(args.device).eval() @@ -53,12 +71,14 @@ def load(self, args): self.pad_id = tokenizer.pad_token_id self.unk_id = vocab["N"] - def context_length_for(self, length: int) -> int: - """Round up to the checkpoint's U-Net block size.""" - multiple = 2**self.num_downsamples - return ((length + multiple - 1) // multiple) * multiple - - def logits_at(self, input_ids, attention_mask, rows, cols): + @override + def logits_at( + self, + input_ids: torch.Tensor, + attention_mask: torch.Tensor, + rows: torch.Tensor, + cols: torch.Tensor, + ) -> torch.Tensor: if (input_ids == self.pad_id).any(): raise ValueError( f"{self.name} ignores the attention mask, but a padding token " diff --git a/rnagym/fitness/baselines/Nucleotide_Transformer/run.sh b/rnagym/fitness/baselines/Nucleotide_Transformer/run.sh new file mode 100644 index 0000000..9ba7ff1 --- /dev/null +++ b/rnagym/fitness/baselines/Nucleotide_Transformer/run.sh @@ -0,0 +1,20 @@ +#!/usr/bin/env bash +set -euo pipefail +umask 002 + +checkpoint_root=${RNAGYM_CHECKPOINT_DIR:-/n/lw_groups/marks/ckpt} +export HF_HUB_CACHE=${HF_HUB_CACHE:-"$checkpoint_root/ntv3/hub"} + +script_dir=$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd) +repository=$(cd -- "$script_dir/../../../.." && pwd) +data_dir=${RNAGYM_DATA_DIR:-"$repository/data"}/fitness + +model_name=${NTV3_MODEL_NAME:-InstaDeepAI/NTv3_650M_pre} +prediction_name=${NTV3_PREDICTION_NAME:-ntv3_650m_4fill} +rows=${1:-${SLURM_ARRAY_TASK_ID:-all}} + +cd "$repository" +python -m rnagym.fitness.baselines.Nucleotide_Transformer.compute_fitness \ + --rows "$rows" \ + --output "$data_dir/model_predictions/$prediction_name" \ + --model "$model_name" diff --git a/rnagym/fitness/baselines/Orthrus/__init__.py b/rnagym/fitness/baselines/Orthrus/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/rnagym/fitness/baselines/Orthrus/score_orthrus.sh b/rnagym/fitness/baselines/Orthrus/score_orthrus.sh new file mode 100644 index 0000000..ebcc4e4 --- /dev/null +++ b/rnagym/fitness/baselines/Orthrus/score_orthrus.sh @@ -0,0 +1,19 @@ +#!/usr/bin/env bash +set -euo pipefail +umask 002 + +checkpoint_root=${RNAGYM_CHECKPOINT_DIR:-/n/lw_groups/marks/ckpt} +export HF_HUB_CACHE=${HF_HUB_CACHE:-"$checkpoint_root/orthrus/hub"} + +script_dir=$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd) +repository=$(cd -- "$script_dir/../../../.." && pwd) +data_dir=${RNAGYM_DATA_DIR:-"$repository/data"}/fitness + +model_name=${ORTHRUS_MODEL_NAME:-antichronology/orthrus-mlm-6-track} +rows=${1:-${SLURM_ARRAY_TASK_ID:-all}} + +cd "$repository" +python -m rnagym.fitness.baselines.Orthrus.score_orthrus_single_dms \ + --rows "$rows" \ + --output "$data_dir/model_predictions/orthrus_4fill" \ + --model "$model_name" diff --git a/fitness/baselines/Orthrus/score_orthrus_single_dms.py b/rnagym/fitness/baselines/Orthrus/score_orthrus_single_dms.py similarity index 74% rename from fitness/baselines/Orthrus/score_orthrus_single_dms.py rename to rnagym/fitness/baselines/Orthrus/score_orthrus_single_dms.py index affb49a..14c3df0 100644 --- a/fitness/baselines/Orthrus/score_orthrus_single_dms.py +++ b/rnagym/fitness/baselines/Orthrus/score_orthrus_single_dms.py @@ -10,14 +10,13 @@ https://huggingface.co/antichronology/orthrus-mlm-6-track/blob/5f0dc87d51065035fc28e71972c69f9c84f4deae/orthrus_hf.py#L290-L325 """ -import sys -from pathlib import Path +import argparse import torch +from typing_extensions import override -sys.path.insert(0, str(Path(__file__).resolve().parents[1])) - -from masked_lm import MaskedLMAdapter, main # noqa: E402 +from rnagym.fitness.baselines.masked_lm import MaskedLMAdapter, main +from rnagym.fitness.tasks.model_registry import checkpoint_revision class OrthrusAdapter(MaskedLMAdapter): @@ -37,20 +36,27 @@ class OrthrusAdapter(MaskedLMAdapter): PAD_CODE = 5 @staticmethod - def add_arguments(parser): + @override + def add_arguments(parser: argparse.ArgumentParser) -> None: parser.add_argument( - "--model_name", + "--model", + dest="model_name", type=str, default="antichronology/orthrus-mlm-6-track", - help="Orthrus checkpoint with an MLM head (default: " - "antichronology/orthrus-mlm-6-track). The contrastive checkpoints " - "raise NotImplementedError from predict_tokens", + help="MLM checkpoint ID or local path", ) - def load(self, args): + @override + def load(self, args: argparse.Namespace) -> None: from transformers import AutoModel - self.model = AutoModel.from_pretrained(args.model_name, trust_remote_code=True) + revision = checkpoint_revision(args.model_name) + self.model = AutoModel.from_pretrained( + args.model_name, + revision=revision, + code_revision=revision, + trust_remote_code=True, + ) self.model = self.model.to(args.device).eval() self.device = args.device if getattr(self.model, "sequence_head", None) is None: @@ -65,7 +71,14 @@ def load(self, args): self.prefix_ids = [] self.suffix_ids = [] - def logits_at(self, input_ids, attention_mask, rows, cols): + @override + def logits_at( + self, + input_ids: torch.Tensor, + attention_mask: torch.Tensor, + rows: torch.Tensor, + cols: torch.Tensor, + ) -> torch.Tensor: """ Convert base codes to six-track inputs for ``predict_tokens``. diff --git a/rnagym/fitness/baselines/RNAERNIE/__init__.py b/rnagym/fitness/baselines/RNAERNIE/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/rnagym/fitness/baselines/RNAERNIE/compute_fitness.py b/rnagym/fitness/baselines/RNAERNIE/compute_fitness.py new file mode 100644 index 0000000..535f1ff --- /dev/null +++ b/rnagym/fitness/baselines/RNAERNIE/compute_fitness.py @@ -0,0 +1,162 @@ +"""Score fitness assays with RNA-ERNIE.""" + +from __future__ import annotations + +import argparse +import re +from pathlib import Path + +import numpy as np +import paddle +import polars as pl +from paddlenlp.transformers import ErnieForMaskedLM + +from rnagym.config import ConfigFitness +from rnagym.fitness.baselines.RNAERNIE.src.rna_ernie import BatchConverter +from rnagym.fitness.data import read_reference, write_csv_atomically + +BASES = "ACGT" +MUTATION_PATTERN = re.compile(r"^[ACGT]\d+[ACGT](?:,[ACGT]\d+[ACGT])*$") +SCORE_COLUMN = "Mutation_Scores" + + +def main() -> None: + """Load RNA-ERNIE once and score the selected assays.""" + args = parse_args() + reference = read_reference(ConfigFitness.REFERENCE_FILE, args.rows) + model = None + converter = BatchConverter( + vocab_path=Path(__file__).with_name("src") / "vocab_1MER.txt" + ) + for row in reference.iter_rows(named=True): + name = row["DMS_ID"] + wild_type = normalize_sequence(row["RAW_CONSTRUCT_SEQ"]) + if len(wild_type) > 510: + raise ValueError(f"{name} is too long for RNA-ERNIE: {len(wild_type)} nt") + assay_file = ConfigFitness.ASSAY_DIR / f"{name}.csv" + if not assay_file.is_file(): + raise FileNotFoundError(f"Fitness assay is missing: {assay_file}") + output_file = args.output / assay_file.name + + if model is None: + model = ErnieForMaskedLM.from_pretrained( + str(ConfigFitness.CHECKPOINT_DIR / "rna-ernie/checkpoint_final"), + use_task_id=False, + ) + model.eval() + result = score_assay(model, converter, assay_file, wild_type) + + write_csv_atomically(result, output_file) + print(f"Scores saved to {output_file}") + + +def normalize_sequence(sequence: str | None) -> str: + """Return one validated sequence in RNA-ERNIE's DNA alphabet.""" + if sequence is None or not sequence.strip(): + raise ValueError("Sequence is missing or empty") + normalized = sequence.upper().replace("U", "T") + invalid = sorted(set(normalized) - set(BASES)) + if invalid: + raise ValueError(f"Sequence contains unsupported bases: {invalid}") + return normalized + + +def parse_args() -> argparse.Namespace: + """Parse RNA-ERNIE input and output paths.""" + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + "--rows", default="all", help="Reference rows: all (default), 12 or 0-8,12" + ) + parser.add_argument( + "--output", type=Path, default=ConfigFitness.PREDICTION_DIR / "RNAErnie" + ) + return parser.parse_args() + + +def parse_mutations(mutant: str | None, wild_type: str) -> list[tuple[int, str, str]]: + """Parse substitutions and validate them against the wild type.""" + if mutant is None: + raise ValueError("Mutation is missing") + normalized = mutant.upper().replace("U", "T").replace(" ", "") + if MUTATION_PATTERN.fullmatch(normalized) is None: + raise ValueError(f"Invalid mutation string: {mutant!r}") + + mutations = [] + positions = set() + for token in normalized.split(","): + position = int(token[1:-1]) - 1 + source, target = token[0], token[-1] + if position in positions: + raise ValueError(f"Mutation string repeats position {position + 1}") + if position < 0 or position >= len(wild_type): + raise ValueError( + f"Mutation position {position + 1} is outside a " + f"{len(wild_type)} nt construct" + ) + if wild_type[position] != source: + raise ValueError( + f"Mutation {mutant!r} expects {source} at position {position + 1}, " + f"but the reference has {wild_type[position]}" + ) + positions.add(position) + mutations.append((position, source, target)) + return mutations + + +def score_assay( + model: ErnieForMaskedLM, + converter: BatchConverter, + assay_file: Path, + wild_type: str, +) -> pl.DataFrame: + """Score every non-wild-type row in one assay.""" + _, _, input_ids = next(iter(converter([("wild_type", wild_type)]))) + with paddle.no_grad(): + logits = model(input_ids) + probabilities = paddle.nn.functional.softmax(logits, axis=-1)[0].numpy() + + assay = pl.read_csv(assay_file) + required_columns = {"mutant", "sequence"} + missing_columns = sorted(required_columns - set(assay.columns)) + if missing_columns: + raise ValueError(f"{assay_file} is missing columns: {missing_columns}") + assay = assay.filter(pl.col("mutant").is_not_null()) + if assay.is_empty(): + raise ValueError(f"{assay_file} has no variants") + + alphabet = converter.alphabet + scores = [] + normalized_mutants = [] + for row, (mutant, sequence) in enumerate( + assay.select("mutant", "sequence").iter_rows() + ): + mutations = parse_mutations(mutant, wild_type) + variant = list(wild_type) + score = 0.0 + for position, source, target in mutations: + variant[position] = target + score += float( + probabilities[position + 1, alphabet[target]] + - probabilities[position + 1, alphabet[source]] + ) + if normalize_sequence(sequence) != "".join(variant): + raise ValueError(f"{assay_file} row {row} sequence disagrees with mutant") + scores.append(score) + normalized_mutants.append( + ",".join( + f"{source}{position + 1}{target}" + for position, source, target in mutations + ) + ) + + scores = np.asarray(scores) + if not np.isfinite(scores).all(): + raise FloatingPointError(f"{assay_file} produced non-finite scores") + return assay.with_columns( + pl.Series("mutant", normalized_mutants), + pl.Series(SCORE_COLUMN, scores), + ) + + +if __name__ == "__main__": + main() diff --git a/rnagym/fitness/baselines/RNAERNIE/run.sh b/rnagym/fitness/baselines/RNAERNIE/run.sh new file mode 100644 index 0000000..0753531 --- /dev/null +++ b/rnagym/fitness/baselines/RNAERNIE/run.sh @@ -0,0 +1,8 @@ +#!/usr/bin/env bash +set -euo pipefail + +script_dir=$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd) +repository=$(cd -- "$script_dir/../../../.." && pwd) + +cd "$repository" +python -m rnagym.fitness.baselines.RNAERNIE.compute_fitness --rows "${1:-${SLURM_ARRAY_TASK_ID:-all}}" diff --git a/rnagym/fitness/baselines/RNAERNIE/src/__init__.py b/rnagym/fitness/baselines/RNAERNIE/src/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/rnagym/fitness/baselines/RNAERNIE/src/rna_ernie.py b/rnagym/fitness/baselines/RNAERNIE/src/rna_ernie.py new file mode 100644 index 0000000..6488ad0 --- /dev/null +++ b/rnagym/fitness/baselines/RNAERNIE/src/rna_ernie.py @@ -0,0 +1,50 @@ +"""Single-nucleotide token batches for the released RNA-ERNIE checkpoint.""" + +from pathlib import Path + +import paddle + + +class BatchConverter: + """Encode sequences as CLS, nucleotide IDs, SEP and right padding.""" + + def __call__(self, data: list[tuple[str, str]]): + """Yield names, normalized sequences and padded token tensors.""" + for start in range(0, len(data), self.batch_size): + batch = data[start : start + self.batch_size] + names = [name for name, _ in batch] + sequences = [sequence.upper().replace("U", "T") for _, sequence in batch] + encoded = [] + for name, sequence in zip(names, sequences): + if len(sequence) > self.max_sequence_length - 2: + raise ValueError(f"{name} exceeds the model sequence limit") + tokens = [ + self.alphabet["[CLS]"], + *(self.alphabet[base] for base in sequence), + self.alphabet["[SEP]"], + ] + encoded.append( + tokens + + [self.alphabet["[PAD]"]] + * (self.max_sequence_length - len(tokens)) + ) + yield names, sequences, paddle.to_tensor(encoded) + + def __init__( + self, vocab_path: Path, batch_size: int = 256, max_sequence_length: int = 512 + ) -> None: + if batch_size < 1 or max_sequence_length < 3: + raise ValueError( + "Batch size must be positive and sequence length must allow special tokens" + ) + self.batch_size = batch_size + self.max_sequence_length = max_sequence_length + self.alphabet = { + token: index + for index, token in enumerate(vocab_path.read_text().splitlines()) + } + required = {"[CLS]", "[SEP]", "[PAD]", "A", "C", "G", "T"} + if not required <= self.alphabet.keys(): + raise ValueError( + f"Vocabulary is missing tokens: {sorted(required - self.alphabet.keys())}" + ) diff --git a/fitness/baselines/RNAERNIE/src/vocab_1MER.txt b/rnagym/fitness/baselines/RNAERNIE/src/vocab_1MER.txt similarity index 100% rename from fitness/baselines/RNAERNIE/src/vocab_1MER.txt rename to rnagym/fitness/baselines/RNAERNIE/src/vocab_1MER.txt diff --git a/rnagym/fitness/baselines/RNAGenesis/__init__.py b/rnagym/fitness/baselines/RNAGenesis/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/rnagym/fitness/baselines/RNAGenesis/score_rnagenesis.sh b/rnagym/fitness/baselines/RNAGenesis/score_rnagenesis.sh new file mode 100644 index 0000000..83e6927 --- /dev/null +++ b/rnagym/fitness/baselines/RNAGenesis/score_rnagenesis.sh @@ -0,0 +1,15 @@ +#!/usr/bin/env bash +set -euo pipefail + +script_dir=$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd) +repository=$(cd -- "$script_dir/../../../.." && pwd) +checkpoint_root=${RNAGYM_CHECKPOINT_DIR:-/n/lw_groups/marks/ckpt} +data_dir=${RNAGYM_DATA_DIR:-"$repository/data"}/fitness + +rows=${1:-${SLURM_ARRAY_TASK_ID:-all}} + +cd "$repository" +python -m rnagym.fitness.baselines.RNAGenesis.score_rnagenesis_single_dms \ + --rows "$rows" \ + --output "$data_dir/model_predictions/rnagenesis_4fill" \ + --model "$checkpoint_root/rnagenesis" diff --git a/fitness/baselines/RNAGenesis/score_rnagenesis_single_dms.py b/rnagym/fitness/baselines/RNAGenesis/score_rnagenesis_single_dms.py similarity index 64% rename from fitness/baselines/RNAGenesis/score_rnagenesis_single_dms.py rename to rnagym/fitness/baselines/RNAGenesis/score_rnagenesis_single_dms.py index cb06457..8509e1c 100644 --- a/fitness/baselines/RNAGenesis/score_rnagenesis_single_dms.py +++ b/rnagym/fitness/baselines/RNAGenesis/score_rnagenesis_single_dms.py @@ -6,32 +6,28 @@ because the shipped tokenizer does not expose the model's mask ID. """ -import sys +import argparse from pathlib import Path import torch +from typing_extensions import override -sys.path.insert(0, str(Path(__file__).resolve().parents[1])) - -from masked_lm import MaskedLMAdapter, main # noqa: E402 +from rnagym.fitness.baselines.masked_lm import MaskedLMAdapter, main MASK_TOKEN = "tMASK" UNK_TOKEN = "N" -def load_vocab(model_path: str) -> dict: +def load_vocab(model_path: Path) -> dict[str, int]: """ Read the model's own vocabulary file and return a token to id mapping. The HuggingFace tokenizer wrapper is bypassed on purpose, see the module docstring. """ - vocab_file = Path(model_path) / "tokenizer.model" + vocab_file = model_path / "tokenizer.model" if not vocab_file.exists(): - raise FileNotFoundError( - f"Vocabulary file not found: {vocab_file}. Point --model_name at a " - f"local copy of the RNAGenesis checkpoint." - ) + raise FileNotFoundError(f"Vocabulary file not found: {vocab_file}") tokens = vocab_file.read_text().splitlines() return {token: index for index, token in enumerate(tokens)} @@ -47,31 +43,25 @@ class RNAGenesisAdapter(MaskedLMAdapter): default_max_batch_tokens = 32768 @staticmethod - def add_arguments(parser): + @override + def add_arguments(parser: argparse.ArgumentParser) -> None: parser.add_argument( - "--model_name", - type=str, + "--model", + dest="model_name", + type=Path, required=True, - help="Local path of the RNAGenesis encoder checkpoint directory, " - "which must contain tokenizer.model", - ) - parser.add_argument( - "--dtype", - type=str, - default="bfloat16", - choices=["bfloat16", "float32"], - help="Torch dtype for the model weights. bfloat16 is the default and " - "is what the released predictions were produced with", + help="Local RNAGenesis checkpoint directory", ) - def load(self, args): + @override + def load(self, args: argparse.Namespace) -> None: from transformers import AutoModelForMaskedLM vocab = load_vocab(args.model_name) self.model = AutoModelForMaskedLM.from_pretrained( args.model_name, trust_remote_code=True, - torch_dtype=getattr(torch, args.dtype), + torch_dtype=torch.bfloat16, ) self.model = self.model.to(args.device).eval() self.device = args.device @@ -83,7 +73,14 @@ def load(self, args): self.prefix_ids = [] # the reference usage adds no special tokens self.suffix_ids = [] - def logits_at(self, input_ids, attention_mask, rows, cols): + @override + def logits_at( + self, + input_ids: torch.Tensor, + attention_mask: torch.Tensor, + rows: torch.Tensor, + cols: torch.Tensor, + ) -> torch.Tensor: outputs = self.model(input_ids=input_ids, attention_mask=attention_mask) return outputs.logits[rows, cols] diff --git a/rnagym/fitness/baselines/RNA_FM/__init__.py b/rnagym/fitness/baselines/RNA_FM/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/rnagym/fitness/baselines/RNA_FM/score_rna_fm.sh b/rnagym/fitness/baselines/RNA_FM/score_rna_fm.sh new file mode 100644 index 0000000..68a4165 --- /dev/null +++ b/rnagym/fitness/baselines/RNA_FM/score_rna_fm.sh @@ -0,0 +1,15 @@ +#!/usr/bin/env bash +set -euo pipefail + +script_dir=$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd) +repository=$(cd -- "$script_dir/../../../.." && pwd) +checkpoint_root=${RNAGYM_CHECKPOINT_DIR:-/n/lw_groups/marks/ckpt} +data_dir=${RNAGYM_DATA_DIR:-"$repository/data"}/fitness + +rows=${1:-${SLURM_ARRAY_TASK_ID:-all}} + +cd "$repository" +python -m rnagym.fitness.baselines.RNA_FM.score_rna_fm_single_dms \ + --rows "$rows" \ + --output "$data_dir/model_predictions/rna_fm_4fill" \ + --checkpoint "$checkpoint_root/rna-fm/hub/checkpoints/RNA-FM_pretrained.pth" diff --git a/fitness/baselines/RNA_FM/score_rna_fm_single_dms.py b/rnagym/fitness/baselines/RNA_FM/score_rna_fm_single_dms.py similarity index 68% rename from fitness/baselines/RNA_FM/score_rna_fm_single_dms.py rename to rnagym/fitness/baselines/RNA_FM/score_rna_fm_single_dms.py index e516d1a..35a85db 100644 --- a/fitness/baselines/RNA_FM/score_rna_fm_single_dms.py +++ b/rnagym/fitness/baselines/RNA_FM/score_rna_fm_single_dms.py @@ -1,14 +1,12 @@ #!/usr/bin/env python3 """Score RNA-FM with the shared masked-marginal engine.""" -import sys -from pathlib import Path +import argparse import torch +from typing_extensions import override -sys.path.insert(0, str(Path(__file__).resolve().parents[1])) - -from masked_lm import MaskedLMAdapter, main # noqa: E402 +from rnagym.fitness.baselines.masked_lm import MaskedLMAdapter, main class RNAFMAdapter(MaskedLMAdapter): @@ -21,18 +19,18 @@ class RNAFMAdapter(MaskedLMAdapter): n_special_tokens = 2 @staticmethod - def add_arguments(parser): + @override + def add_arguments(parser: argparse.ArgumentParser) -> None: parser.add_argument( - "--checkpoint_path", + "--checkpoint", + dest="checkpoint_path", type=str, required=True, - help="Path to the RNA-FM_pretrained.pth weights file. Note this differs " - "from compute_fitness.py's --model_location, which is the cloned RNA-FM " - "module directory added to sys.path. Here the package is expected to be " - "installed (pip install rna-fm) and only the checkpoint is passed.", + help="RNA-FM weights (.pth)", ) - def load(self, args): + @override + def load(self, args: argparse.Namespace) -> None: # The released checkpoint pickles an argparse.Namespace, which torch>=2.6 # refuses to unpickle under the weights_only default, so that one class is # allowlisted rather than disabling the check entirely @@ -51,7 +49,14 @@ def load(self, args): self.prefix_ids = [alphabet.cls_idx] self.suffix_ids = [alphabet.eos_idx] - def logits_at(self, input_ids, attention_mask, rows, cols): + @override + def logits_at( + self, + input_ids: torch.Tensor, + attention_mask: torch.Tensor, + rows: torch.Tensor, + cols: torch.Tensor, + ) -> torch.Tensor: return self.model(input_ids)["logits"][rows, cols] diff --git a/rnagym/fitness/baselines/RiNALMo/__init__.py b/rnagym/fitness/baselines/RiNALMo/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/rnagym/fitness/baselines/RiNALMo/flash-attn.patch b/rnagym/fitness/baselines/RiNALMo/flash-attn.patch new file mode 100644 index 0000000..7f5e73a --- /dev/null +++ b/rnagym/fitness/baselines/RiNALMo/flash-attn.patch @@ -0,0 +1,11 @@ +diff --git a/rinalmo/model/attention.py b/rinalmo/model/attention.py +--- a/rinalmo/model/attention.py ++++ b/rinalmo/model/attention.py +@@ -206,5 +206,6 @@ class FlashMultiHeadSelfAttention(nn.Module): + if key_padding_mask is not None: + batch_size = qkv.shape[0] + seqlen = qkv.shape[1] +- x_unpad, indices, cu_seqlens, max_s = unpad_input(qkv, key_padding_mask) ++ # FlashAttention 2.7 adds a fifth return value unused by RiNALMo ++ x_unpad, indices, cu_seqlens, max_s = unpad_input(qkv, key_padding_mask)[:4] + output_unpad = self.flash_self_attn( diff --git a/rnagym/fitness/baselines/RiNALMo/score_rinalmo.sh b/rnagym/fitness/baselines/RiNALMo/score_rinalmo.sh new file mode 100644 index 0000000..842b947 --- /dev/null +++ b/rnagym/fitness/baselines/RiNALMo/score_rinalmo.sh @@ -0,0 +1,17 @@ +#!/usr/bin/env bash +set -euo pipefail + +script_dir=$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd) +repository=$(cd -- "$script_dir/../../../.." && pwd) +checkpoint_root=${RNAGYM_CHECKPOINT_DIR:-/n/lw_groups/marks/ckpt} +fitness_dir=$(cd -- "$script_dir/../.." && pwd) +data_dir=${RNAGYM_DATA_DIR:-"$repository/data"}/fitness +export PYTHONPATH="$fitness_dir/.pixi/model-sources/RiNALMo${PYTHONPATH:+:$PYTHONPATH}" + +rows=${1:-${SLURM_ARRAY_TASK_ID:-all}} + +cd "$repository" +python -m rnagym.fitness.baselines.RiNALMo.score_rinalmo_single_dms \ + --rows "$rows" \ + --output "$data_dir/model_predictions/rinalmo_4fill" \ + --checkpoint "$checkpoint_root/rinalmo/rinalmo_giga_pretrained.pt" diff --git a/fitness/baselines/RiNALMo/score_rinalmo_single_dms.py b/rnagym/fitness/baselines/RiNALMo/score_rinalmo_single_dms.py similarity index 74% rename from fitness/baselines/RiNALMo/score_rinalmo_single_dms.py rename to rnagym/fitness/baselines/RiNALMo/score_rinalmo_single_dms.py index 794070c..5e285ac 100644 --- a/fitness/baselines/RiNALMo/score_rinalmo_single_dms.py +++ b/rnagym/fitness/baselines/RiNALMo/score_rinalmo_single_dms.py @@ -6,14 +6,12 @@ recomputed in float32 to avoid quantized log-likelihood ratios. """ -import sys -from pathlib import Path +import argparse import torch +from typing_extensions import override -sys.path.insert(0, str(Path(__file__).resolve().parents[1])) - -from masked_lm import MaskedLMAdapter, main # noqa: E402 +from rnagym.fitness.baselines.masked_lm import MaskedLMAdapter, main class RiNALMoAdapter(MaskedLMAdapter): @@ -27,18 +25,18 @@ class RiNALMoAdapter(MaskedLMAdapter): requires_cuda = True # the giga checkpoint stores flash-attention modules @staticmethod - def add_arguments(parser): + @override + def add_arguments(parser: argparse.ArgumentParser) -> None: parser.add_argument( - "--checkpoint_path", + "--checkpoint", + dest="checkpoint_path", type=str, required=True, - help="Path to the RiNALMo giga-v1 checkpoint (.pt), e.g. " - "rinalmo_giga_pretrained.pt from the project's Zenodo record.", + help="RiNALMo giga-v1 weights (.pt)", ) - def load(self, args): - # The released checkpoint stores the flash-attention module layout, so - # flash attention must stay enabled + @override + def load(self, args: argparse.Namespace) -> None: from rinalmo.config import model_config from rinalmo.data.alphabet import Alphabet from rinalmo.model.model import RiNALMo @@ -57,7 +55,14 @@ def load(self, args): self.prefix_ids = [alphabet.cls_idx] self.suffix_ids = [alphabet.eos_idx] - def logits_at(self, input_ids, attention_mask, rows, cols): + @override + def logits_at( + self, + input_ids: torch.Tensor, + attention_mask: torch.Tensor, + rows: torch.Tensor, + cols: torch.Tensor, + ) -> torch.Tensor: with torch.autocast("cuda", dtype=torch.bfloat16): representation = self.model(input_ids)["representation"] return self.model.lm_mask_head(representation.float())[rows, cols] diff --git a/rnagym/fitness/baselines/__init__.py b/rnagym/fitness/baselines/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/rnagym/fitness/baselines/masked_lm/README.md b/rnagym/fitness/baselines/masked_lm/README.md new file mode 100644 index 0000000..d7f7306 --- /dev/null +++ b/rnagym/fitness/baselines/masked_lm/README.md @@ -0,0 +1,39 @@ +# Masked-marginal scoring + +For each mutated position, mask that base and score +`log p(mutant) - log p(wild type)`. Sum over the variant's mutations. +The four strategies differ in the context at the other mutated positions: + +| strategy | fill at the other mutated sites | score | +|:--|:--|:--| +| `wt-fill` | wild-type bases | `sum_i log p(mt_i \| x^wt_-i) - log p(wt_i \| x^wt_-i)` | +| `mask-fill` | masks | `sum_i log p(mt_i \| x^wt_-M) - log p(wt_i \| x^wt_-M)` | +| `mut-fill` | mutant bases | `sum_i log p(mt_i \| x^mt_-i) - log p(wt_i \| x^mt_-i)` | +| `match-fill` | the allele being scored | `sum_i log p(mt_i \| x^mt_-i) - log p(wt_i \| x^wt_-i)` | + +Here `M` is the set of mutated positions, `x^wt` the wild type, `x^mt` the +variant, and `x_-i` a mask at position `i`. All four agree on single mutants. + +The leaderboard uses `wt-fill`, following the [ESM example][0] and +[ProteinGym baseline][1]. The [ESM paper][2] defines the other three contexts. +One run computes all four, sharing repeated contexts across strategies. + +## Adding a model + +Subclass `MaskedLMAdapter`, declare its alphabet, token limits and batch defaults, +and implement `add_arguments`, `load` and `logits_at`. Call `runner.main`. +[RNA-FM][3] is a short example. The engine checks the declared alphabet and +special tokens against the loaded tokenizer. + +Long constructs are windowed around the masked span. A context fails if its +masked positions cannot fit within the model's sequence limit. Outputs include +one CSV per assay and a manifest with source, checkpoint arguments, batching and +hardware information. + +Run `pixi run test` from `rnagym/fitness/` for the real-assay fixture checks, +or `pixi run -e rna-fm check-published` for checkpoint reproduction. + +[0]: https://github.com/facebookresearch/esm/blob/2b369911bb5b4b0dda914521b9475cad1656b2ac/examples/variant-prediction/predict.py#L186-L225 +[1]: https://github.com/OATML-Markslab/ProteinGym/blob/144fe22b07dfaeec2b366f2346203a9838a55b4c/proteingym/baselines/esm/compute_fitness.py#L486-L514 +[2]: https://papers.nips.cc/paper_files/paper/2021/hash/f51338d736f95dd42427296047067694-Abstract.html +[3]: ../RNA_FM/score_rna_fm_single_dms.py diff --git a/fitness/baselines/masked_lm/__init__.py b/rnagym/fitness/baselines/masked_lm/__init__.py similarity index 71% rename from fitness/baselines/masked_lm/__init__.py rename to rnagym/fitness/baselines/masked_lm/__init__.py index a8a3b51..f014c0a 100644 --- a/fitness/baselines/masked_lm/__init__.py +++ b/rnagym/fitness/baselines/masked_lm/__init__.py @@ -7,10 +7,10 @@ ``runner.main``. """ -from .adapter import MaskedLMAdapter -from .engine import accumulate_scores, window_contexts -from .runner import main -from .strategies import ( +from rnagym.fitness.baselines.masked_lm.adapter import MaskedLMAdapter +from rnagym.fitness.baselines.masked_lm.engine import accumulate_scores, window_contexts +from rnagym.fitness.baselines.masked_lm.runner import main +from rnagym.fitness.baselines.masked_lm.strategies import ( MASK_CHAR, STRATEGIES, TaskTable, diff --git a/fitness/baselines/masked_lm/adapter.py b/rnagym/fitness/baselines/masked_lm/adapter.py similarity index 87% rename from fitness/baselines/masked_lm/adapter.py rename to rnagym/fitness/baselines/masked_lm/adapter.py index f2443be..2124541 100644 --- a/fitness/baselines/masked_lm/adapter.py +++ b/rnagym/fitness/baselines/masked_lm/adapter.py @@ -1,5 +1,12 @@ """Model interface for the shared masked-marginal engine.""" +from __future__ import annotations + +import argparse +from collections.abc import Sequence + +import torch + class MaskedLMAdapter: """Supply model tokenization and logits to the shared engine.""" @@ -7,7 +14,7 @@ class MaskedLMAdapter: name = "masked LM" bases = "ACGU" score_column = "score" - max_tokens = None + max_tokens: int | None = None default_batch_size = 512 default_max_batch_tokens = 65536 # Whether contexts of different lengths may share a padded batch. Current @@ -21,33 +28,25 @@ class MaskedLMAdapter: # the loaded tokenizer in ``check_alphabet`` n_special_tokens = 0 # Optional filler for architecture-required context padding - context_pad_char = None + context_pad_char: str | None = None # Whether context positions map to token positions by a constant shift, which # is what lets the engine gather without calling ``token_position`` per term # An adapter that overrides ``token_position`` non-linearly must clear this constant_token_offset = True - prefix_ids = () - suffix_ids = () + prefix_ids: Sequence[int] = () + suffix_ids: Sequence[int] = () pad_id = 0 mask_id = 0 unk_id = 0 - base_ids = None + base_ids: dict[str, int] = {} device = "cpu" @staticmethod - def add_arguments(parser): + def add_arguments(parser: argparse.ArgumentParser): """Add the model's own command line arguments, such as a checkpoint.""" raise NotImplementedError - def load(self, args): - """Construct the model and tokenizer and fill in the token ids.""" - raise NotImplementedError - - def logits_at(self, input_ids, attention_mask, rows, cols): - """Return raw logits of shape ``(positions, vocabulary)``.""" - raise NotImplementedError - def canonicalize_sequence(self, sequence: str) -> str: """Normalize whitespace, case, and the model's T or U alphabet.""" sequence = str(sequence).strip().upper() @@ -55,27 +54,6 @@ def canonicalize_sequence(self, sequence: str) -> str: return sequence.replace("U", "T") return sequence.replace("T", "U") - def encode_context(self, context: str, mask_char: str) -> list: - """Encode one token per context character plus special tokens.""" - ids = list(self.prefix_ids) - for char in context: - token_id = ( - self.mask_id - if char == mask_char - else self.base_ids.get(char, self.unk_id) - ) - ids.append(token_id) - ids.extend(self.suffix_ids) - return ids - - def context_length_for(self, length: int) -> int: - """Return the model-ready context length.""" - return length - - def token_position(self, context_position: int) -> int: - """Map a context coordinate to an input-token coordinate.""" - return len(self.prefix_ids) + context_position - def check_alphabet(self): """Fail loudly if the tokenizer does not cover the model's alphabet.""" if not isinstance(self.base_ids, dict): @@ -98,3 +76,38 @@ def check_alphabet(self): ) if self.mask_id in self.base_ids.values(): raise ValueError(f"A base maps to the mask token id: {self.base_ids}") + + def context_length_for(self, length: int) -> int: + """Return the model-ready context length.""" + return length + + def encode_context(self, context: str, mask_char: str) -> list[int]: + """Encode one token per context character plus special tokens.""" + ids = list(self.prefix_ids) + for char in context: + token_id = ( + self.mask_id + if char == mask_char + else self.base_ids.get(char, self.unk_id) + ) + ids.append(token_id) + ids.extend(self.suffix_ids) + return ids + + def load(self, args: argparse.Namespace): + """Construct the model and tokenizer and fill in the token ids.""" + raise NotImplementedError + + def logits_at( + self, + input_ids: torch.Tensor, + attention_mask: torch.Tensor, + rows: torch.Tensor, + cols: torch.Tensor, + ) -> torch.Tensor: + """Return raw logits of shape ``(positions, vocabulary)``.""" + raise NotImplementedError + + def token_position(self, context_position: int) -> int: + """Map a context coordinate to an input-token coordinate.""" + return len(self.prefix_ids) + context_position diff --git a/fitness/baselines/masked_lm/engine.py b/rnagym/fitness/baselines/masked_lm/engine.py similarity index 94% rename from fitness/baselines/masked_lm/engine.py rename to rnagym/fitness/baselines/masked_lm/engine.py index e4cb80b..663aa1a 100644 --- a/fitness/baselines/masked_lm/engine.py +++ b/rnagym/fitness/baselines/masked_lm/engine.py @@ -1,89 +1,22 @@ """Run batched inference over deduplicated masked contexts.""" +from __future__ import annotations + import numpy as np import torch from tqdm.auto import tqdm -from .strategies import MASK_CHAR, validate_table - - -def window_contexts(table, budget: int) -> None: - """Window contexts in place around their masked span.""" - if all(len(c) <= budget for c in table.contexts): - return - - # Masked span per context, from the task table rather than from the string - # The table is sorted by context, so the spans come from one grouped reduce - # rather than a pass over every term, which matters on the mRNA-coding - # assays where this path is reached at all - span_lo = {} - span_hi = {} - if table.n_terms(): - used, first = np.unique(table.ctx_id, return_index=True) - lows = np.minimum.reduceat(table.pos, first) - highs = np.maximum.reduceat(table.pos, first) - span_lo = dict(zip(used.tolist(), lows.tolist())) - span_hi = dict(zip(used.tolist(), highs.tolist())) - - shift = np.zeros(len(table.contexts), dtype=np.int64) - trimmed = list(table.contexts) - for k, context in enumerate(table.contexts): - if len(context) <= budget: - continue - if k not in span_lo: - trimmed[k] = context[:budget] - continue - low, high = span_lo[k], span_hi[k] - span = high - low + 1 - if span > budget: - raise ValueError( - f"Masked span of {span} positions does not fit in a window of " - f"{budget}. This context cannot be scored" - ) - centre = (low + high) // 2 - start = max(0, centre - budget // 2) - end = min(len(context), start + budget) - start = max(0, end - budget) - trimmed[k] = context[start:end] - shift[k] = start - - table.contexts = trimmed - if shift.any(): - table.pos = (table.pos - shift[table.ctx_id]).astype(np.int32) - validate_table(table) - - -def pad_contexts(table, adapter) -> None: - """Pad fixed-length contexts to the adapter's required length.""" - if adapter.context_pad_char is None: - return - lengths = {len(c) for c in table.contexts} - if len(lengths) != 1: - raise ValueError( - f"{adapter.name} pads contexts to a fixed length, so every context " - f"must start the same length. Found {sorted(lengths)}" - ) - length = lengths.pop() - target = adapter.context_length_for(length) - if target < length: - raise ValueError( - f"{adapter.name} asked to pad a {length} nt context down to {target}" - ) - if target == length: - return - padding = target - length - filler = adapter.context_pad_char - print( - f"Padding contexts from {length} to {target} positions with " - f"{padding} trailing {filler}" - ) - table.contexts = [context + filler * padding for context in table.contexts] - validate_table(table) +from rnagym.fitness.baselines.masked_lm.adapter import MaskedLMAdapter +from rnagym.fitness.baselines.masked_lm.strategies import ( + MASK_CHAR, + TaskTable, + validate_table, +) def accumulate_scores( - adapter, - table, + adapter: MaskedLMAdapter, + table: TaskTable, n_rows: int, batch_size: int, max_batch_tokens: int, @@ -197,3 +130,77 @@ def accumulate_scores( f"{adapter.name} produced a non-finite accumulated score" ) return scores + + +def pad_contexts(table: TaskTable, adapter: MaskedLMAdapter) -> None: + """Pad fixed-length contexts to the adapter's required length.""" + if adapter.context_pad_char is None: + return + lengths = {len(c) for c in table.contexts} + if len(lengths) != 1: + raise ValueError( + f"{adapter.name} pads contexts to a fixed length, so every context " + f"must start the same length. Found {sorted(lengths)}" + ) + length = lengths.pop() + target = adapter.context_length_for(length) + if target < length: + raise ValueError( + f"{adapter.name} asked to pad a {length} nt context down to {target}" + ) + if target == length: + return + padding = target - length + filler = adapter.context_pad_char + print( + f"Padding contexts from {length} to {target} positions with " + f"{padding} trailing {filler}" + ) + table.contexts = [context + filler * padding for context in table.contexts] + validate_table(table) + + +def window_contexts(table: TaskTable, budget: int) -> None: + """Window contexts in place around their masked span.""" + if all(len(c) <= budget for c in table.contexts): + return + + # Masked span per context, from the task table rather than from the string + # The table is sorted by context, so the spans come from one grouped reduce + # rather than a pass over every term, which matters on the mRNA-coding + # assays where this path is reached at all + span_lo: dict[int, int] = {} + span_hi: dict[int, int] = {} + if table.n_terms(): + used, first = np.unique(table.ctx_id, return_index=True) + lows = np.minimum.reduceat(table.pos, first) + highs = np.maximum.reduceat(table.pos, first) + span_lo = dict(zip(used.tolist(), lows.tolist())) + span_hi = dict(zip(used.tolist(), highs.tolist())) + + shift = np.zeros(len(table.contexts), dtype=np.int64) + trimmed = list(table.contexts) + for k, context in enumerate(table.contexts): + if len(context) <= budget: + continue + if k not in span_lo: + trimmed[k] = context[:budget] + continue + low, high = span_lo[k], span_hi[k] + span = high - low + 1 + if span > budget: + raise ValueError( + f"Masked span of {span} positions does not fit in a window of " + f"{budget}. This context cannot be scored" + ) + centre = (low + high) // 2 + start = max(0, centre - budget // 2) + end = min(len(context), start + budget) + start = max(0, end - budget) + trimmed[k] = context[start:end] + shift[k] = start + + table.contexts = trimmed + if shift.any(): + table.pos = (table.pos - shift[table.ctx_id]).astype(np.int32) + validate_table(table) diff --git a/rnagym/fitness/baselines/masked_lm/runner.py b/rnagym/fitness/baselines/masked_lm/runner.py new file mode 100644 index 0000000..012a16b --- /dev/null +++ b/rnagym/fitness/baselines/masked_lm/runner.py @@ -0,0 +1,291 @@ +"""Score assays with all four masked-marginal fill strategies.""" + +from __future__ import annotations + +import argparse +import fcntl +import hashlib +import json +import shlex +import subprocess +import sys +from collections.abc import Sequence +from pathlib import Path +from tempfile import TemporaryDirectory + +import numpy as np +import polars as pl +import torch + +from rnagym.config import ConfigFitness +from rnagym.fitness.baselines.masked_lm.adapter import MaskedLMAdapter +from rnagym.fitness.baselines.masked_lm.engine import ( + accumulate_scores, + pad_contexts, + window_contexts, +) +from rnagym.fitness.baselines.masked_lm.strategies import ( + STRATEGIES, + TaskTable, + build_tasks, + recover_wild_type, +) +from rnagym.fitness.data import read_assay, read_reference + + +def build_parser(adapter: MaskedLMAdapter) -> argparse.ArgumentParser: + """Parse the assay, output directory and checkpoint.""" + parser = argparse.ArgumentParser(description=f"Score assays with {adapter.name}") + parser.add_argument( + "--rows", default="all", help="Reference rows: all (default), 12 or 0-8,12" + ) + parser.add_argument( + "--output", type=Path, required=True, help="Prediction directory" + ) + adapter.add_arguments(parser) + return parser + + +def code_revision() -> dict[str, object]: + """Return the git revision and a hash of the scoring sources.""" + here = Path(__file__).resolve().parent + revision, dirty = "unknown", None + try: + revision_command = f"git -C {shlex.quote(str(here))} rev-parse HEAD" + revision = subprocess.run( + shlex.split(revision_command), + capture_output=True, + text=True, + check=True, + ).stdout.strip() + status_command = f"git -C {shlex.quote(str(here))} status --porcelain" + status = subprocess.run( + shlex.split(status_command), + capture_output=True, + text=True, + check=True, + ).stdout.strip() + dirty = bool(status) + except (subprocess.CalledProcessError, FileNotFoundError): + pass + + digest = hashlib.sha256() + sources = sorted(here.glob("*.py")) + entry = Path(sys.argv[0]).resolve() + if entry.is_file(): + sources.append(entry) + for path in sources: + digest.update(path.name.encode()) + digest.update(path.read_bytes()) + return { + "git_head": revision, + "git_tree_dirty": dirty, + "scoring_source_sha256": digest.hexdigest(), + "scoring_source_files": [p.name for p in sources], + } + + +def load_dms_data(dms_dir_path: str | Path, dms_id: str) -> pl.DataFrame: + """Load and validate one processed assay.""" + return read_assay(Path(dms_dir_path) / f"{dms_id}.csv") + + +def main(adapter: MaskedLMAdapter): + """Load one checkpoint and score the selected assays under all fill strategies.""" + parser = build_parser(adapter) + args = parser.parse_args() + + strategies = STRATEGIES + output_dir = args.output + output_dir.mkdir(parents=True, exist_ok=True) + + try: + reference = read_reference(ConfigFitness.REFERENCE_FILE, args.rows) + loaded = False + for dms_id, construct in reference.select( + "DMS_ID", "RAW_CONSTRUCT_SEQ" + ).iter_rows(): + dms_df = load_dms_data(ConfigFitness.ASSAY_DIR, dms_id) + missing_sequences = dms_df["sequence"].is_null() | dms_df[ + "sequence" + ].str.strip_chars().eq("") + if missing_sequences.any(): + rows = np.flatnonzero(missing_sequences.to_numpy()).tolist()[:5] + raise ValueError(f"DMS file has missing sequences at rows {rows}") + + sequences = [ + adapter.canonicalize_sequence(s) for s in dms_df["sequence"].to_list() + ] + mutants = dms_df["mutant"].to_list() + wild_type = resolve_wild_type(adapter, mutants, sequences, construct) + + table = build_tasks( + mutants, + sequences, + wild_type, + adapter.bases, + strategies, + verbose=False, + ) + n_scorable = int(table.scorable.sum()) + if not table.contexts: + raise ValueError("No scorable variants found") + + max_tokens = adapter.max_tokens + if max_tokens is not None: + # The declared count, not the loaded one: this guard runs before the + # model is loaded so that an unsupported request fails cheaply + budget = window_budget(adapter, max_tokens) + if needs_windowing(table.contexts, budget): + window_contexts(table, budget) + + device = "cuda:0" if torch.cuda.is_available() else "cpu" + if adapter.requires_cuda and not device.startswith("cuda"): + raise ValueError(f"{adapter.name} requires a CUDA device") + args.device = device + if not loaded: + adapter.load(args) + adapter.check_alphabet() + loaded = True + + pad_contexts(table, adapter) + scores = accumulate_scores( + adapter, + table, + n_rows=len(dms_df), + batch_size=adapter.default_batch_size, + max_batch_tokens=adapter.default_max_batch_tokens, + ) + + for i, strategy in enumerate(strategies): + dms_df = dms_df.with_columns( + pl.Series(f"{adapter.score_column}_{strategy}", scores[i]) + ) + multi_mutants = any( + str(mutant).count(",") > 0 + for mutant in np.asarray(mutants)[table.scorable] + ) + if not multi_mutants and not np.allclose( + scores[:, table.scorable], scores[0, table.scorable] + ): + raise ValueError("Fill strategies disagree on single mutants") + + output_file = output_dir / f"{dms_id}.csv" + manifest_file = output_dir / f"{dms_id}.manifest.json" + with TemporaryDirectory(prefix=f".{dms_id}.", dir=output_dir) as directory: + temporary = Path(directory) / output_file.name + manifest_temporary = Path(directory) / manifest_file.name + dms_df.write_csv(temporary) + write_manifest( + manifest_temporary, + temporary, + dms_id, + adapter, + args, + strategies, + table, + wild_type, + ) + # Keep both outputs from the same writer when assay jobs finish together + with (output_dir / ".write.lock").open("a") as lock: + fcntl.flock(lock, fcntl.LOCK_EX) + temporary.replace(output_file) + manifest_temporary.replace(manifest_file) + print( + f"{dms_id}: {n_scorable}/{len(sequences)} variants scored, saved to {output_file}" + ) + + # Model libraries raise unrelated exception types at this CLI boundary + except Exception as err: # noqa: BLE001 + print(f"Error: {err}", file=sys.stderr) + sys.exit(1) + + +def needs_windowing(contexts: Sequence[str], budget: int) -> bool: + """Whether any context is too long for the model's position limit.""" + return any(len(c) > budget for c in contexts) + + +def resolve_wild_type( + adapter: MaskedLMAdapter, + mutants: Sequence[str | None], + sequences: Sequence[str], + construct: str | None, +) -> str: + """Recover the assay wild type and check it against the reference sheet.""" + recovered = recover_wild_type(mutants, sequences, adapter.bases) + if construct is None: + print("Reference sheet has no RAW_CONSTRUCT_SEQ. Using the recovered wild type") + return recovered + folded = adapter.canonicalize_sequence(construct) + if folded != recovered: + raise ValueError( + "The wild type recovered from the assay disagrees with the reference " + f"sheet's RAW_CONSTRUCT_SEQ (lengths {len(recovered)} and {len(folded)}). " + "The wild-type-background strategies need one agreed background" + ) + return recovered + + +def runtime_environment(device: str | torch.device) -> dict[str, object]: + """Return the Torch, CUDA, and GPU versions used for scoring.""" + import torch + + device = torch.device(device) + device_name = torch.cuda.get_device_name(device) if device.type == "cuda" else None + return { + "torch": torch.__version__, + "cuda": getattr(torch.version, "cuda", None), + "device": str(device), + "gpu": device_name, + } + + +def window_budget(adapter: MaskedLMAdapter, max_tokens: int) -> int: + """Return the nucleotide budget after declared special tokens.""" + budget = max_tokens - adapter.n_special_tokens + if budget < 1: + raise ValueError( + f"max_tokens={max_tokens} leaves no sequence positions after " + f"{adapter.n_special_tokens} special tokens" + ) + return budget + + +def write_manifest( + path: Path, + prediction_file: Path, + dms_id: str, + adapter: MaskedLMAdapter, + args: argparse.Namespace, + strategies: Sequence[str], + table: TaskTable, + wild_type: str, +): + """Write prediction provenance next to an assay output.""" + manifest = { + "prediction_sha256": hashlib.sha256(prediction_file.read_bytes()).hexdigest(), + "dms_id": dms_id, + "environment": runtime_environment(adapter.device), + "model": adapter.name, + "batch_size": adapter.default_batch_size, + "max_batch_tokens": adapter.default_max_batch_tokens, + "max_tokens": adapter.max_tokens, + "score_column_stem": adapter.score_column, + "strategies": [s.replace("_", "-") for s in strategies], + "columns": { + s.replace("_", "-"): f"{adapter.score_column}_{s}" for s in strategies + }, + "alphabet": adapter.bases, + "wild_type_length": len(wild_type), + "context_length": len(table.contexts[0]) if table.contexts else None, + "context_pad_char": adapter.context_pad_char, + "contexts": len(table.contexts), + "terms": table.n_terms(), + "scorable_variants": int(table.scorable.sum()), + "code_revision": code_revision(), + "arguments": { + k: (str(v) if isinstance(v, Path) else v) for k, v in vars(args).items() + }, + } + Path(path).write_text(json.dumps(manifest, indent=2, sort_keys=True) + "\n") diff --git a/fitness/baselines/masked_lm/strategies.py b/rnagym/fitness/baselines/masked_lm/strategies.py similarity index 90% rename from fitness/baselines/masked_lm/strategies.py rename to rnagym/fitness/baselines/masked_lm/strategies.py index 1b58781..a05bcdf 100644 --- a/fitness/baselines/masked_lm/strategies.py +++ b/rnagym/fitness/baselines/masked_lm/strategies.py @@ -4,17 +4,20 @@ positions are zero-based context coordinates and must point at a mask. """ +from __future__ import annotations + from array import array +from collections.abc import Sequence from dataclasses import dataclass import numpy as np -import pandas as pd + +from rnagym.fitness.tasks.model_registry import STRATEGIES MASK_CHAR = "#" # Canonical order. The CLI accepts the hyphenated spelling used in the write-up # the underscored spelling is used for identifiers and output column suffixes -STRATEGIES = ("wt_fill", "mask_fill", "mut_fill", "match_fill") def normalize_strategy(name: str) -> str: @@ -29,7 +32,7 @@ def normalize_strategy(name: str) -> str: class TaskTable: """Deduplicated contexts and parallel arrays of accumulation terms.""" - contexts: list + contexts: list[str] ctx_id: np.ndarray pos: np.ndarray base: np.ndarray @@ -37,13 +40,13 @@ class TaskTable: strategy: np.ndarray sign: np.ndarray scorable: np.ndarray - strategies: tuple + strategies: tuple[str, ...] def n_terms(self) -> int: return int(self.ctx_id.size) -def parse_mutations(mutant_str: str, bases: str) -> list: +def parse_mutations(mutant_str: str, bases: str) -> list[tuple[int, str, str]]: """Return ``(position, wild base, mutant base)`` substitution tuples.""" fold_from, fold_to = ("U", "T") if "T" in bases else ("T", "U") mutations = [] @@ -59,11 +62,15 @@ def parse_mutations(mutant_str: str, bases: str) -> list: return mutations -def recover_wild_type(mutants, sequences, bases: str) -> str: +def recover_wild_type( + mutants: Sequence[str | None], sequences: Sequence[str], bases: str +) -> str: """Recover one wild type by reverting every valid variant.""" - candidates = {} + candidates: dict[str, int] = {} for mutant_str, seq in zip(mutants, sequences): - if pd.isna(mutant_str): + if mutant_str is None or ( + isinstance(mutant_str, float) and np.isnan(mutant_str) + ): continue try: mutations = parse_mutations(mutant_str, bases) @@ -84,15 +91,15 @@ def recover_wild_type(mutants, sequences, bases: str) -> str: if not candidates: raise ValueError("No variant could be reverted to a wild-type sequence") if len(candidates) > 1: - top = sorted(candidates.items(), key=lambda kv: -kv[1])[:3] + top = sorted(candidates, key=candidates.__getitem__, reverse=True)[:3] raise ValueError( "Variants imply more than one wild-type sequence " - f"({len(candidates)} distinct, top counts {[c for _, c in top]})" + f"({len(candidates)} distinct, top counts {[candidates[key] for key in top]})" ) return next(iter(candidates)) -def difference_counts(sequences, wild_type: str) -> np.ndarray: +def difference_counts(sequences: Sequence[str], wild_type: str) -> np.ndarray: """Count differences, using -1 for sequences of the wrong length.""" length = len(wild_type) counts = np.full(len(sequences), -1, dtype=np.int64) @@ -108,7 +115,7 @@ def difference_counts(sequences, wild_type: str) -> np.ndarray: return counts -def validate_table(table) -> None: +def validate_table(table: TaskTable) -> None: """Require every task position to point at a mask in its context.""" if table.n_terms() == 0: return @@ -167,7 +174,7 @@ def _single_mask(seq: str, pos: int) -> str: return seq[:pos] + MASK_CHAR + seq[pos + 1 :] -def _multi_mask(seq: str, positions) -> str: +def _multi_mask(seq: str, positions: Sequence[int]) -> str: chars = list(seq) for pos in positions: chars[pos] = MASK_CHAR @@ -175,11 +182,11 @@ def _multi_mask(seq: str, positions) -> str: def build_tasks( - mutants, - sequences, + mutants: Sequence[str | None], + sequences: Sequence[str], wild_type: str, bases: str, - strategies=STRATEGIES, + strategies: tuple[str, ...] = STRATEGIES, verbose: bool = True, ) -> TaskTable: """Build deduplicated contexts and signed log-probability terms. @@ -204,7 +211,7 @@ def build_tasks( # The wild-type single-mask contexts depend only on the position, so they are # shared by every variant and worth caching - wt_ctx_cache = {} + wt_ctx_cache: dict[int, int] = {} def wt_masked(pos: int) -> int: key = wt_ctx_cache.get(pos) @@ -227,7 +234,9 @@ def emit(context_key: int, pos: int, base: str, row: int, strategy: str, sign: i n_skipped = 0 for i, (mutant_str, seq) in enumerate(zip(mutants, sequences)): - if pd.isna(mutant_str): + if mutant_str is None or ( + isinstance(mutant_str, float) and np.isnan(mutant_str) + ): continue # wild-type row: leave as NaN try: mutations = parse_mutations(mutant_str, bases) @@ -267,7 +276,7 @@ def emit(context_key: int, pos: int, base: str, row: int, strategy: str, sign: i f"Wild type has {wild_type[pos]} at position {pos + 1}, " f"expected {wt_base}" ) - if needs_wt and n_differences[i] != len(positions): + if n_differences is not None and n_differences[i] != len(positions): raise ValueError( f"Sequence differs from the wild type at {n_differences[i]} " f"positions but declares {len(positions)} mutations, so the " @@ -292,7 +301,7 @@ def emit(context_key: int, pos: int, base: str, row: int, strategy: str, sign: i key = wt_masked(pos) emit(key, pos, mut_base, i, "wt_fill", 1) emit(key, pos, wt_base, i, "wt_fill", -1) - if "mask_fill" in strat_id: + if joint_ctx is not None: emit(joint_ctx, pos, mut_base, i, "mask_fill", 1) emit(joint_ctx, pos, wt_base, i, "mask_fill", -1) if "mut_fill" in strat_id: diff --git a/rnagym/fitness/data.py b/rnagym/fitness/data.py new file mode 100644 index 0000000..6a31dec --- /dev/null +++ b/rnagym/fitness/data.py @@ -0,0 +1,79 @@ +"""Validated tables shared by fitness scoring and evaluation.""" + +from __future__ import annotations + +import re +from collections.abc import Sequence +from pathlib import Path +from tempfile import NamedTemporaryFile + +import polars as pl + + +def parse_row_ids(specification: str) -> list[int]: + """Expand reference rows such as ``0-8,11-32`` in ascending order.""" + rows = set() + for selection in specification.split(","): + match = re.fullmatch(r"\s*(\d+)(?:-(\d+))?\s*", selection) + if match is None: + raise ValueError(f"Invalid --rows selection: {selection!r}") + start = int(match.group(1)) + end = int(match.group(2) or start) + if end < start: + raise ValueError(f"Descending --rows range: {selection!r}") + rows.update(range(start, end + 1)) + return sorted(rows) + + +def read_assay(path: str | Path) -> pl.DataFrame: + """Read an assay without dropping its wild-type or repeated measurements.""" + table = pl.read_csv(path) + require_columns(table, ("mutant", "sequence", "DMS_score"), path) + return table + + +def read_reference(path: str | Path, rows: str = "all") -> pl.DataFrame: + """Read validated metadata for all assays or selected zero-based rows.""" + table = pl.read_csv(path) + require_columns(table, ("DMS_ID", "RNA_TYPE", "RAW_CONSTRUCT_SEQ"), path) + if table.is_empty(): + raise ValueError(f"{path} has no assays") + for column in ("DMS_ID", "RNA_TYPE", "RAW_CONSTRUCT_SEQ"): + if ( + table[column].is_null().any() + or table[column].str.strip_chars().eq("").any() + ): + raise ValueError(f"{path} has missing {column} values") + if table["DMS_ID"].is_duplicated().any(): + raise ValueError(f"{path} repeats DMS_ID values") + if rows != "all": + row_ids = parse_row_ids(rows) + if row_ids[-1] >= table.height: + raise ValueError(f"Rows {row_ids} fall outside the reference sheet") + table = table[row_ids] + return table + + +def require_columns( + table: pl.DataFrame, columns: Sequence[str], path: str | Path +) -> None: + """Reject an incomplete table schema before processing its rows.""" + missing = sorted(set(columns) - set(table.columns)) + if missing: + raise ValueError(f"{path} is missing columns: {missing}") + + +def write_csv_atomically(table: pl.DataFrame, path: Path) -> None: + """Replace a CSV only after the complete table has been written.""" + path.parent.mkdir(parents=True, exist_ok=True) + permissions = path.stat().st_mode & 0o777 if path.exists() else 0o644 + with NamedTemporaryFile( + dir=path.parent, prefix=f".{path.name}.", suffix=".tmp", delete=False + ) as handle: + temporary = Path(handle.name) + try: + table.write_csv(temporary) + temporary.chmod(permissions) + temporary.replace(path) + finally: + temporary.unlink(missing_ok=True) diff --git a/rnagym/fitness/pixi.lock b/rnagym/fitness/pixi.lock new file mode 100644 index 0000000..3468328 --- /dev/null +++ b/rnagym/fitness/pixi.lock @@ -0,0 +1,14084 @@ +version: 7 +platforms: +- name: 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--- /dev/null +++ b/rnagym/fitness/pixi.toml @@ -0,0 +1,292 @@ +[workspace] +name = "rnagym-fitness" +description = "RNAGym fitness benchmark environment" +channels = ["conda-forge", "pytorch", "nvidia", "bioconda", "Paddle"] +platforms = [ + { name = "linux-64-cuda-12-9", platform = "linux-64", cuda = "12.9" }, +] + +[dependencies] +coverage = "==7.10.7" +cpuonly = { version = "*", channel = "pytorch" } +go-shfmt = "==3.13.1" +numpy = "2.*" +polars = "1.*" +pytest = "==8.4.2" +pytest-cov = "==7.0.0" +python = "3.11.*" +pytorch = { version = "2.*", channel = "pytorch" } +ruff = "==0.15.21" +scikit-learn = "1.*" +scipy = "1.*" +shellcheck = "==0.11.0" +taplo = "==0.10.0" +tqdm = "==4.69.0" +typing_extensions = ">=4.6" + +[pypi-dependencies] +pyrefly = "==1.2.0" +rnagym = { path = "../..", editable = true } + +[tasks] +analyze-fill-strategies = "python -m rnagym.fitness.tasks.analyze_fill_strategies" +check-published = "python -m rnagym.fitness.tasks.check_published" +coverage = "bash sh/coverage.sh" +fmt = "bash sh/format.sh" +leaderboard = "python -m rnagym.fitness.tasks.leaderboard" +lint = "bash sh/lint.sh" +merge = "python -m rnagym.fitness.tasks.merge_scoring_files" +test = "python -m pytest -q ../../tests/test_fitness.py" + +[feature.model] +channel-priority = "flexible" + +[feature.model.activation.env] +# Triton's cached C launchers must use the current Python ABI +TRITON_CACHE_DIR = "$PIXI_PROJECT_ROOT/.pixi/triton-cache/$PIXI_ENVIRONMENT_NAME" + +[feature.model.dependencies] +curl = "8.*" +git = "2.*" +numpy = "*" +polars = "1.*" +scipy = "*" +tqdm = "4.*" +typing_extensions = ">=4.6" + +[feature.model.pypi-dependencies] +coverage = { version = "==7.10.7", extras = ["toml"] } +rnagym = { path = "../..", editable = true } + +[feature.aido-rna.dependencies] +numpy = "1.26.*" +pandas = "2.*" +python = "3.11.*" +scipy = "1.14.*" + +[feature.aido-rna.pypi-dependencies] +flash-attn = { url = 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+triton = "==1.0.0" +wandb = "==0.13.3" + +[feature.genslm.tasks] +weights = "bash sh/fetch-weight.sh genslm" + +[feature.genslm.tasks.check-published] +cmd = "python -m rnagym.fitness.tasks.check_published genslm" +depends-on = ["weights"] + +[feature.genslm.tasks.predict] +cmd = "bash baselines/GenSLM/run_model.sh" +depends-on = ["weights"] + +[feature.ntv3.dependencies] +python = "3.11.*" + +[feature.ntv3.pypi-dependencies] +torch = "==2.6.0" +transformers = "==4.55.4" + +[feature.ntv3.tasks] +check-published = "python -m rnagym.fitness.tasks.check_published ntv3" +predict = "bash baselines/Nucleotide_Transformer/run.sh" + +[feature.orthrus.dependencies] +numpy = "1.26.*" +python = "3.10.*" +pytorch = { version = "==2.2.2", channel = "pytorch" } +pytorch-cuda = { version = "==11.8", channel = "pytorch" } + +[feature.orthrus.pypi-dependencies] +causal-conv1d = { url = "https://github.com/Dao-AILab/causal-conv1d/releases/download/v1.2.0.post2/causal_conv1d-1.2.0.post2%2Bcu118torch2.2cxx11abiFALSE-cp310-cp310-linux_x86_64.whl" } +mamba-ssm = { url = "https://github.com/state-spaces/mamba/releases/download/v1.2.0.post1/mamba_ssm-1.2.0.post1%2Bcu118torch2.2cxx11abiFALSE-cp310-cp310-linux_x86_64.whl" } +transformers = "==4.45.2" + +[feature.orthrus.tasks] +check-published = "python -m rnagym.fitness.tasks.check_published orthrus" +predict = "bash baselines/Orthrus/score_orthrus.sh" + +[feature.rna-ernie.dependencies] +# Avoid loading NumPy's MKL alongside Paddle's bundled MKL runtime +libblas = { version = "*", build = "*openblas" } +numpy = "==1.23.1" +paddlepaddle = { version = "==2.5.2", build = "py39_cpu_many_linux", channel = "Paddle" } +pip = "25.*" +protobuf = "==3.20.2" +python = "3.9.*" + +[feature.rna-ernie.pypi-dependencies] +paddlenlp = "==2.5.2" +sentencepiece = "==0.1.97" + +[feature.rna-ernie.tasks] +weights = "bash sh/fetch-weight.sh rna-ernie" + +[feature.rna-ernie.tasks.check-published] +cmd = "python -m rnagym.fitness.tasks.check_published rna-ernie" +depends-on = ["weights"] + +[feature.rna-ernie.tasks.predict] +cmd = "bash baselines/RNAERNIE/run.sh" +depends-on = ["weights"] + +[feature.rna-fm.dependencies] +numpy = "==1.26.4" +python = "3.11.*" + +[feature.rna-fm.pypi-dependencies] +rna-fm = "==0.2.2" +torch = "==2.6.0" + +[feature.rna-fm.tasks] +weights = "bash sh/fetch-weight.sh rna-fm" + +[feature.rna-fm.tasks.check-published] +cmd = "python -m rnagym.fitness.tasks.check_published rna-fm" +depends-on = ["weights"] + +[feature.rna-fm.tasks.predict] +cmd = "bash baselines/RNA_FM/score_rna_fm.sh" +depends-on = ["weights"] + +[feature.rnagenesis.dependencies] +numpy = "==1.26.4" +python = "3.11.*" +setuptools = "==83.0.0" + +[feature.rnagenesis.pypi-dependencies] +deepspeed = "==0.19.4" +huggingface-hub = "==0.36.2" +torch = "==2.6.0" +transformers = "==4.45.2" + +[feature.rnagenesis.tasks] +weights = "bash sh/fetch-weight.sh rnagenesis" + +[feature.rnagenesis.tasks.check-published] +cmd = "python -m rnagym.fitness.tasks.check_published rnagenesis" +depends-on = ["weights"] + +[feature.rnagenesis.tasks.predict] +cmd = "bash baselines/RNAGenesis/score_rnagenesis.sh" +depends-on = ["weights"] + +[feature.rinalmo.dependencies] +einops = "==0.7.0" +ml-collections = "==0.1.1" +numpy = "1.26.*" +python = "3.11.*" +scipy = "1.14.*" +setuptools = "<81" + +[feature.rinalmo.pypi-dependencies] +flash-attn = { url = "https://github.com/Dao-AILab/flash-attention/releases/download/v2.7.4.post1/flash_attn-2.7.4.post1%2Bcu12torch2.6cxx11abiFALSE-cp311-cp311-linux_x86_64.whl" } +torch = "==2.6.0" + +[feature.rinalmo.tasks] +source = "bash sh/fetch-source.sh rinalmo" +weights = "bash sh/fetch-weight.sh rinalmo" + +[feature.rinalmo.tasks.check-published] +cmd = "python -m rnagym.fitness.tasks.check_published rinalmo" +depends-on = ["source", "weights"] + +[feature.rinalmo.tasks.predict] +cmd = "bash baselines/RiNALMo/score_rinalmo.sh" +depends-on = ["source", "weights"] + +[environments] +default = { solve-group = "default" } +aido-rna = { features = ["model", "aido-rna"], no-default-feature = true } +evmutation = { features = ["model", "evmutation"], no-default-feature = true } +evo = { features = ["model", "evo"], no-default-feature = true } +evo2 = { features = ["model", "evo2"], no-default-feature = true } +genslm = { features = ["model", "genslm"], no-default-feature = true } +ntv3 = { features = ["model", "ntv3"], no-default-feature = true } +orthrus = { features = ["model", "orthrus"], no-default-feature = true } +rna-ernie = { features = ["model", "rna-ernie"], no-default-feature = true } +rna-fm = { features = ["model", "rna-fm"], no-default-feature = true } +rnagenesis = { features = ["model", "rnagenesis"], no-default-feature = true } +rinalmo = { features = ["model", "rinalmo"], no-default-feature = true } diff --git a/rnagym/fitness/sh/coverage.sh b/rnagym/fitness/sh/coverage.sh new file mode 100644 index 0000000..4a6ea4b --- /dev/null +++ b/rnagym/fitness/sh/coverage.sh @@ -0,0 +1,23 @@ +#!/usr/bin/env bash +set -euo pipefail + +script_dir=$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd) +package_dir=$(cd -- "$script_dir/.." && pwd) +repository=$(cd -- "$package_dir/../.." && pwd) + +coverage_bootstrap=$(mktemp -d) +trap 'rm -rf -- "$coverage_bootstrap"' EXIT +# Start coverage in each model environment without modifying its installation +printf '%s\n' 'import coverage' 'coverage.process_startup()' >"$coverage_bootstrap/sitecustomize.py" +export PYTHONPATH="$coverage_bootstrap:$repository${PYTHONPATH:+:$PYTHONPATH}" +export COVERAGE_FILE="$package_dir/.pixi/.coverage" +export COVERAGE_RCFILE="$repository/pyproject.toml" +cd "$repository" +python -m coverage erase +python -m coverage run -m pytest -q tests/test_fitness.py +status=0 +python -m coverage run -m rnagym.fitness.tasks.check_published "$@" || status=$? +python -m coverage combine +python -m coverage report --show-missing --fail-under=80 || status=$? +python -m coverage xml -o "$package_dir/.pixi/coverage.xml" +exit "$status" diff --git a/rnagym/fitness/sh/fetch-source.sh b/rnagym/fitness/sh/fetch-source.sh new file mode 100755 index 0000000..cbc75e0 --- /dev/null +++ b/rnagym/fitness/sh/fetch-source.sh @@ -0,0 +1,49 @@ +#!/usr/bin/env bash +set -euo pipefail + +project_root=$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")/.." && pwd) +patch_file="$project_root/baselines/RiNALMo/flash-attn.patch" + +case ${1:-} in +rinalmo) + url=https://github.com/lbcb-sci/RiNALMo.git + revision=2c2c5c14a5ae609d8c560a5d9ca32e51e0288955 + destination="$project_root/.pixi/model-sources/RiNALMo" + ;; +*) + echo "unknown source: ${1:-}" >&2 + exit 2 + ;; +esac + +is_revision() { + [[ -d $destination/.git ]] && + [[ $(git -C "$destination" rev-parse HEAD 2>/dev/null) == "$revision" ]] +} + +if is_revision; then + if ! git -C "$destination" apply --reverse --check "$patch_file" 2>/dev/null; then + git -C "$destination" apply "$patch_file" + fi + exit 0 +fi +if [[ -e $destination ]]; then + echo "$destination exists but is not revision $revision" >&2 + exit 1 +fi + +parent=$(dirname -- "$destination") +mkdir -p "$parent" +temporary=$(mktemp -d "$parent/.RiNALMo.XXXXXX") +trap 'rm -rf -- "$temporary"' EXIT + +git clone --no-checkout "$url" "$temporary" +git -C "$temporary" checkout --detach "$revision" +test "$(git -C "$temporary" rev-parse HEAD)" = "$revision" +git -C "$temporary" apply "$patch_file" + +if ! mv -T -- "$temporary" "$destination" 2>/dev/null && ! is_revision; then + echo "Could not install RiNALMo source at $destination" >&2 + exit 1 +fi +trap - EXIT diff --git a/rnagym/fitness/sh/fetch-weight.sh b/rnagym/fitness/sh/fetch-weight.sh new file mode 100755 index 0000000..336debd --- /dev/null +++ b/rnagym/fitness/sh/fetch-weight.sh @@ -0,0 +1,74 @@ +#!/usr/bin/env bash +set -euo pipefail + +# shellcheck source=../sh/weights.sh +source "$(dirname -- "${BASH_SOURCE[0]}")/../../sh/weights.sh" + +case ${1:-} in +genslm) + destination="$checkpoint_root/genslm/2.5B/patric_2.5b_epoch00_val_los_0.29_bias_removed.pt" + if ! printf '%s %s\n' b13645dc523854a5f811d9a50bd747b611310d85d41a5498a9d7f93cea205f9d "$destination" | sha256sum --check --status 2>/dev/null; then + echo "GenSLM publishes its 2.5B checkpoint through authenticated Globus:" >&2 + echo "https://app.globus.org/file-manager?origin_id=25918ad0-2a4e-4f37-bcfc-8183b19c3150" >&2 + echo "A verified copy is required at $destination" >&2 + exit 1 + fi + ;; +rna-ernie) + destination="$checkpoint_root/rna-ernie/checkpoint_final" + download \ + 'https://drive.usercontent.google.com/download?id=1MQjtnrtssoF5qAiALakaDDBnQfy0PJC4&export=download&confirm=t' \ + "$destination/model_state.pdparams" \ + e5f17a98af4f1051cabc929b4b1e7d20a8bc8f989b296ef1b515e19b19a3867b + download \ + https://raw.githubusercontent.com/soberTTT/RNAErnie-1/b5e4c1cfdc6f53101111823abaddcab00c43b0d3/output/BERT%2CERNIE%2CMOTIF%2CPROMPT/checkpoint_final/model_config.json \ + "$destination/model_config.json" \ + 4fe00781c61d30151a797e902dbbc68d9c3937436b9f67fa478c563f4b6d8d2b + ;; +rna-fm) + download \ + https://huggingface.co/cuhkaih/rnafm/resolve/main/RNA-FM_pretrained.pth \ + "$checkpoint_root/rna-fm/hub/checkpoints/RNA-FM_pretrained.pth" \ + 5b5d7d87b37c291ef42c140ef9edf7aea29f255fa2a4fd435f776c52e93d5e99 + ;; +rnagenesis) + destination="$checkpoint_root/rnagenesis" + if [[ -s $destination/config.json && + -s $destination/configuration_xtrimopglm.py && + -s $destination/modeling_xtrimopglm.py && + -s $destination/pytorch_model.bin && + -s $destination/tokenizer.model ]] && + printf '%s %s\n' \ + 7a690346ab3866ebdc361fb5d7389ecf6e3e8fe62f50868d28a5bcbff52ecd20 \ + "$destination/quantization.py" | sha256sum --check --status 2>/dev/null; then + exit 0 + fi + if [[ -e $destination ]]; then + echo "$destination exists but is not a complete RNAGenesis checkpoint" >&2 + exit 1 + fi + temporary=$(mktemp -d "$checkpoint_root/.rnagenesis.XXXXXX") + trap 'rm -rf -- "$temporary"' EXIT + chmod 2775 "$temporary" + hf download Zaixi/RNAGenesis \ + --revision d8a42130984cbf04f6a5e16a3aa0c0d6578036a8 \ + --local-dir "$temporary" + # RNAGenesis imports this xTrimoPGLM file but omits it from its model repo + download \ + https://huggingface.co/biomap-research/xtrimopglm-1b-mlm/resolve/676c1bcb9f737c166a25587f83fd580610acb1ad/quantization.py \ + "$temporary/quantization.py" \ + 7a690346ab3866ebdc361fb5d7389ecf6e3e8fe62f50868d28a5bcbff52ecd20 + mv -T -- "$temporary" "$destination" + trap - EXIT + ;; +rinalmo) + download \ + https://zenodo.org/api/records/15043668/files/rinalmo_giga_pretrained.pt/content \ + "$checkpoint_root/rinalmo/rinalmo_giga_pretrained.pt" \ + cd93c3f21eb3e767373c9491192686b5846247bd1110693e453c1dd0f321c0db + ;; +*) + echo "unknown weights: ${1:-}" >&2 + exit 2 + ;; +esac diff --git a/rnagym/fitness/sh/format.sh b/rnagym/fitness/sh/format.sh new file mode 100644 index 0000000..42d3ea1 --- /dev/null +++ b/rnagym/fitness/sh/format.sh @@ -0,0 +1,10 @@ +#!/usr/bin/env bash +set -euo pipefail + +script_dir=$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd) +cd "$script_dir/.." + +taplo format pixi.toml ../../pyproject.toml +ruff check --fix --extend-select I . ../../tests/*fitness*.py ../config.py +ruff format . ../../tests/*fitness*.py ../config.py +shfmt -w ../sh sh baselines/*/*.sh diff --git a/rnagym/fitness/sh/lint.sh b/rnagym/fitness/sh/lint.sh new file mode 100644 index 0000000..2e996ae --- /dev/null +++ b/rnagym/fitness/sh/lint.sh @@ -0,0 +1,12 @@ +#!/usr/bin/env bash +set -euo pipefail + +script_dir=$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd) +cd "$script_dir/.." + +taplo lint pixi.toml ../../pyproject.toml +ruff check --extend-select I . ../../tests/*fitness*.py ../config.py +ruff format --check . ../../tests/*fitness*.py ../config.py +pyrefly check --python-interpreter-path "$PWD/.pixi/envs/default/bin/python" . +shfmt -d ../sh sh baselines/*/*.sh +shellcheck ../sh/*.sh sh/*.sh baselines/*/*.sh diff --git a/rnagym/fitness/tasks/__init__.py b/rnagym/fitness/tasks/__init__.py new file mode 100644 index 0000000..213ade0 --- /dev/null +++ b/rnagym/fitness/tasks/__init__.py @@ -0,0 +1 @@ +"""RNAGym fitness benchmark tasks.""" diff --git a/rnagym/fitness/tasks/analyze_fill_strategies.py b/rnagym/fitness/tasks/analyze_fill_strategies.py new file mode 100644 index 0000000..b997eea --- /dev/null +++ b/rnagym/fitness/tasks/analyze_fill_strategies.py @@ -0,0 +1,183 @@ +#!/usr/bin/env python3 +"""Compare the four masked-marginal fill strategies across checkpoints.""" + +from __future__ import annotations + +import argparse +from collections.abc import Sequence +from pathlib import Path +from typing import cast + +import numpy as np +import polars as pl +from scipy import stats + +from rnagym.config import ConfigFitness +from rnagym.fitness.data import read_reference +from rnagym.fitness.tasks.model_registry import ( + ASSAY_GROUPS, + FOUR_FILL_MODELS, + SCORE_COLS, + STRATEGIES, + resolve_source, +) + +NCRNA = ASSAY_GROUPS["ncRNA"] + + +def checkpoints(): + """Group the registry's four-fill entries by checkpoint.""" + grouped: dict[str, dict[str, str]] = {} + for entry in FOUR_FILL_MODELS: + for strategy in STRATEGIES: + suffix = f"_{strategy}" + if entry.endswith(suffix): + grouped.setdefault(entry[: -len(suffix)], {})[strategy] = entry + break + incomplete = { + name: sorted(set(STRATEGIES) - set(entries)) + for name, entries in grouped.items() + if set(entries) != set(STRATEGIES) + } + if incomplete: + raise ValueError(f"Incomplete four-fill registry entries: {incomplete}") + return grouped + + +def collect(expected: Sequence[str], rna_type: dict[str, str]): + """Per-assay signed Spearman for every checkpoint and strategy.""" + records = [] + for name, entries in checkpoints().items(): + columns: dict[str, str] = {} + folders: set[str] = set() + for strategy, entry in entries.items(): + folder, columns[strategy] = resolve_source(SCORE_COLS, entry) + folders.add(folder) + if len(folders) != 1: + raise ValueError(f"{name}: its strategies are registered across {folders}") + directory = ConfigFitness.PREDICTION_DIR / folders.pop() + if not directory.is_dir(): + continue + found = sorted( + path.stem for path in directory.glob("*.csv") if path.stem in expected + ) + if set(found) != set(expected): + missing = sorted(set(expected) - set(found)) + print( + f"{name:18s} incomplete: {len(found)}/{len(expected)} assays, " + f"missing {missing[:4]}" + ) + raise FileNotFoundError(f"{name}: missing assay predictions: {missing}") + for assay in found: + assay_path = directory / f"{assay}.csv" + frame = pl.read_csv(assay_path) + absent = [s for s, c in columns.items() if c not in frame.columns] + if absent: + raise KeyError(f"{assay_path} has no columns for {absent}") + # Every strategy must cover the same variants, or the comparison + # between them is confounded by coverage rather than by method + values = { + strategy: frame[column].cast(pl.Float64).to_numpy() + for strategy, column in columns.items() + } + infinite = [ + strategy + for strategy, strategy_values in values.items() + if np.isinf(strategy_values).any() + ] + if infinite: + raise ValueError(f"{assay_path} has infinite scores for {infinite}") + masks = { + strategy: np.isfinite(strategy_values) + for strategy, strategy_values in values.items() + } + if not all( + np.array_equal(mask, masks["wt_fill"]) + for strategy, mask in masks.items() + if strategy != "wt_fill" + ): + raise ValueError(f"{assay_path}: strategies cover different variants") + dms_score = frame["DMS_score"].cast(pl.Float64).to_numpy() + if np.isinf(dms_score).any(): + raise ValueError(f"{assay_path} has infinite DMS scores") + for strategy in STRATEGIES: + usable = np.isfinite(dms_score) & masks[strategy] + if usable.sum() < 3: + raise ValueError(f"{assay}: only {usable.sum()} usable variants") + rho = stats.spearmanr( + dms_score[usable], values[strategy][usable] + ).correlation + if not np.isfinite(rho): + raise ValueError(f"{assay}: Spearman undefined for {strategy}") + records.append( + { + "model": name, + "strategy": strategy, + "assay": assay, + "RNA_TYPE": rna_type[assay], + "n": int(usable.sum()), + "spearman": rho, + } + ) + return pl.DataFrame(records) + + +def macro_table(per_assay: pl.DataFrame): + rows = [] + for (model, strategy), group in per_assay.sort("model", "strategy").group_by( + "model", "strategy", maintain_order=True + ): + by_type = { + t: cast( + float | None, group.filter(pl.col("RNA_TYPE") == t)["spearman"].mean() + ) + for t in NCRNA + } + rows.append( + { + "model": model, + "strategy": strategy, + **by_type, + "macro_3ncRNA": float( + np.mean([value for value in by_type.values() if value is not None]) + ) + if all(value is not None for value in by_type.values()) + else None, + } + ) + return pl.DataFrame(rows) + + +def main(argv: Sequence[str] | None = None): + """Compare masked-marginal fill strategies.""" + args = parse_args(argv) + ref = read_reference(ConfigFitness.REFERENCE_FILE) + rna_type = dict(ref.select("DMS_ID", "RNA_TYPE").iter_rows()) + expected = sorted(d for d, t in rna_type.items() if t in NCRNA) + + per_assay = collect(expected, rna_type) + if per_assay.is_empty(): + raise SystemExit("No complete checkpoint found in the predictions folder") + macro = macro_table(per_assay) + wide = ( + macro.pivot(on="strategy", index="model", values="macro_3ncRNA") + .select("model", *STRATEGIES) + .sort("wt_fill", descending=True) + ) + args.output.mkdir(parents=True, exist_ok=True) + per_assay.write_csv(args.output / "fill_strategy_per_assay.csv") + macro.write_csv(args.output / "fill_strategy_macro.csv") + with pl.Config(tbl_rows=-1, float_precision=4): + print(wide) + print(f"Saved fill-strategy metrics to {args.output}") + + +def parse_args(argv: Sequence[str] | None = None): + """Parse command-line arguments.""" + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--output", type=Path, default=ConfigFitness.REPORT_DIR) + return parser.parse_args(argv) + + +if __name__ == "__main__": + main() diff --git a/rnagym/fitness/tasks/check_published.py b/rnagym/fitness/tasks/check_published.py new file mode 100644 index 0000000..ffe7c7d --- /dev/null +++ b/rnagym/fitness/tasks/check_published.py @@ -0,0 +1,638 @@ +"""Reproduce fitness inference and the published ncRNA leaderboard.""" + +from __future__ import annotations + +import argparse +import hashlib +import importlib.metadata +import json +import os +import platform +import shlex +import shutil +import signal +import subprocess +import sys +import tempfile +import time +from collections.abc import Iterator, Sequence +from contextlib import suppress +from dataclasses import dataclass +from pathlib import Path +from typing import Any + +import numpy as np +import polars as pl +from polars.testing import assert_frame_equal +from scipy.stats import spearmanr + +from rnagym.config import ConfigFitness +from rnagym.fitness.data import read_reference +from rnagym.fitness.tasks.merge_scoring_files import standardize_mutation +from rnagym.fitness.tasks.model_registry import ( + ASSAY_GROUPS, + CHECKPOINT_MODELS, + FOUR_FILL_SPECS, + SCORE_COLS, + STRATEGIES, + resolve_source, +) + +CHECK_ROWS = 128 +MIN_CORRELATION = 0.95 +MODEL_OVERRIDE_VARIABLES = ( + "AIDO_RNA_MODEL_NAME", + "AIDO_RNA_PREDICTION_NAME", + "EVO2_MODEL_NAME", + "EVO2_LOCAL_PATH", + "EVO2_PREDICTION_NAME", + "EVO_MODEL_NAME", + "EVO_PREDICTION_NAME", + "NTV3_MODEL_NAME", + "NTV3_PREDICTION_NAME", + "ORTHRUS_MODEL_NAME", +) + + +@dataclass(frozen=True) +class Comparison: + """Summary of one generated and published prediction comparison.""" + + exact: bool + max_absolute_difference: float + max_metric_difference: float + rows: int + normalized_error: float + minimum_correlation: float + + +@dataclass(frozen=True) +class ModelFamily: + """Prediction launcher, published checkpoints and native scoring test.""" + + launcher: str + checkpoints: tuple[tuple[str, str | None], ...] + model_variable: str | None = None + prediction_variable: str | None = None + native_test: str | None = None + + +MODEL_FAMILIES = { + "aido-rna": ModelFamily( + launcher="baselines/AIDO_RNA/score_aido_rna.sh", + checkpoints=( + ("aido_rna_1m", "genbio-ai/AIDO.RNA-1M-MARS"), + ("aido_rna_25m", "genbio-ai/AIDO.RNA-25M-MARS"), + ("aido_rna_300m", "genbio-ai/AIDO.RNA-300M-MARS"), + ("aido_rna_650m", "genbio-ai/AIDO.RNA-650M"), + ("aido_rna", "genbio-ai/AIDO.RNA-1.6B"), + ), + model_variable="AIDO_RNA_MODEL_NAME", + prediction_variable="AIDO_RNA_PREDICTION_NAME", + ), + "evmutation": ModelFamily( + "baselines/EVmutation/run.sh", (), native_test="test_evmutation_scoring" + ), + "evo": ModelFamily( + launcher="baselines/Evo/score_evo.sh", + checkpoints=( + ("evo1", "evo-1-131k-base"), + ("evo1.5", "evo-1.5-8k-base"), + ), + model_variable="EVO_MODEL_NAME", + prediction_variable="EVO_PREDICTION_NAME", + ), + "evo2": ModelFamily( + launcher="baselines/Evo/score_evo2.sh", + checkpoints=( + ("evo2_1b_base", "evo2_1b_base"), + ("evo2", "evo2_7b"), + ("evo2_20b", "evo2_20b"), + ("evo2_40b", "evo2_40b"), + ), + model_variable="EVO2_MODEL_NAME", + prediction_variable="EVO2_PREDICTION_NAME", + ), + "genslm": ModelFamily( + "baselines/GenSLM/run_model.sh", + (("GenSLM", None),), + native_test="test_genslm_scoring", + ), + "ntv3": ModelFamily( + launcher="baselines/Nucleotide_Transformer/run.sh", + checkpoints=( + ("ntv3_8m", "InstaDeepAI/NTv3_8M_pre"), + ("ntv3_100m", "InstaDeepAI/NTv3_100M_pre"), + ("ntv3_650m", "InstaDeepAI/NTv3_650M_pre"), + ), + model_variable="NTV3_MODEL_NAME", + prediction_variable="NTV3_PREDICTION_NAME", + ), + "orthrus": ModelFamily("baselines/Orthrus/score_orthrus.sh", (("orthrus", None),)), + "rna-ernie": ModelFamily("baselines/RNAERNIE/run.sh", (("RNAErnie", None),)), + "rna-fm": ModelFamily("baselines/RNA_FM/score_rna_fm.sh", (("rna_fm", None),)), + "rnagenesis": ModelFamily( + "baselines/RNAGenesis/score_rnagenesis.sh", (("rnagenesis", None),) + ), + "rinalmo": ModelFamily("baselines/RiNALMo/score_rinalmo.sh", (("rinalmo", None),)), +} + + +def check_leaderboard(directory: Path, deadline: float) -> None: + """Rebuild all 31 ncRNA assays and compare every published category and model.""" + merged, reports = directory / "merged", directory / "reports" + run_command( + f"{shlex.quote(sys.executable)} -m rnagym.fitness.tasks.merge_scoring_files --output {shlex.quote(str(merged))}", + ConfigFitness.REPO_DIR, + deadline, + ) + run_command( + f"{shlex.quote(sys.executable)} -m rnagym.fitness.tasks.leaderboard --input {shlex.quote(str(merged))} --output {shlex.quote(str(reports))}", + ConfigFitness.REPO_DIR, + deadline, + ) + for name in ( + "leaderboard_signed_3ncRNA.csv", + "leaderboard_evmutation.csv", + "evmutation_coverage.csv", + ): + assert_frame_equal( + pl.read_csv(reports / name), + pl.read_csv(ConfigFitness.LEADERBOARD_DIR / name), + check_exact=False, + rel_tol=0, + abs_tol=1e-10, + ) + print("Both leaderboards and EVmutation coverage matched", flush=True) + + +def compare_predictions( + actual_file: Path, + expected_file: Path, + score_columns: tuple[str, ...], +) -> Comparison: + """Require matching rows and score ranks, reporting magnitude and fitness drift.""" + actual = pl.read_csv(actual_file) + expected = pl.read_csv(expected_file) + required = ("mutant", "sequence", *score_columns) + for path, table in ((actual_file, actual), (expected_file, expected)): + missing = sorted(set(required) - set(table.columns)) + if missing: + raise ValueError(f"{path} is missing columns: {missing}") + + actual = actual.with_columns(standardize_mutation("mutant")) + expected = expected.with_columns(standardize_mutation("mutant")) + for column in ("mutant", "sequence"): + actual_values = actual.get_column(column).cast(pl.String).to_list() + expected_values = expected.get_column(column).cast(pl.String).to_list() + if actual_values != expected_values: + if len(actual_values) != len(expected_values): + raise AssertionError( + f"{column} row count differs: produced {len(actual_values)}, " + f"published {len(expected_values)}" + ) + row = next( + index + for index, values in enumerate(zip(actual_values, expected_values)) + if values[0] != values[1] + ) + raise AssertionError( + f"{column} differs at row {row}: produced {actual_values[row]!r}, " + f"published {expected_values[row]!r}" + ) + + exact = True + max_metric_difference = 0.0 + normalized_error = 0.0 + minimum_correlation = 1.0 + if actual.is_empty() or not score_columns: + raise AssertionError("Prediction comparison requires rows and score columns") + max_absolute_difference = 0.0 + for column in score_columns: + actual_values = actual.get_column(column).cast(pl.Float64).to_numpy() + expected_values = expected.get_column(column).cast(pl.Float64).to_numpy() + if np.isinf(actual_values).any() or np.isinf(expected_values).any(): + raise AssertionError(f"{column} contains an infinite score") + + actual_missing = np.isnan(actual_values) + expected_missing = np.isnan(expected_values) + if not np.array_equal(actual_missing, expected_missing): + row = int(np.flatnonzero(actual_missing != expected_missing)[0]) + mutant = actual.get_column("mutant")[row] + raise AssertionError( + f"{column} missing-value status differs for {mutant!r} at row {row}" + ) + + finite = ~actual_missing + if finite.sum() < 3: + raise AssertionError(f"{column} has fewer than three finite scores") + differences = np.abs(actual_values[finite] - expected_values[finite]) + expected_scale = float(np.std(expected_values[finite])) + if expected_scale == 0: + raise AssertionError(f"{column}: published scores have no variation") + if differences.size: + max_absolute_difference = max( + max_absolute_difference, float(differences.max()) + ) + column_exact = np.array_equal(actual_values, expected_values, equal_nan=True) + exact = exact and column_exact + error = float(np.sqrt(np.mean(differences**2)) / expected_scale) + if np.ptp(actual_values[finite]) == 0: + raise AssertionError( + f"{column}: normalized error {error:.6g}, constant predictions" + ) + correlation = float( + spearmanr(actual_values[finite], expected_values[finite]).statistic + ) + if ( + not np.isfinite(error) + or not np.isfinite(correlation) + or correlation < MIN_CORRELATION + ): + raise AssertionError( + f"{column}: normalized error {error:.6g}, rank correlation " + f"{correlation:.6g} (minimum {MIN_CORRELATION})" + ) + normalized_error = max(normalized_error, error) + minimum_correlation = min(minimum_correlation, correlation) + measurement = "DMS_score" if "DMS_score" in expected else "dms_score" + if measurement not in expected: + raise ValueError("Published predictions are missing measured fitness") + measured = expected[measurement].cast(pl.Float64).to_numpy()[finite] + if not np.isfinite(measured).all(): + raise AssertionError("Published DMS scores must be finite") + if np.std(measured) > 0: + published_metric = spearmanr(measured, expected_values[finite]).statistic + actual_metric = spearmanr(measured, actual_values[finite]).statistic + metric_difference = abs(actual_metric - published_metric) + max_metric_difference = max(max_metric_difference, metric_difference) + return Comparison( + exact=exact, + max_absolute_difference=max_absolute_difference, + max_metric_difference=max_metric_difference, + rows=actual.height, + normalized_error=normalized_error, + minimum_correlation=minimum_correlation, + ) + + +def fingerprint() -> str: + """Hash scoring code, native checks, fixtures and the dependency lock.""" + digest = hashlib.sha256() + paths = { + ConfigFitness.REPO_DIR / "pyproject.toml", + ConfigFitness.REPO_DIR / "rnagym/config.py", + } + paths.update((ConfigFitness.REPO_DIR / "rnagym/sh").glob("*.sh")) + paths.update((ConfigFitness.REPO_DIR / "tests").glob("*fitness*.py")) + paths.update( + path + for path in (ConfigFitness.REPO_DIR / "tests/fixtures/fitness").rglob("*") + if path.is_file() + ) + for pattern in ( + "*.py", + "pixi.*", + "sh/*.sh", + "tasks/*.py", + "baselines/*/*.py", + "baselines/*/*.patch", + "baselines/*/*.sh", + "baselines/*/src/*", + "baselines/masked_lm/*.py", + ): + paths.update(path for path in ConfigFitness.DIR.glob(pattern) if path.is_file()) + for path in sorted(paths): + digest.update(str(path.relative_to(ConfigFitness.REPO_DIR)).encode()) + digest.update(path.read_bytes()) + return digest.hexdigest() + + +def main(argv: Sequence[str] | None = None) -> None: + """Require all requested checks to finish before reporting success.""" + args = parse_args(argv) + if args.timeout <= 0 or not np.isfinite(args.timeout): + raise ValueError("--timeout must be finite and positive") + if args.environment and args.leaderboard_only: + raise ValueError("--leaderboard-only cannot select a model environment") + started = time.monotonic() + deadline = started + args.timeout + report: dict[str, Any] = { + "status": "running", + "scope": args.environment + or ("leaderboard" if args.leaderboard_only else "all"), + "code_sha256": fingerprint(), + "environment": {}, + "checks": [], + } + args.report.parent.mkdir(parents=True, exist_ok=True) + # Invalidate an earlier success even if this process is killed without cleanup + args.report.write_text(json.dumps(report, indent=2) + "\n") + # Forward parent timeouts through the child process groups before exiting + previous_handler = signal.signal(signal.SIGTERM, signal.default_int_handler) + try: + report["environment"] = runtime_versions() + if args.environment: + family = MODEL_FAMILIES[args.environment] + if family.native_test: + run_command( + f"{shlex.quote(sys.executable)} -m pytest -q tests/test_fitness.py::{family.native_test}", + ConfigFitness.REPO_DIR, + deadline, + ) + report["scoring"] = {args.environment: "passed"} + if family.checkpoints: + report["checks"].extend(run_one(args.environment, deadline)) + else: + if not args.leaderboard_only: + models = [ + "RNA-FM" if name == "rna_fm" else name + for family in MODEL_FAMILIES.values() + for name, _ in family.checkpoints + ] + if sorted(models) != sorted(CHECKPOINT_MODELS): + raise AssertionError( + "Reproduction must cover every leaderboard checkpoint exactly once" + ) + with tempfile.TemporaryDirectory(prefix="rnagym-reproduce-") as directory: + root = Path(directory) + check_leaderboard(root, deadline) + report["leaderboard"] = "matched" + if not args.leaderboard_only: + for environment in MODEL_FAMILIES: + validate_family(environment) + for environment in MODEL_FAMILIES: + child_report = root / f"{environment}.json" + try: + run_command( + f"pixi run --locked -e {environment} check-published --timeout {deadline - time.monotonic()} --report {shlex.quote(str(child_report))}", + ConfigFitness.DIR, + deadline, + ) + finally: + if child_report.is_file(): + child = json.loads(child_report.read_text()) + report["checks"].extend(child["checks"]) + report.setdefault("scoring", {}).update( + child.get("scoring", {}) + ) + report.setdefault("families", {})[environment] = child + child = json.loads(child_report.read_text()) + if child["status"] != "passed": + raise AssertionError(f"{environment} did not pass") + if report["code_sha256"] != fingerprint(): + raise AssertionError("Code or dependencies changed during reproduction") + report["status"] = "passed" + except BaseException as error: + report.update(status="failed", error=f"{type(error).__name__}: {error}") + raise + finally: + signal.signal(signal.SIGTERM, previous_handler) + report["seconds"] = time.monotonic() - started + args.report.write_text(json.dumps(report, indent=2) + "\n") + print(f"Reproduction passed in {report['seconds']:.1f}s: {args.report}") + + +def parse_args(argv: Sequence[str] | None = None) -> argparse.Namespace: + """Parse the family, total runtime bound and persistent result report.""" + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("environment", nargs="?", choices=tuple(MODEL_FAMILIES)) + parser.add_argument("--leaderboard-only", action="store_true") + parser.add_argument( + "--timeout", + type=float, + default=3600, + help="Total runtime limit in seconds, including child processes", + ) + parser.add_argument( + "--report", + type=Path, + default=Path(tempfile.gettempdir()) / "rnagym-reproduction.json", + ) + return parser.parse_args(argv) + + +def prediction_layout(registry_name: str) -> tuple[str, tuple[str, ...]]: + """Return the published folder and every score column to compare.""" + if registry_name in FOUR_FILL_SPECS: + folder, column_stem = FOUR_FILL_SPECS[registry_name] + columns = tuple(f"{column_stem}_{strategy}" for strategy in STRATEGIES) + return folder, columns + folder, column = resolve_source(SCORE_COLS, registry_name) + return folder, (column,) + + +def prepare_fixture( + source_data_dir: Path, temporary_data_dir: Path +) -> dict[str, tuple[int, list[int]]]: + """Keep the complete reference sheet and its original assay row indices.""" + reference_file = source_data_dir / "reference_sheet_final.csv" + assay_dir = temporary_data_dir / "assays" + assay_dir.mkdir(parents=True) + shutil.copy2(reference_file, temporary_data_dir / reference_file.name) + selections = {} + for assay_id, row_id in selected_assays(reference_file).items(): + assay = pl.read_csv(source_data_dir / "assays" / f"{assay_id}.csv").drop_nulls( + "mutant" + ) + rows = select_rows(assay, CHECK_ROWS) + assay[rows].write_csv(assay_dir / f"{assay_id}.csv") + selections[assay_id] = row_id, rows + return selections + + +def run_command( + command: str, cwd: Path, deadline: float, environment: dict[str, str] | None = None +) -> None: + """Run a command within the suite deadline, terminating its process group on timeout.""" + remaining = deadline - time.monotonic() + if remaining <= 0: + raise TimeoutError("Reproduction deadline exceeded") + with subprocess.Popen( + shlex.split(command), cwd=cwd, env=environment, start_new_session=True + ) as process: + try: + code = process.wait(timeout=remaining) + except BaseException: + with suppress(ProcessLookupError): + os.killpg(process.pid, signal.SIGTERM) + try: + process.wait(timeout=10) + except subprocess.TimeoutExpired: + with suppress(ProcessLookupError): + os.killpg(process.pid, signal.SIGKILL) + process.wait() + raise + if code: + raise subprocess.CalledProcessError(code, command) + + +def run_one(environment: str, deadline: float) -> Iterator[dict[str, object]]: + """Exercise the real launcher on distinct original reference-sheet rows.""" + validate_family(environment) + family = MODEL_FAMILIES[environment] + with tempfile.TemporaryDirectory(prefix="rnagym-check-published-") as directory: + temporary_root = Path(directory) + temporary_data_dir = temporary_root / "fitness" + selections = prepare_fixture(ConfigFitness.DATA_DIR, temporary_data_dir) + process_environment = os.environ.copy() + for variable in MODEL_OVERRIDE_VARIABLES: + process_environment.pop(variable, None) + process_environment["RNAGYM_DATA_DIR"] = str(temporary_root) + launcher = ConfigFitness.DIR / family.launcher + for registry_name, model_name in family.checkpoints: + folder, columns = prediction_layout(registry_name) + if family.model_variable: + if model_name is None: + raise ValueError(f"{registry_name} has no checkpoint name") + process_environment[family.model_variable] = model_name + if family.prediction_variable: + process_environment[family.prediction_variable] = folder + row_selection = ",".join(str(row) for row, _ in selections.values()) + print(f"Checking {registry_name}: {len(selections)} assays", flush=True) + started = time.monotonic() + run_command( + f"bash {shlex.quote(str(launcher))} {shlex.quote(row_selection)}", + ConfigFitness.REPO_DIR, + deadline, + process_environment, + ) + checkpoint_seconds = time.monotonic() - started + for assay_id, (row_id, rows) in selections.items(): + produced = ( + temporary_data_dir + / "model_predictions" + / folder + / f"{assay_id}.csv" + ) + if not produced.is_file(): + raise FileNotFoundError( + f"Launcher did not create the selected assay: {produced}" + ) + published = pl.read_csv( + ConfigFitness.PREDICTION_DIR / folder / f"{assay_id}.csv" + ).drop_nulls("mutant") + expected_file = temporary_root / "expected.csv" + published[rows].write_csv(expected_file) + try: + comparison = compare_predictions(produced, expected_file, columns) + except (AssertionError, ValueError) as error: + saved = Path( + tempfile.mkdtemp(prefix=f"rnagym-{registry_name}-{assay_id}-") + ) + shutil.copy2(produced, saved / "actual.csv") + shutil.copy2(expected_file, saved / "expected.csv") + raise AssertionError( + f"{error}. Comparison files saved to {saved}" + ) from error + record = { + "environment": environment, + "model": registry_name, + "assay": assay_id, + "row_id": row_id, + **comparison.__dict__, + "checkpoint_seconds": checkpoint_seconds, + "assay_sha256": hashlib.sha256( + (temporary_data_dir / "assays" / f"{assay_id}.csv").read_bytes() + ).hexdigest(), + "published_sha256": hashlib.sha256( + expected_file.read_bytes() + ).hexdigest(), + } + yield record + produced.unlink() + + +def runtime_versions() -> dict[str, str]: + """Record the interpreter and numerical libraries actually used.""" + versions = {"python": platform.python_version(), "platform": platform.platform()} + for package in ( + "evo-model", + "evo2", + "flash-attn", + "nvidia-cublas-cu12", + "nvidia-cudnn-cu12", + "nvidia-cufft-cu12", + "numpy", + "paddlepaddle", + "polars", + "scipy", + "torch", + "transformer-engine", + "transformers", + "vtx", + ): + try: + versions[package] = importlib.metadata.version(package) + except importlib.metadata.PackageNotFoundError: + pass + if "torch" in versions: + import torch + + versions["cuda"] = torch.version.cuda or "none" + versions["cudnn"] = str(torch.backends.cudnn.version()) + if torch.cuda.is_available(): + versions["gpu"] = ", ".join( + torch.cuda.get_device_name(index) + for index in range(torch.cuda.device_count()) + ) + return versions + + +def selected_assays(reference_file: Path) -> dict[str, int]: + """Return every ncRNA assay and its original reference-sheet row.""" + reference = read_reference(reference_file).with_row_index("row_id") + selected = reference.filter(pl.col("RNA_TYPE").is_in(ASSAY_GROUPS["ncRNA"])) + if selected.is_empty(): + raise ValueError("Reference sheet has no ncRNA assays") + return dict(selected.select("DMS_ID", "row_id").iter_rows()) + + +def select_rows(table: pl.DataFrame, limit: int | None) -> list[int]: + """Select reproducible single and multiple mutants in original row order.""" + if limit is None or table.height <= limit: + return list(range(table.height)) + multiple = table["mutant"].str.contains(",").fill_null(False).to_numpy() + selected = set() + for mask in (multiple, ~multiple): + rows = np.flatnonzero(mask) + selected.update( + rows[ + np.linspace(0, len(rows) - 1, min(len(rows), limit // 2), dtype=int) + ].tolist() + ) + remaining = sorted(set(range(table.height)) - selected) + count = min(limit - len(selected), len(remaining)) + if count: + selected.update( + np.asarray(remaining)[ + np.linspace(0, len(remaining) - 1, count, dtype=int) + ].tolist() + ) + return sorted(selected) + + +def validate_family(environment: str) -> None: + """Require every expected checkpoint and assay before loading a model.""" + family = MODEL_FAMILIES[environment] + assays = selected_assays(ConfigFitness.DATA_DIR / "reference_sheet_final.csv") + paths = [ConfigFitness.DIR / family.launcher] + paths.extend( + ConfigFitness.PREDICTION_DIR / prediction_layout(name)[0] / f"{assay}.csv" + for name, _ in family.checkpoints + for assay in assays + ) + if environment == "genslm": + paths.append( + ConfigFitness.CHECKPOINT_DIR + / "genslm/2.5B/patric_2.5b_epoch00_val_los_0.29_bias_removed.pt" + ) + missing = [str(path) for path in paths if not path.is_file()] + if missing: + raise FileNotFoundError( + "Reproduction inputs are missing: " + ", ".join(missing) + ) + + +if __name__ == "__main__": + main() diff --git a/rnagym/fitness/tasks/leaderboard.py b/rnagym/fitness/tasks/leaderboard.py new file mode 100644 index 0000000..43f8023 --- /dev/null +++ b/rnagym/fitness/tasks/leaderboard.py @@ -0,0 +1,128 @@ +"""Build the fitness leaderboard and comparison on EVmutation-covered variants.""" + +from __future__ import annotations + +import argparse +import shlex +import tempfile +from collections.abc import Sequence +from pathlib import Path + +import polars as pl + +from rnagym.config import ConfigFitness +from rnagym.fitness.tasks import performance_fitness + +CATEGORIES = ("Ribozyme", "tRNA", "Aptamer") +MODEL_LABELS = { + "aido_rna": "AIDO.RNA (1.6B)", + "aido_rna_1m": "AIDO.RNA (1M)", + "aido_rna_25m": "AIDO.RNA (25M)", + "aido_rna_300m": "AIDO.RNA (300M)", + "aido_rna_650m": "AIDO.RNA (650M)", + "evo1": "Evo 1", + "evo1.5": "Evo 1.5", + "evo2": "Evo 2 (7B)", + "evo2_1b_base": "Evo 2 (1B base)", + "evo2_20b": "Evo 2 (20B)", + "evo2_40b": "Evo 2 (40B)", + "EVmutation": "EVmutation", + "GenSLM": "GenSLM (2.5B)", + "ntv3_8m": "Nucleotide Transformer v3 (8M)", + "ntv3_100m": "Nucleotide Transformer v3 (100M)", + "ntv3_650m": "Nucleotide Transformer v3 (650M)", + "orthrus": "Orthrus", + "rinalmo": "RiNALMo", + "RNAErnie": "RNA-ERNIE", + "RNA-FM": "RNA-FM", + "rnagenesis": "RNAGenesis", +} + + +def main(argv: Sequence[str] | None = None) -> None: + """Recompute both tables from merged predictions.""" + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--input", type=Path, default=ConfigFitness.COMBINED_DIR) + parser.add_argument("--output", type=Path, default=ConfigFitness.LEADERBOARD_DIR) + args = parser.parse_args(argv) + with tempfile.TemporaryDirectory(prefix="rnagym-fitness-") as directory: + reports = Path(directory) + performance_fitness.main( + performance_fitness.parse_args( + shlex.split( + f"--input {shlex.quote(str(args.input))} --output {shlex.quote(str(reports))}" + ) + ) + ) + write_tables(reports, args.output) + print(f"Wrote fitness tables to {args.output}") + + +def render_table(table: pl.DataFrame, coverage: pl.DataFrame) -> str: + """Format category means, marking the baseline's partial coverage in the main table.""" + headers = [ + f"{name} (n={coverage.filter(pl.col('RNA_TYPE') == name).height})" + for name in CATEGORIES + ] + lines = [ + "| Rank | Model | " + " | ".join(headers) + " | Macro (3 ncRNA) |", + "|---:|:--|--:|--:|--:|--:|", + ] + for rank, row in enumerate(table.iter_rows(named=True), 1): + model = MODEL_LABELS[row["model"]] + if row["model"] == "EVmutation": + model += "*" + values = [ + "-" if row[column] is None else f"{row[column]:.4f}" + for column in (*CATEGORIES, "macro_3ncRNA") + ] + place = str(rank) if row["macro_3ncRNA"] is not None else "-" + lines.append(f"| {place} | {model} | " + " | ".join(values) + " |") + return "\n".join(lines) + + +def write_tables(reports: Path, output: Path) -> None: + """Write full precision scores, coverage counts and the README tables.""" + coverage = pl.read_csv(reports / "evmutation/coverage.csv") + covered = coverage.filter(pl.col("evaluated_variants") > 0) + variants = int(coverage["evaluated_variants"].sum()) + total = int(coverage["variants"].sum()) + generated = "" + output.mkdir(parents=True, exist_ok=True) + for directory, filename in ( + (reports, "leaderboard_signed_3ncRNA.csv"), + (reports / "evmutation", "leaderboard_evmutation.csv"), + ): + table = ( + pl.read_csv(directory / "results_by_rna_type.csv") + .select( + pl.col("Model").str.strip_suffix("_score").alias("model"), + *[pl.col(f"Spearman_{name}_Mean").alias(name) for name in CATEGORIES], + pl.col("Spearman_All_Mean").alias("macro_3ncRNA"), + ) + .sort("macro_3ncRNA", descending=True, nulls_last=True) + ) + table.write_csv(output / filename) + if directory == reports: + generated = render_table(table, coverage) + coverage.write_csv(output / "evmutation_coverage.csv") + generated += ( + f"\n\n\\* EVmutation scores {covered.height}/{coverage.height} assays and " + + f"{variants:,}/{total:,} variants ({variants / total:.1%}). " + + "Its row uses those variants. [Coverage by assay][5]. " + + "[Compare all models on those variants][6]." + ) + readme = output / "README.md" + start, end = "", "" + text = ( + readme.read_text() + if readme.exists() + else f"# Fitness leaderboard\n\n{start}\n{end}\n\n[5]: evmutation_coverage.csv\n[6]: leaderboard_evmutation.csv\n" + ) + before, rest = text.split(start) + _, after = rest.split(end) + readme.write_text(f"{before}{start}\n{generated}\n{end}{after}") + + +if __name__ == "__main__": + main() diff --git a/rnagym/fitness/tasks/merge_scoring_files.py b/rnagym/fitness/tasks/merge_scoring_files.py new file mode 100755 index 0000000..1d4e5ea --- /dev/null +++ b/rnagym/fitness/tasks/merge_scoring_files.py @@ -0,0 +1,270 @@ +#!/usr/bin/env python3 +"""Merge processed DMS assay CSVs with model prediction scores.""" + +from __future__ import annotations + +import argparse +import logging +import tempfile +from collections.abc import Sequence +from pathlib import Path + +import numpy as np +import polars as pl + +from rnagym.config import ConfigFitness +from rnagym.fitness.data import read_reference +from rnagym.fitness.tasks.model_registry import ( + ALL_MODELS, + ASSAY_GROUPS, + SCORE_COLS, + resolve_source, +) + +logging.basicConfig( + level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s" +) +logger = logging.getLogger(__name__) + + +def combine_csv_data( + processed_folder: str | Path, + model_predictions_folder: str | Path, + output_folder: str | Path, + model_list: Sequence[str], + score_cols_dict: dict[str, str | dict[str, str]], + assay_types: dict[str, str] | None = None, + assay_group: str = "all", +): + """ + Merge predictions without dropping or multiplying assay measurements. + + Every requested model must cover every assay in the selected group. + The coverage check runs before any output is written, and all merged files + are staged before they replace prior outputs. + """ + if len(model_list) != len(set(model_list)): + raise ValueError(f"Model list contains duplicates: {model_list}") + processed_files = sorted(Path(processed_folder).glob("*.csv")) + if not processed_files: + raise FileNotFoundError(f"No assay CSVs found under {processed_folder}") + if assay_group != "all" and assay_group not in ASSAY_GROUPS: + raise ValueError(f"Unknown assay group: {assay_group!r}") + if assay_types is not None: + unknown = sorted( + path.stem for path in processed_files if path.stem not in assay_types + ) + if unknown: + raise ValueError( + f"Processed assays are absent from the reference sheet: {unknown[:5]}" + ) + if assay_group != "all": + if assay_types is None: + raise ValueError("assay_types is required when selecting an assay group") + selected_types = ASSAY_GROUPS[assay_group] + processed_files = [ + path for path in processed_files if assay_types[path.stem] in selected_types + ] + if not processed_files: + raise FileNotFoundError( + f"No {assay_group} assay CSVs found under {processed_folder}" + ) + processed_names = {path.name for path in processed_files} + + expected = {} + for model_name in model_list: + if model_name == "EVmutation": + expected[model_name] = set() + else: + expected[model_name] = processed_names + + missing = {} + for model_name, assay_names in expected.items(): + folder, _ = resolve_source(score_cols_dict, model_name) + absent = sorted( + name + for name in assay_names + if not (Path(model_predictions_folder) / folder / name).is_file() + ) + if absent: + missing[model_name] = absent + if missing: + detail = " | ".join( + f"{model}: {len(files)} missing, including {files[:3]}" + for model, files in sorted(missing.items()) + ) + raise FileNotFoundError( + f"Prediction coverage is incomplete under {model_predictions_folder}: {detail}" + ) + + output_path = Path(output_folder) + output_path.parent.mkdir(parents=True, exist_ok=True) + contributed = {model: set() for model in model_list} + + with tempfile.TemporaryDirectory( + dir=output_path.parent, prefix=f".{output_path.name}-" + ) as staging_dir: + staging_path = Path(staging_dir) + for processed_file in processed_files: + csv_file = processed_file.name + + df = pl.read_csv(processed_file) + mutation_col = get_mutation_column(df) + df = df.drop_nulls(mutation_col).with_columns( + standardize_mutation(mutation_col) + ) + + for model_name in model_list: + folder, score_col = resolve_source(score_cols_dict, model_name) + model_path = Path(model_predictions_folder) / folder / csv_file + if not model_path.exists(): + df = df.with_columns( + pl.lit(None, dtype=pl.Float64).alias(f"{model_name}_score") + ) + continue + + df = merge_predictions( + df, pl.read_csv(model_path), score_col, model_name + ) + contributed[model_name].add(csv_file) + + df.write_csv(staging_path / csv_file) + + absent = sorted(model for model, assays in contributed.items() if not assays) + if absent: + raise FileNotFoundError( + f"No predictions found for {absent} under {model_predictions_folder}. " + "Check the model names and prediction folders." + ) + + output_path.mkdir(parents=True, exist_ok=True) + staged_names = {path.name for path in staging_path.iterdir()} + for stale_file in output_path.glob("*.csv"): + if stale_file.name not in staged_names: + stale_file.unlink() + for staged_file in staging_path.iterdir(): + staged_file.replace(output_path / staged_file.name) + + for model_name, assays in sorted(contributed.items()): + logger.info(f"{model_name}: {len(assays)} assays merged") + + +def get_mutation_column(df: pl.DataFrame): + """Find the mutation column name in a DataFrame.""" + for col in ["mutant", "mutation", "Mutation", "mutations"]: + if col in df.columns: + return col + raise ValueError("Couldn't find a mutation column in the dataframe") + + +def main(argv: Sequence[str] | None = None): + """Run the prediction merge command.""" + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + "--models", nargs="+", default=ALL_MODELS, help="Models to merge" + ) + parser.add_argument("--output", type=Path, default=ConfigFitness.COMBINED_DIR) + parser.add_argument("--type", default="ncRNA", choices=["all", *ASSAY_GROUPS]) + args = parser.parse_args(argv) + model_list = args.models + unknown = [m for m in model_list if m not in SCORE_COLS] + if unknown: + parser.error(f"No score column configured for: {unknown}") + + reference = read_reference(ConfigFitness.REFERENCE_FILE) + assay_types = dict(reference.select("DMS_ID", "RNA_TYPE").iter_rows()) + + combine_csv_data( + ConfigFitness.ASSAY_DIR, + ConfigFitness.PREDICTION_DIR, + args.output, + model_list, + SCORE_COLS, + assay_types=assay_types, + assay_group=args.type, + ) + + +def merge_predictions( + assay: pl.DataFrame, prediction: pl.DataFrame, score_column: str, model: str +) -> pl.DataFrame: + """Attach scores while preserving every experimental measurement. + + The published leaderboard uses the first prediction for each mutation and + retains repeated experimental measurements. Repeated predictions must have + matching sequences and agree within 0.2% relative or 1e-6 absolute tolerance. + This allows rounding drift while rejecting conflicting prediction files. + """ + mutation = get_mutation_column(assay) + source_mutation = get_mutation_column(prediction) + if score_column not in prediction: + raise KeyError(f"{model} has no column {score_column!r}") + prediction = ( + prediction.drop_nulls(source_mutation) + .with_columns(standardize_mutation(source_mutation)) + .rename({source_mutation: mutation}) + ) + output_column = f"{model}_score" + if output_column in assay: + raise ValueError(f"Assay already contains {output_column}") + scores = prediction[score_column].cast(pl.Float64, strict=True) + if model != "EVmutation" and not scores.is_finite().fill_null(False).all(): + raise ValueError(f"{model} has missing or nonfinite predictions") + if scores.is_infinite().any() or scores.is_nan().any(): + raise ValueError(f"{model} has nonfinite predictions") + + if prediction[mutation].is_duplicated().any(): + for _, repeated in prediction.filter(pl.col(mutation).is_duplicated()).group_by( + mutation + ): + if "sequence" in repeated and repeated["sequence"].n_unique() != 1: + raise ValueError(f"{model}: repeated prediction sequences conflict") + values = repeated[score_column].cast(pl.Float64).to_numpy() + if not np.allclose(values, values[0], rtol=2e-3, atol=1e-6, equal_nan=True): + raise ValueError( + f"{model}: conflicting scores for duplicate mutation {repeated[mutation][0]}" + ) + prediction = prediction.unique( + subset=mutation, keep="first", maintain_order=True + ) + + columns = [pl.col(mutation), pl.col(score_column).alias(output_column)] + if "sequence" in prediction and "sequence" in assay: + columns.append(pl.col("sequence").alias("_prediction_sequence")) + prediction = prediction.select(columns) + matched = assay.join( + prediction, on=mutation, how="inner", validate="m:1", maintain_order="left" + ) + if matched.height != assay.height: + raise ValueError( + f"Row count mismatch after merging {model}: expected {assay.height}, got {matched.height}" + ) + if "_prediction_sequence" in matched: + different = matched.select( + ( + pl.col("sequence").str.to_uppercase().str.replace_all("T", "U") + != pl.col("_prediction_sequence") + .str.to_uppercase() + .str.replace_all("T", "U") + ) + .fill_null(True) + .any() + ).item() + if different: + raise ValueError(f"{model}: prediction sequence disagrees with assay") + matched = matched.drop("_prediction_sequence") + return matched + + +def standardize_mutation(column: str) -> pl.Expr: + """Normalize a mutation column to uppercase RNA notation.""" + return ( + pl.col(column) + .str.to_uppercase() + .str.replace_all("T", "U") + .str.replace_all(" ", "") + ) + + +if __name__ == "__main__": + main() diff --git a/fitness/model_registry.py b/rnagym/fitness/tasks/model_registry.py similarity index 51% rename from fitness/model_registry.py rename to rnagym/fitness/tasks/model_registry.py index 93cd325..5e42364 100644 --- a/fitness/model_registry.py +++ b/rnagym/fitness/tasks/model_registry.py @@ -1,6 +1,10 @@ -"""Assay groups, prediction folders and score columns for fitness workflows.""" +"""Assay groups, checkpoint revisions and prediction columns for fitness workflows.""" -ALL_MODELS = ( +from __future__ import annotations + +from pathlib import Path + +CHECKPOINT_MODELS = ( "evo1", "evo1.5", "evo2", @@ -22,6 +26,7 @@ "aido_rna_300m", "aido_rna_650m", ) +ALL_MODELS = (*CHECKPOINT_MODELS, "EVmutation") ASSAY_GROUPS = { "ncRNA": ("Ribozyme", "tRNA", "Aptamer"), @@ -29,7 +34,25 @@ "coding": ("mRNA-coding",), } -SCORE_COLS = { +CHECKPOINT_REVISIONS = { + "InstaDeepAI/NTv3_8M_pre": "c57a813117f0f90142098f81cc912b3357c9ecd1", + "InstaDeepAI/NTv3_100M_pre": "5c685dca15891f5c5b80e0c930e23b87a217e441", + "InstaDeepAI/NTv3_650M_pre": "5e6050bed864a5a8fb32481096bf555495316b31", + "antichronology/orthrus-mlm-6-track": "5f0dc87d51065035fc28e71972c69f9c84f4deae", + "arcinstitute/evo2_1b_base": "2279e1df422c991037470302360edd40d0d2ea1e", + "arcinstitute/evo2_7b": "bda0089f92582d5baabf0f22d9fc85f3588f6b58", + "arcinstitute/evo2_20b": "8b0f0a9a70c66367ed181a17d049b95699a28fed", + "arcinstitute/evo2_40b": "d529aa57c30771814217ad89baaeaf6e2315c7d7", + "evo-design/evo-1.5-8k-base": "99a9a4df722662b03d2a79b1770c3150421aa9e9", + "genbio-ai/AIDO.RNA-1M-MARS": "00029ce0f63d5c40ef6cc12ddd7744dcc5a97a42", + "genbio-ai/AIDO.RNA-25M-MARS": "f7f0bfaeabf4fbd258995dab82c5cf356ec6e852", + "genbio-ai/AIDO.RNA-300M-MARS": "0f21db50d971d43ee66b39963ae4ec3c28b66e0d", + "genbio-ai/AIDO.RNA-650M": "efaf4af45dc6b522b7ba62ee725d00ad9c43ce11", + "genbio-ai/AIDO.RNA-1.6B": "cea90e53284c2a8b77c66528303d04a784b9c67b", + "togethercomputer/evo-1-131k-base": "c206aab77ae5967a069c4200ecb1858588528c9d", +} + +SCORE_COLS: dict[str, str | dict[str, str]] = { "evo1": "evo_1_131k_base_score", "evo1.5": "evo_1.5_8k_base_score", "evo2": "evo2_7b_score", @@ -37,43 +60,7 @@ "evo2_20b": "evo2_20b_score", "evo2_40b": "evo2_40b_score", "GenSLM": "logit_scores", - "ntv3_8m": { - "folder": "ntv3_8m_4fill", - "column": "ntv3_score_wt_fill", - }, - "ntv3_100m": { - "folder": "ntv3_100m_4fill", - "column": "ntv3_score_wt_fill", - }, - "ntv3_650m": { - "folder": "ntv3_650m_4fill", - "column": "ntv3_score_wt_fill", - }, - "RNA-FM": {"folder": "rna_fm_4fill", "column": "RNA_FM_scores_wt_fill"}, - "rinalmo": {"folder": "rinalmo_4fill", "column": "logit_scores_wt_fill"}, "RNAErnie": "Mutation_Scores", - "orthrus": {"folder": "orthrus_4fill", "column": "orthrus_score_wt_fill"}, - "aido_rna": {"folder": "aido_rna_4fill", "column": "aido_rna_score_wt_fill"}, - "rnagenesis": { - "folder": "rnagenesis_4fill", - "column": "rnagenesis_score_wt_fill", - }, - "aido_rna_1m": { - "folder": "aido_rna_1m_4fill", - "column": "aido_rna_score_wt_fill", - }, - "aido_rna_25m": { - "folder": "aido_rna_25m_4fill", - "column": "aido_rna_score_wt_fill", - }, - "aido_rna_300m": { - "folder": "aido_rna_300m_4fill", - "column": "aido_rna_score_wt_fill", - }, - "aido_rna_650m": { - "folder": "aido_rna_650m_4fill", - "column": "aido_rna_score_wt_fill", - }, "EVmutation": "prediction_epistatic", } @@ -93,6 +80,16 @@ } STRATEGIES = ("wt_fill", "mask_fill", "mut_fill", "match_fill") +SCORE_COLS.update( + { + ("RNA-FM" if name == "rna_fm" else name): { + "folder": folder, + "column": f"{stem}_wt_fill", + } + for name, (folder, stem) in FOUR_FILL_SPECS.items() + } +) + _four_fill_models = [] for name, (folder, column_stem) in FOUR_FILL_SPECS.items(): entries = { @@ -107,7 +104,22 @@ FOUR_FILL_MODELS = tuple(_four_fill_models) -def resolve_source(score_columns: dict, model_name: str) -> tuple[str, str]: +def checkpoint_revision(model: str) -> str | None: + """Resolve a pinned Hub checkpoint or an explicit local model directory.""" + if Path(model).is_dir(): + return None + try: + return CHECKPOINT_REVISIONS[model] + except KeyError: + raise ValueError( + f"Unpinned checkpoint: {model!r}. " + "Use a registered checkpoint or a local model directory." + ) from None + + +def resolve_source( + score_columns: dict[str, str | dict[str, str]], model_name: str +) -> tuple[str, str]: """Return the prediction folder and score column for a model entry.""" try: specification = score_columns[model_name] diff --git a/rnagym/fitness/tasks/performance_fitness.py b/rnagym/fitness/tasks/performance_fitness.py new file mode 100755 index 0000000..7e212a0 --- /dev/null +++ b/rnagym/fitness/tasks/performance_fitness.py @@ -0,0 +1,422 @@ +#!/usr/bin/env python3 +"""Evaluate fitness prediction models across RNA assays.""" + +from __future__ import annotations + +import argparse +import logging +from collections.abc import Sequence +from pathlib import Path +from typing import cast + +import numpy as np +import polars as pl +from scipy import stats +from sklearn.metrics import matthews_corrcoef, roc_auc_score + +from rnagym.config import ConfigFitness +from rnagym.fitness.data import read_reference +from rnagym.fitness.tasks.model_registry import ALL_MODELS, ASSAY_GROUPS + +logging.basicConfig(level=logging.WARNING) +logger = logging.getLogger(__name__) + +DEPTHS = ("single", "multiple") +METRICS = ("Spearman", "AUC", "MCC") +RNA_TYPES = ("mRNA-splicing", "mRNA-coding", "tRNA", "Aptamer", "Ribozyme") + + +def _add_depth(data: pl.DataFrame) -> pl.DataFrame: + """Classify mutations without changing the source mutation column.""" + return data.with_columns( + pl.when(pl.col("mutant").is_null()) + .then(None) + .when(pl.col("mutant").str.contains(",")) + .then(pl.lit("multiple")) + .otherwise(pl.lit("single")) + .alias("Depth") + ) + + +def _metric_rows( + dms_id: str, + rna_type: str, + results: dict[str, dict[str, float]], + depth: str | None = None, +) -> list[dict[str, object]]: + """Convert one assay's metric dictionaries to rows.""" + rows: list[dict[str, object]] = [] + for model, metrics in results.items(): + row: dict[str, object] = { + "DMS_ID": dms_id, + "RNA_TYPE": rna_type, + "Model": model, + } + if depth is not None: + row["Depth"] = depth + row.update(metrics) + rows.append(row) + return rows + + +def aggregate_by_depth(depth_metrics: pl.DataFrame, models: list[str]) -> pl.DataFrame: + """Average assay metrics within each mutation depth.""" + means: dict[str, dict[str, dict[str, float | None]]] = {} + for model in models: + means[model] = {} + for depth in DEPTHS: + assays = depth_metrics.filter( + (pl.col("Model") == model) & (pl.col("Depth") == depth) + ) + means[model][depth] = { + metric: cast(float | None, assays[metric].mean()) for metric in METRICS + } + means[model]["All"] = { + metric: cast( + float | None, + pl.Series( + [means[model][depth][metric] for depth in DEPTHS], dtype=pl.Float64 + ).mean(), + ) + for metric in METRICS + } + + rows: list[dict[str, object]] = [] + for depth in ("All", "multiple", "single"): + for model in models: + row: dict[str, object] = {"Depth": depth, "Model": model} + row.update( + {f"{metric}_Mean": means[model][depth][metric] for metric in METRICS} + ) + rows.append(row) + return pl.DataFrame(rows, infer_schema_length=None) + + +def aggregate_by_rna_type( + assay_metrics: pl.DataFrame, + models: list[str], + rna_types: list[str], +) -> pl.DataFrame: + """Average assay metrics by RNA type and equally across RNA types.""" + grouped = { + (row["Model"], row["RNA_TYPE"]): row + for row in assay_metrics.group_by("Model", "RNA_TYPE") + .agg(pl.col(*METRICS).mean()) + .iter_rows(named=True) + } + + rows: list[dict[str, object]] = [] + for model in models: + row: dict[str, object] = {"Model": model} + for metric in METRICS: + type_means = [] + for rna_type in rna_types: + mean = grouped.get((model, rna_type), {}).get(metric) + row[f"{metric}_{rna_type}_Mean"] = mean + type_means.append(mean) + row[f"{metric}_All_Mean"] = ( + float(np.mean(np.asarray(type_means, dtype=float))) + if all(value is not None for value in type_means) + else None + ) + rows.append(row) + + columns = ["Model"] + for metric in METRICS: + for rna_type in [*rna_types, "All"]: + columns.append(f"{metric}_{rna_type}_Mean") + return pl.DataFrame(rows, infer_schema_length=None).select(columns) + + +def aggregate_by_type_and_depth( + depth_metrics: pl.DataFrame, models: list[str], rna_types: list[str] +) -> pl.DataFrame: + """Average metrics by RNA type and mutation depth.""" + grouped = { + (row["Model"], row["Depth"], row["RNA_TYPE"]): row + for row in depth_metrics.group_by("Model", "Depth", "RNA_TYPE") + .agg(pl.col(*METRICS).mean()) + .iter_rows(named=True) + } + rows: list[dict[str, object]] = [] + + for model in models: + for depth in (*DEPTHS, "overall"): + type_values = [] + for rna_type in rna_types: + values = grouped.get((model, depth, rna_type), dict.fromkeys(METRICS)) + row: dict[str, object] = { + "Model": model, + "Depth": depth, + "RNA_TYPE": rna_type, + } + row.update({f"{metric}_Mean": values[metric] for metric in METRICS}) + rows.append(row) + type_values.append(values) + + all_values = ( + pl.DataFrame(type_values, infer_schema_length=None) + .select(pl.col(*METRICS).mean()) + .row(0, named=True) + ) + row: dict[str, object] = {"Model": model, "Depth": depth, "RNA_TYPE": "All"} + row.update({f"{metric}_Mean": all_values[metric] for metric in METRICS}) + rows.append(row) + + return pl.DataFrame(rows, infer_schema_length=None) + + +def calculate_metrics( + assay_scores: np.ndarray, model_scores: np.ndarray +) -> dict[str, float]: + """Calculate directed Spearman, AUC, and MCC. + + AUC binarizes assay scores at their median. MCC binarizes both score vectors + at their respective medians. + """ + binary_assay = assay_scores > np.median(assay_scores) + binary_model = model_scores > np.median(model_scores) + return { + "Spearman": stats.spearmanr(assay_scores, model_scores).correlation, + "AUC": float(roc_auc_score(binary_assay, model_scores)), + "MCC": matthews_corrcoef(binary_assay, binary_model), + } + + +def cli(argv: Sequence[str] | None = None) -> None: + """Run the fitness performance command.""" + args = parse_args(argv) + summary = main(args) + with pl.Config(tbl_rows=-1, tbl_cols=-1, float_precision=4): + print(summary) + print(f"Saved metrics to {args.output}") + + +def get_performance_dataset( + data: pl.DataFrame, assay_column: str, score_columns: list[str] +) -> dict[str, dict[str, float]]: + """Calculate each model's metrics from complete finite values.""" + data = data.drop_nulls("mutant") + missing_metrics = dict.fromkeys(METRICS, np.nan) + results = {} + + for score_column in score_columns: + for column in (assay_column, score_column): + if column not in data: + raise KeyError(column) + pairs = data.select( + pl.col(assay_column, score_column).cast(pl.Float64, strict=False) + ) + invalid = { + column: pairs.height - pairs[column].is_finite().fill_null(False).sum() + for column in pairs.columns + } + invalid = {column: count for column, count in invalid.items() if count} + if invalid: + raise ValueError(f"Missing or nonfinite metric values: {invalid}") + if pairs.is_empty(): + results[score_column] = missing_metrics.copy() + continue + + try: + results[score_column] = calculate_metrics( + pairs[assay_column].to_numpy(), pairs[score_column].to_numpy() + ) + except (TypeError, ValueError) as error: + logger.warning("Could not calculate %s: %s", score_column, error) + results[score_column] = missing_metrics.copy() + + return results + + +def load_assay_metrics( + reference: pl.DataFrame, + combined_dir: str | Path, + score_columns: list[str], + msa_only: bool = False, +) -> tuple[pl.DataFrame, pl.DataFrame, pl.DataFrame]: + """Calculate assay metrics and record the evaluated variant counts.""" + assay_rows = [] + depth_rows = [] + combined_dir = Path(combined_dir) + coverage = [] + + for assay_info in reference.iter_rows(named=True): + assay_path = combined_dir / f"{assay_info['DMS_ID']}.csv" + if not assay_path.is_file(): + raise FileNotFoundError(f"Selected assay is missing: {assay_path}") + assay = pl.read_csv(assay_path).drop_nulls("mutant") + missing_columns = [column for column in score_columns if column not in assay] + if missing_columns: + raise KeyError(f"{assay_path} is missing score columns: {missing_columns}") + + if "Depth" not in assay: + assay = _add_depth(assay) + + filtered = assay + if msa_only: + baseline = assay["EVmutation_score"].cast(pl.Float64) + if baseline.is_nan().any() or baseline.is_infinite().any(): + raise ValueError(f"{assay_path} has nonfinite EVmutation scores") + filtered = assay.filter(baseline.is_not_null()) + evaluable = filtered.height >= 2 and filtered["DMS_score"].n_unique() >= 2 + coverage.append( + { + "DMS_ID": assay_info["DMS_ID"], + "RNA_TYPE": assay_info["RNA_TYPE"], + "variants": assay.height, + "scored_variants": filtered.height, + "evaluated_variants": filtered.height if evaluable else 0, + } + ) + if msa_only and not evaluable: + continue + + filtered_results = get_performance_dataset(filtered, "DMS_score", score_columns) + undefined = [ + model + for model, metrics in filtered_results.items() + if not np.isfinite(metrics["Spearman"]) + ] + if undefined: + raise ValueError( + f"{assay_path} has undefined Spearman correlation for {undefined}. " + "Each selected assay requires varying measurements and predictions." + ) + assay_rows.extend( + _metric_rows(assay_info["DMS_ID"], assay_info["RNA_TYPE"], filtered_results) + ) + + depth_rows.extend( + _metric_rows( + assay_info["DMS_ID"], + assay_info["RNA_TYPE"], + filtered_results, + "overall", + ) + ) + for depth in DEPTHS: + depth_results = get_performance_dataset( + filtered.filter(pl.col("Depth") == depth), "DMS_score", score_columns + ) + depth_rows.extend( + _metric_rows( + assay_info["DMS_ID"], + assay_info["RNA_TYPE"], + depth_results, + depth, + ) + ) + + if not assay_rows: + raise ValueError("No assays have enough scored variants with varying fitness") + return ( + pl.DataFrame(assay_rows).with_columns(pl.col(*METRICS).fill_nan(None)), + pl.DataFrame(depth_rows).with_columns(pl.col(*METRICS).fill_nan(None)), + pl.DataFrame(coverage), + ) + + +def main(args: argparse.Namespace) -> pl.DataFrame: + """Write the fitness metrics and return category means.""" + reference = select_assays(read_reference(ConfigFitness.REFERENCE_FILE), args.type) + if reference.is_empty(): + raise ValueError(f"No assays found for type {args.type!r}") + models = list(args.models or ALL_MODELS) + if len(models) != len(set(models)): + raise ValueError("Model list contains duplicates") + score_columns = [f"{model}_score" for model in models] + rna_types = [ + rna_type for rna_type in RNA_TYPES if (reference["RNA_TYPE"] == rna_type).any() + ] + complete_columns = [ + column for column in score_columns if column != "EVmutation_score" + ] + assays: list[pl.DataFrame] = [] + depths: list[pl.DataFrame] = [] + if complete_columns: + assay_metrics, depth_metrics, _ = load_assay_metrics( + reference, args.input, complete_columns + ) + assays.append(assay_metrics) + depths.append(depth_metrics) + if "EVmutation" in models: + shared_assays, shared_depths, coverage = load_assay_metrics( + reference, args.input, score_columns, msa_only=True + ) + save_results( + args.output / "evmutation", + shared_assays, + shared_depths, + score_columns, + rna_types, + ) + coverage.write_csv(args.output / "evmutation/coverage.csv") + baseline = pl.col("Model") == "EVmutation_score" + assays.append(shared_assays.filter(baseline)) + depths.append(shared_depths.filter(baseline)) + assay_metrics, depth_metrics = pl.concat(assays), pl.concat(depths) + by_rna_type = save_results( + args.output, assay_metrics, depth_metrics, score_columns, rna_types + ) + return by_rna_type.select( + "Model", + *[pl.col(f"Spearman_{name}_Mean").alias(name) for name in rna_types], + pl.col("Spearman_All_Mean").alias("Mean"), + ).sort("Mean", descending=True) + + +def parse_args(argv: Sequence[str] | None = None) -> argparse.Namespace: + """Parse command-line arguments.""" + parser = argparse.ArgumentParser(description="Evaluate fitness predictions") + parser.add_argument("--input", type=Path, default=ConfigFitness.COMBINED_DIR) + parser.add_argument("--output", type=Path, default=ConfigFitness.REPORT_DIR) + parser.add_argument("--models", nargs="+", help="Models to evaluate") + parser.add_argument("--type", default="ncRNA", choices=["all", *ASSAY_GROUPS]) + return parser.parse_args(argv) + + +def save_results( + output_dir: str | Path, + assay_metrics: pl.DataFrame, + depth_metrics: pl.DataFrame, + models: list[str], + rna_types: list[str], +) -> pl.DataFrame: + """Write assay and aggregate metrics and return the category means.""" + by_rna_type = aggregate_by_rna_type(assay_metrics, models, rna_types) + by_depth = aggregate_by_depth(depth_metrics, models) + by_type_and_depth = aggregate_by_type_and_depth(depth_metrics, models, rna_types) + output_dir = Path(output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + tables = { + "assay_level_results.csv": assay_metrics, + "results_by_mutation_depth.csv": by_depth, + "results_by_rna_type.csv": by_rna_type, + "results_by_rna_type_and_depth.csv": by_type_and_depth, + } + for filename, table in tables.items(): + table.write_csv(output_dir / filename) + + transposed = assay_metrics.select("DMS_ID", "RNA_TYPE").unique(maintain_order=True) + for metric in METRICS: + metric_table = assay_metrics.pivot(on="Model", index="DMS_ID", values=metric) + metric_table = metric_table.select("DMS_ID", *models).rename( + {model: f"{metric}_{model}" for model in models} + ) + transposed = transposed.join( + metric_table, on="DMS_ID", validate="1:1", maintain_order="left" + ) + transposed.write_csv(output_dir / "assay_level_results_transposed.csv") + return by_rna_type + + +def select_assays(reference: pl.DataFrame, assay_type: str) -> pl.DataFrame: + """Select an assay group.""" + if assay_type == "all": + return reference.clone() + return reference.filter(pl.col("RNA_TYPE").is_in(ASSAY_GROUPS[assay_type])) + + +if __name__ == "__main__": + cli() diff --git a/rnagym/s2d/README.md b/rnagym/s2d/README.md index 27608b4..d0827f1 100644 --- a/rnagym/s2d/README.md +++ b/rnagym/s2d/README.md @@ -1,62 +1,40 @@ -# RNAGym secondary structure benchmark +# Secondary structure benchmark -## Getting started +RNA secondary structure prediction on the RNAGym datasets in `data/2d/`. +See the [leaderboard][0] for results. -The datasets are stored under [`../../data/2d/`](../../data/2d/). See the -[repository-level instructions](../../README.md#resources) to download and extract -them. +## Run -## Environments +Complete the [shared data setup][3], then run from `rnagym/s2d/`: -This directory has isolated [Pixi](https://pixi.sh) environments for the -secondary structure baselines in RNAGym: +```bash +pixi install +pixi run -e test +pixi run -e predict +``` + +Replace `` with a model environment from [pixi.toml][1]: -| Environment | Tool | +| Environment | Model | | --- | --- | -| `(default)` | Default env for scripts and processing | +| `contrafold` | CONTRAfold | +| `eternafold` | EternaFold | +| `mxfold2` | MXfold2 | | `ribonanzanet` | RibonanzaNet | | `rinalmo` | RiNALMo | -| `eternafold` | EternaFold | -| `contrafold` | CONTRAfold | -| `vienna` | ViennaRNA | -| `rnastructure` | RNAstructure | | `rna-fm` | RNA-FM | +| `rnastructure` | RNAstructure | | `ufold` | UFold | -| `mxfold2` | MXfold2 | - -Install the environments with `pixi install --all`, then enter an environment -with `pixi shell -e `, or run a command directly with `pixi run -e -`. - -### Note on source-only neural models - -RibonanzaNet, RiNALMo, and UFold use pinned upstream source. Their `source` task -checks out a pinned upstream commit under `.pixi/model-sources/`, and `test` -runs this task automatically. For example: - -```bash -pixi run -e ufold source -pixi run -e ufold test -``` - -Neural model tests fetch verified public weights automatically. RiNALMo requires -a GPU. The other adapters test one short sequence on CPU. - -## Reproducing the predictions - -After verifying the contents of `sh/predict.sh` for your cluster, generate -predictions with a model and save them to -`../../data/2d/predictions/` with: - -```bash -pixi run -e predict -``` +| `vienna` | ViennaRNA | -To generate the leaderboard from the saved predictions: +Model sources and public weights are fetched automatically. Checkpoints use +`RNAGYM_CHECKPOINT_DIR`. RiNALMo tests require a GPU. -```bash -pixi run leaderboard -``` +Prediction jobs use the Slurm settings in [sh/predict.sh][2]. Outputs go to +`data/2d/predictions//`. Once the jobs finish, run `pixi run leaderboard` +to score the predictions and update the leaderboard CSV and README. -This writes the [2D leaderboard](../../leaderboard/2d/README.md) and its -[detailed scores](../../leaderboard/2d/leaderboard.csv). +[0]: ../../leaderboard/2d/ +[1]: pixi.toml +[2]: sh/predict.sh +[3]: ../../README.md#getting-started diff --git a/rnagym/s2d/models/eternafold.py b/rnagym/s2d/models/eternafold.py index a8794a3..cb5e122 100644 --- a/rnagym/s2d/models/eternafold.py +++ b/rnagym/s2d/models/eternafold.py @@ -13,5 +13,5 @@ def predict(sequence: str) -> Prediction: return _predict( sequence, ETERNAFOLD / "src" / "contrafold", - ETERNAFOLD / "parameters" / "EternaFoldParams.v1", + Config2D.CHECKPOINT_DIR / "eternafold" / "EternaFoldParams.v1", ) diff --git a/rnagym/s2d/models/mxfold2.py b/rnagym/s2d/models/mxfold2.py index 0becf27..c9a3814 100644 --- a/rnagym/s2d/models/mxfold2.py +++ b/rnagym/s2d/models/mxfold2.py @@ -9,6 +9,8 @@ import torch from mxfold2.predict import Predict +from rnagym.config import Config2D + from .utils import Prediction, decode_pair_probabilities, warn_out_of_range MODEL_DIR = Path(mxfold2.__file__).parent / "models" @@ -24,7 +26,9 @@ def _model(): predictor = Predict() model, _ = predictor.build_model(args) - weights = torch.load(MODEL_DIR / args.param, map_location="cpu") + weights = torch.load( + Config2D.CHECKPOINT_DIR / "mxfold2" / args.param, map_location="cpu" + ) model.load_state_dict(weights) model.eval() return model diff --git a/rnagym/s2d/models/ribonanzanet.py b/rnagym/s2d/models/ribonanzanet.py index 6e867a7..68d1504 100644 --- a/rnagym/s2d/models/ribonanzanet.py +++ b/rnagym/s2d/models/ribonanzanet.py @@ -13,7 +13,7 @@ MODEL_SOURCE = Config2D.MODEL_SOURCE_DIR / "rnet-inference" / "src" sys.path.insert(0, str(MODEL_SOURCE)) -# Official model definition and checkpoint loading +# Official model definition, with the checkpoint path patched by the source task # https://github.com/DasLab/rnet-inference/blob/25996e720f25fc3c0c7e9679a45d54ff2d5f5500/src/rnet_2d.py#L12-L31 from rnet_2d import RNA_Dataset # noqa: E402 from rnet_2d import model as MODEL # noqa: E402 diff --git a/rnagym/s2d/models/rinalmo.py b/rnagym/s2d/models/rinalmo.py index 5ae868c..e14a4fa 100644 --- a/rnagym/s2d/models/rinalmo.py +++ b/rnagym/s2d/models/rinalmo.py @@ -13,7 +13,7 @@ from .utils import Prediction, decode_pair_probabilities, dot_bracket MODEL_SOURCE = Config2D.MODEL_SOURCE_DIR / "RiNALMo" -MODEL_WEIGHTS = Config2D.MODEL_WEIGHT_DIR / "rinalmo" / "rinalmo_giga_ss_bprna_ft.pt" +MODEL_WEIGHTS = Config2D.CHECKPOINT_DIR / "rinalmo" / "rinalmo_giga_ss_bprna_ft.pt" DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") MAX_BATCH_PAIR_CELLS = 2_000_000 MAX_BATCH_SIZE = 256 diff --git a/rnagym/s2d/models/rna_fm.py b/rnagym/s2d/models/rna_fm.py index eabd680..577fe37 100644 --- a/rnagym/s2d/models/rna_fm.py +++ b/rnagym/s2d/models/rna_fm.py @@ -10,8 +10,8 @@ from .utils import Prediction, decode_pair_probabilities, dot_bracket -MODEL_WEIGHTS = Config2D.MODEL_WEIGHT_DIR / "rna-fm" / "RNA-FM-ResNet_PDB-All.pth" -TORCH_HOME = Config2D.MODEL_WEIGHT_DIR / "rna-fm" +MODEL_WEIGHTS = Config2D.CHECKPOINT_DIR / "rna-fm" / "RNA-FM-ResNet_PDB-All.pth" +TORCH_HOME = Config2D.CHECKPOINT_DIR / "rna-fm" DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") diff --git a/rnagym/s2d/models/ufold.py b/rnagym/s2d/models/ufold.py index eb83ac6..917b575 100644 --- a/rnagym/s2d/models/ufold.py +++ b/rnagym/s2d/models/ufold.py @@ -12,7 +12,7 @@ from .utils import Prediction, decode_pair_probabilities, dot_bracket MODEL_SOURCE = Config2D.MODEL_SOURCE_DIR / "UFold" -MODEL_WEIGHTS = Config2D.MODEL_WEIGHT_DIR / "ufold" / "ufold_train_alldata.pt" +MODEL_WEIGHTS = Config2D.CHECKPOINT_DIR / "ufold" / "ufold_train_alldata.pt" DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") # Pre-processing diff --git a/rnagym/s2d/pixi.toml b/rnagym/s2d/pixi.toml index 6179648..d089a89 100644 --- a/rnagym/s2d/pixi.toml +++ b/rnagym/s2d/pixi.toml @@ -64,17 +64,19 @@ make = "4.*" [feature.eternafold.tasks] source = "bash sh/fetch-source.sh eternafold" +weights = "bash sh/fetch-weight.sh eternafold" [feature.eternafold.tasks.build] cmd = "make -C .pixi/model-sources/EternaFold/src contrafold CXX=\"$CXX\"" depends-on = ["source"] [feature.eternafold.tasks.test] -depends-on = ["build", "test-model"] +cmd = "CUDA_VISIBLE_DEVICES='' python -m pytest -q ../../tests/test_s2d.py" +depends-on = ["build", "weights"] [feature.eternafold.tasks.predict] cmd = "bash sh/predict.sh eternafold" -depends-on = ["build"] +depends-on = ["build", "weights"] [feature.rnastructure.dependencies] rnastructure = "==6.4" @@ -109,13 +111,15 @@ rev = "c82a30460684c7301086d84185a1927b1c256afa" [feature.ribonanzanet.tasks] source = "bash sh/fetch-source.sh ribonanzanet" +weights = "bash sh/fetch-weight.sh ribonanzanet" [feature.ribonanzanet.tasks.test] -depends-on = ["source", "test-model"] +cmd = "CUDA_VISIBLE_DEVICES='' python -m pytest -q ../../tests/test_s2d.py" +depends-on = ["source", "weights"] [feature.ribonanzanet.tasks.predict] cmd = "bash sh/predict.sh ribonanzanet" -depends-on = ["source"] +depends-on = ["source", "weights"] [feature.rinalmo.dependencies] python = "3.11.*" @@ -164,7 +168,8 @@ source = "bash sh/fetch-source.sh ufold" weights = "bash sh/fetch-weight.sh ufold" [feature.ufold.tasks.test] -depends-on = ["source", "weights", "test-model"] +cmd = "CUDA_VISIBLE_DEVICES='' python -m pytest -q ../../tests/test_s2d.py" +depends-on = ["source", "weights"] [feature.ufold.tasks.predict] cmd = "bash sh/predict.sh ufold" @@ -186,7 +191,8 @@ scipy = "==1.13.1" weights = "bash sh/fetch-weight.sh rna-fm" [feature.rna-fm.tasks.test] -depends-on = ["weights", "test-model"] +cmd = "CUDA_VISIBLE_DEVICES='' python -m pytest -q ../../tests/test_s2d.py" +depends-on = ["weights"] [feature.rna-fm.tasks.predict] cmd = "bash sh/predict.sh rna-fm" @@ -204,8 +210,15 @@ polars = "==1.43.0" mxfold2 = { url = "https://github.com/mxfold/mxfold2/releases/download/v0.1.2/mxfold2-0.1.2-cp310-cp310-manylinux_2_17_x86_64.whl" } [feature.mxfold2.tasks] -test = { depends-on = ["test-model"] } -predict = "bash sh/predict.sh mxfold2" +weights = "bash sh/fetch-weight.sh mxfold2" + +[feature.mxfold2.tasks.test] +cmd = "CUDA_VISIBLE_DEVICES='' python -m pytest -q ../../tests/test_s2d.py" +depends-on = ["weights"] + +[feature.mxfold2.tasks.predict] +cmd = "bash sh/predict.sh mxfold2" +depends-on = ["weights"] [environments] default = { features = ["shared"] } diff --git a/rnagym/s2d/sh/fetch-source.sh b/rnagym/s2d/sh/fetch-source.sh index 45a2082..5d96872 100644 --- a/rnagym/s2d/sh/fetch-source.sh +++ b/rnagym/s2d/sh/fetch-source.sh @@ -46,6 +46,13 @@ git -C "$destination" fetch origin "$revision" git -C "$destination" checkout --detach "$revision" test "$(git -C "$destination" rev-parse HEAD)" = "$revision" +if [[ $1 == ribonanzanet ]]; then + patch="$PROJECT_ROOT/sh/ribonanzanet-weights.patch" + if ! git -C "$destination" apply --reverse --check "$patch" 2>/dev/null; then + git -C "$destination" apply "$patch" + fi +fi + if [[ $1 == rna-assessment ]]; then chmod +x "$destination/MC-Annotate" fi diff --git a/rnagym/s2d/sh/fetch-weight.sh b/rnagym/s2d/sh/fetch-weight.sh index 6e5e68f..fa26c03 100644 --- a/rnagym/s2d/sh/fetch-weight.sh +++ b/rnagym/s2d/sh/fetch-weight.sh @@ -1,38 +1,48 @@ #!/usr/bin/env bash set -euo pipefail -download() { - local url=$1 destination=$2 checksum=$3 - - mkdir -p "$(dirname "$destination")" - if ! echo "$checksum $destination" | sha256sum --check --status 2>/dev/null; then - curl -L --fail --retry 3 --output "$destination.tmp" "$url" - echo "$checksum $destination.tmp" | sha256sum --check --status - mv "$destination.tmp" "$destination" - fi -} +# shellcheck source=../sh/weights.sh +source "$(dirname -- "${BASH_SOURCE[0]}")/../../sh/weights.sh" case $1 in +eternafold) + download \ + https://raw.githubusercontent.com/eternagame/EternaFold/702d3e485e768a6f2355d5d065e1241b04618e61/parameters/EternaFoldParams.v1 \ + "$checkpoint_root/eternafold/EternaFoldParams.v1" \ + 1421e89cc8df24b53a320eff6c72b8acfdb2771faba29ab9c798f7bdfc853a8a + ;; +mxfold2) + download \ + https://raw.githubusercontent.com/keio-bioinformatics/mxfold2/51b213676708bebd664f0c40873a46e09353e1ee/mxfold2/models/TrainSetAB.pth \ + "$checkpoint_root/mxfold2/TrainSetAB.pth" \ + 8ba1283e70c1c073033161adc120f67cdc467176476358d26a12cadaf63262ec + ;; +ribonanzanet) + download \ + https://raw.githubusercontent.com/DasLab/rnet-inference/25996e720f25fc3c0c7e9679a45d54ff2d5f5500/RibonanzaNet-Weights/RibonanzaNet-SS.pt \ + "$checkpoint_root/ribonanzanet/RibonanzaNet-SS.pt" \ + 626060952368affbf61b78b532d6166387094754b68bc0553da376f2d2b00d56 + ;; rinalmo) download \ https://zenodo.org/api/records/15043668/files/rinalmo_giga_ss_bprna_ft.pt/content \ - .pixi/model-weights/rinalmo/rinalmo_giga_ss_bprna_ft.pt \ + "$checkpoint_root/rinalmo/rinalmo_giga_ss_bprna_ft.pt" \ 44e377cb0c92f7b9cff8db8a38e33e5c0363d2f9a19831c6342346bb788a5e55 ;; ufold) download \ 'https://drive.usercontent.google.com/download?id=1DJDkKwZNdt-cwKPkDxu3ErWqoTK2k7uy&export=download&confirm=t' \ - .pixi/model-weights/ufold/ufold_train_alldata.pt \ + "$checkpoint_root/ufold/ufold_train_alldata.pt" \ ea84411dd59f230b0a79d984c116dad8c8a75bef1b9d0079376b581d0cdae64b ;; rna-fm) download \ https://huggingface.co/cuhkaih/rnafm/resolve/main/RNA-FM_pretrained.pth \ - .pixi/model-weights/rna-fm/hub/checkpoints/RNA-FM_pretrained.pth \ + "$checkpoint_root/rna-fm/hub/checkpoints/RNA-FM_pretrained.pth" \ 5b5d7d87b37c291ef42c140ef9edf7aea29f255fa2a4fd435f776c52e93d5e99 download \ https://huggingface.co/cuhkaih/rnafm/resolve/main/SS/RNA-FM-ResNet_PDB-All.pth \ - .pixi/model-weights/rna-fm/RNA-FM-ResNet_PDB-All.pth \ + "$checkpoint_root/rna-fm/RNA-FM-ResNet_PDB-All.pth" \ bc9df2111ae6ba95b373641ed1ab35ef4fa8b4cd0f8d21065a55450f575fe15d ;; *) diff --git a/rnagym/s2d/sh/format.sh b/rnagym/s2d/sh/format.sh index efbf86c..a4b0527 100644 --- a/rnagym/s2d/sh/format.sh +++ b/rnagym/s2d/sh/format.sh @@ -4,4 +4,4 @@ set -euo pipefail taplo format pixi.toml ruff check --fix --extend-select I . ../config.py ../sequences.py ../../tests/test_s2d.py ruff format . ../config.py ../sequences.py ../../tests/test_s2d.py -shfmt -w sh tasks/*.slurm +shfmt -w ../sh sh tasks/*.slurm diff --git a/rnagym/s2d/sh/lint.sh b/rnagym/s2d/sh/lint.sh index 2a07b7f..4afbaee 100644 --- a/rnagym/s2d/sh/lint.sh +++ b/rnagym/s2d/sh/lint.sh @@ -4,5 +4,5 @@ set -euo pipefail taplo lint pixi.toml ruff check --extend-select I . ../config.py ../sequences.py ../../tests/test_s2d.py ruff format --check . ../config.py ../sequences.py ../../tests/test_s2d.py -shfmt -d sh tasks/*.slurm -shellcheck sh/*.sh tasks/*.slurm +shfmt -d ../sh sh tasks/*.slurm +shellcheck ../sh/*.sh sh/*.sh tasks/*.slurm diff --git a/rnagym/s2d/sh/ribonanzanet-weights.patch b/rnagym/s2d/sh/ribonanzanet-weights.patch new file mode 100644 index 0000000..f03439d --- /dev/null +++ b/rnagym/s2d/sh/ribonanzanet-weights.patch @@ -0,0 +1,9 @@ +diff --git a/src/rnet_2d.py b/src/rnet_2d.py +--- a/src/rnet_2d.py ++++ b/src/rnet_2d.py +@@ -27,4 +27,4 @@ model = finetuned_RibonanzaNet(load_config_from_yaml(path.join(path.dirname(__file__ + if USE_GPU: + model = model.cuda() +-model.load_state_dict(torch.load(path.join(path.dirname(__file__), '../RibonanzaNet-Weights', 'RibonanzaNet-SS.pt'), map_location='cpu')) ++model.load_state_dict(torch.load(path.join(os.environ.get('RNAGYM_CHECKPOINT_DIR', '/n/lw_groups/marks/ckpt'), 'ribonanzanet', 'RibonanzaNet-SS.pt'), map_location='cpu')) + model.eval() diff --git a/rnagym/s3d/README.md b/rnagym/s3d/README.md index 01cf402..f68489e 100644 --- a/rnagym/s3d/README.md +++ b/rnagym/s3d/README.md @@ -1,68 +1,70 @@ -# RNAGym 3D structure benchmark +# Tertiary structure benchmark -This directory includes the code and environments used by RNAGym for 3D -structure prediction. The datasets are stored under -[`../../data/3d/`](../../data/3d/). +RNA 3D structure prediction on quality-filtered targets released after +2023-01-12 in the [RNA3DB][0] 2026-01-05 release. Repeated sequences with distinct +structures are retained. Predictions and MSAs are shared by sequence. +See the [leaderboard][1] for results. -## Datasets +## Setup -RNAGym retains every quality-filtered target released after 2023-01-12 from the -[RNA3DB][22] 2026-01-05 full release. Repeated sequences with distinct -structures are retained, while predictions and MSAs are generated once per -unique sequence. Model-specific training homology uses each model's structural -training cutoff. +Complete the [shared data setup][5], then run from `rnagym/s3d/`: -`data/3d/curation/annotated_chains.parquet` contains RNA3DB chains of at least 8 -nt and the Rfam, polymer coverage, resolution, and other metadata used to filter -them. +```bash +pixi install +``` -## Generating the datasets +The shared archive includes the datasets, MSAs and precomputed US-align outputs +under `data/3d/`. -Configure `Config3D` in [`rnagym/config.py`](../config.py), then reconstruct the -dataset from this directory with: +## Predict and evaluate ```bash -pixi run pipeline +pixi run -e predict ``` -This downloads the pinned RNA3DB release, annotates its chains, selects the -targets, and updates the shared sequence registry. Rfam 15.0 must contain a -pressed `Rfam.cm` and `family.txt.gz` at the paths defined by `Config3D`. Each -step can also be run individually with `pixi run `. - -### Training-structure comparisons +Replace `` with a model environment from [pixi.toml][3]: -`pixi run usalign` launches the missing target-to-training comparisons. -Completed outputs are reused. Run `pixi run split` afterward to add the results -to `rnagym_3d.parquet`. +| Environment | Model | +| --- | --- | +| `af3` | AlphaFold 3 | +| `nufold` | NuFold | +| `rf2na` | RoseTTAFold2NA | +| `rhofold` | RhoFold+ | +| `trrna` | trRosettaRNA | -The shared data archive described in the [root README](../../README.md) includes -the precomputed 3D US-align outputs under `data/3d/usalign/`. +Sources and public checkpoints are fetched automatically. Checkpoints use +`RNAGYM_CHECKPOINT_DIR`. AlphaFold 3 requires licensed parameters and its +databases. RF2NA requires PDB100 and must run after AF3 to reuse partner-chain +MSAs. Reference databases use `RNAGYM_DATABASE_DIR`. -## Launching RNA predictions +Prediction jobs use the Slurm settings in [sh/predict.sh][6]. Once they finish: -To launch predictions: +```bash +pixi run score +pixi run leaderboard +``` -1. Run `pixi run riboseek-db` once to prepare [Riboseek][21], RNAcentral, and - NCBI nt under `RNAGYM_DATABASE_DIR`. -2. Run `pixi run riboseek` to generate the MSAs. -3. Run `pixi run -e predict` for each model. Run RF2NA after AF3 because - it reuses AF3's partner-chain MSAs. +Predictions and scores go to `data/3d/`. `leaderboard` updates the leaderboard +CSV and README. -Public model sources and checkpoints are installed automatically. AlphaFold 3 -requires its licensed parameters and databases, while RF2NA requires its -official PDB100 database. Their expected locations are defined by `Config3D`. +## Rebuild data -## Analysis +To regenerate the shared datasets, check the paths in [Config3D][2], then run +`pixi run pipeline`. It downloads and filters RNA3DB and requires Rfam 15.0 +with a pressed `Rfam.cm` and `family.txt.gz`. -Score every available model and generate the -[3D leaderboard](../../leaderboard/3d/) with: +`pixi run usalign` submits comparisons against each model's training structures. +Once those jobs finish, run `pixi run split` to add the results to +`data/3d/rnagym_3d.parquet`. -```bash -pixi run score -pixi run leaderboard -``` +To regenerate MSAs, run `pixi run riboseek-db` to prepare [Riboseek][4] databases +under `RNAGYM_DATABASE_DIR`. Once those jobs finish, run `pixi run riboseek` and +wait for the alignments before predicting structures. - -[21]: https://github.com/steineggerlab/riboseek -[22]: https://github.com/marcellszi/rna3db/releases/tag/2026-01-05-full-release +[0]: https://github.com/marcellszi/rna3db/releases/tag/2026-01-05-full-release +[1]: ../../leaderboard/3d/ +[2]: ../config.py +[3]: pixi.toml +[4]: https://github.com/steineggerlab/riboseek +[5]: ../../README.md#getting-started +[6]: sh/predict.sh diff --git a/rnagym/s3d/sh/fetch-assets.sh b/rnagym/s3d/sh/fetch-assets.sh index 6d62e7a..42209cd 100755 --- a/rnagym/s3d/sh/fetch-assets.sh +++ b/rnagym/s3d/sh/fetch-assets.sh @@ -2,21 +2,12 @@ set -euo pipefail -checkpoint_root=${RNAGYM_CHECKPOINT_DIR:-/n/lw_groups/marks/ckpt} +# shellcheck source=../sh/weights.sh +source "$(dirname -- "${BASH_SOURCE[0]}")/../../sh/weights.sh" + download_root=.pixi/downloads source_root=.pixi/model-sources -download() { - local url=$1 destination=$2 checksum=$3 - - mkdir -p "$(dirname "$destination")" - if ! echo "$checksum $destination" | sha256sum --check --status 2>/dev/null; then - curl -L --fail --retry 3 --output "$destination.tmp" "$url" - echo "$checksum $destination.tmp" | sha256sum --check --status - mv "$destination.tmp" "$destination" - fi -} - link_latest() { local model=$1 version=$2 @@ -56,7 +47,7 @@ rhofold) ;; rf2na) root="$checkpoint_root/rosettafold2na/0.2" - archive="$download_root/RF2NA_apr23.tgz" + archive="$root/RF2NA_apr23.tgz" download https://files.ipd.uw.edu/dimaio/RF2NA_apr23.tgz \ "$archive" \ 1a5e9f6dc6fde298f883e4d58d7766d464053f200c5247ccf18f5cc8dfdb3809 @@ -73,10 +64,10 @@ trrna) source="$source_root/trRosettaRNA_v1.1" download \ https://yanglab.qd.sdu.edu.cn/trRosettaRNA/download/trRosettaRNA_v1.1.zip \ - "$download_root/trRosettaRNA_v1.1.zip" \ + "$root/trRosettaRNA_v1.1.zip" \ 31b1a889819b1f04ebdf6857f521bf10750d49b59c3ad1b407fa43b204f608c4 [[ -f "$source/predict.py" ]] || unzip -q \ - "$download_root/trRosettaRNA_v1.1.zip" -d "$source_root" + "$root/trRosettaRNA_v1.1.zip" -d "$source_root" if [[ ! -d "$root/params" ]]; then mkdir -p "$root" mv "$source/params" "$root/params" diff --git a/rnagym/s3d/sh/format.sh b/rnagym/s3d/sh/format.sh index 399d8cf..0234d2e 100644 --- a/rnagym/s3d/sh/format.sh +++ b/rnagym/s3d/sh/format.sh @@ -4,4 +4,4 @@ set -euo pipefail taplo format pixi.toml ruff check --fix --extend-select I . ../config.py ../sequences.py ../tasks ../../tests/test_s3d.py ruff format . ../config.py ../sequences.py ../tasks ../../tests/test_s3d.py -shfmt -w sh tasks/*.slurm +shfmt -w ../sh sh tasks/*.slurm diff --git a/rnagym/s3d/sh/lint.sh b/rnagym/s3d/sh/lint.sh index 31aecb7..a537d1f 100644 --- a/rnagym/s3d/sh/lint.sh +++ b/rnagym/s3d/sh/lint.sh @@ -4,5 +4,5 @@ set -euo pipefail taplo lint pixi.toml ruff check --extend-select I . ../config.py ../sequences.py ../tasks ../../tests/test_s3d.py ruff format --check . ../config.py ../sequences.py ../tasks ../../tests/test_s3d.py -shfmt -d sh tasks/*.slurm -shellcheck -e SC1091 sh/*.sh tasks/*.slurm +shfmt -d ../sh sh tasks/*.slurm +shellcheck -e SC1091 ../sh/*.sh sh/*.sh tasks/*.slurm diff --git a/rnagym/sh/weights.sh b/rnagym/sh/weights.sh new file mode 100644 index 0000000..0c856a7 --- /dev/null +++ b/rnagym/sh/weights.sh @@ -0,0 +1,26 @@ +#!/usr/bin/env bash + +checkpoint_root=${RNAGYM_CHECKPOINT_DIR:-/n/lw_groups/marks/ckpt} +umask 002 +mkdir -p "$checkpoint_root" +exec 9>"$checkpoint_root/.download.lock" +flock 9 + +download() { + local url=$1 destination=$2 checksum=$3 temporary + + mkdir -p "$(dirname -- "$destination")" + if ! printf '%s %s\n' "$checksum" "$destination" | sha256sum --check --status 2>/dev/null; then + temporary=$(mktemp "${destination}.XXXXXX") + if ! curl -L --fail --retry 3 --output "$temporary" "$url"; then + rm -f -- "$temporary" + return 1 + fi + if ! printf '%s %s\n' "$checksum" "$temporary" | sha256sum --check --status; then + rm -f -- "$temporary" + return 1 + fi + chmod 664 "$temporary" + mv -- "$temporary" "$destination" + fi +} diff --git a/tests/fixtures/fitness/Domingo_2018_tRNA.a3m b/tests/fixtures/fitness/Domingo_2018_tRNA.a3m new file mode 100644 index 0000000..a171c17 --- /dev/null +++ b/tests/fixtures/fitness/Domingo_2018_tRNA.a3m @@ -0,0 +1,2000 @@ +>sequence_0586129 Riboseek CM 30k x3 +GUUCCGUUGGCGUAAUGGUAACGCGUCUCCCUCCUAAGGAGAAGACUGCGGGUUCGAGUCCCGUACGGAACG +>URS00019C3D87 0 0.490 1.000E+00 18 70 72 1 37 39 +------------------UAAGGAG-----------A-----AGACUGCGGGUUCGAGUCCCGUACGGAAC- +>URS0001FFC9FB 25 0.618 1.000E+00 9 63 72 0 41 44 +---------G-------------CGUCUGACUUCUAAUCAGAAAGCUGCGGGUUCGAGUCCCGC-------- +>URS000319550C 16 0.708 1.000E+00 23 70 72 0 47 49 +-----------------------UGCCUCCUUGUCAAGGAGGAGAGCGCGGGUUCGAGUCCCGUCGGGGCC- +>URS000046E5E2 12 0.634 1.000E+00 63 23 72 8 48 49 +-----------------------CACCGCCCUGUCAAGGCGGAAGCUGCGGGUUCGAGCCCCGU-------- +>URS0000CFF332 5 0.658 1.000E+00 23 63 72 0 37 49 +-----------------------CACC---CUUCUAAGGUGAAGAUUACAGGUUCGAGUCCUGU-------- +>URS0000DBCF50 31 0.760 1.000E+00 56 7 72 0 49 50 +-------UGGCUCAGUGGCAGAGCGUUCGCCUAGUAAGCGAAAGGUCCAUAGUUCGA--------------- +>URS0001D3DB3D 11 0.755 1.000E+00 9 56 72 1 49 50 +---------GCCUAAUGGaUAAGGCGCCUCCCUCCUAAGGAGGAGAAUGCGGGUUCGA--------------- +>URS00034A2E33 30 0.847 1.000E+00 18 63 72 1 46 52 +------------------UAGAGCGACUCCCUCCUAAGGAGUAGGCCGCAGGUUCGAAUCCUGC-------- +>URS0003AD6979 36 0.847 1.000E+00 18 63 72 1 46 52 +------------------UAGAGCGUCUGCCUCCGGAGCAGAAGGUCGCAGGUUCGAAUCCUGU-------- +>URS0003A00CFF 41 0.840 1.000E+00 14 63 72 1 50 52 +--------------GUGGUACUACGUCUCCUUGCCAAGGAGAAGGUCGCGGGUUCGAUUCCCGU-------- +>URS0003249DD1 31 0.818 1.000E+00 20 63 72 0 43 52 +--------------------AGGCGUCUGCCUCCUAAGUAGAAGAUUGUGGGUUCGAGUCCCAC-------- +>URS0003285947 28 0.514 1.000E+00 0 69 72 0 52 53 +GCUCGUGUGGCGUAAUGGCAACGCGUCUGACUUCUAAUCAGAAGAUU-----------------CUGGGU-- +>URS000052C385 20 0.688 1.000E+00 63 19 72 8 52 53 +-------------------AGAGCACCGCCCUGUCAAGGCGGAAGCUGCGGGUUCGAGCCCCGC-------- +>URS0000E3570B 28 0.612 1.000E+00 9 70 72 0 48 53 +---------G-------------CGUCCCCCUCCUAAGGGGAAGGCCACAGGUUCGACUCCUGUCAGGGAC- +>URS000340CE07 32 0.645 1.000E+00 9 70 72 0 48 53 +---------G-------------CGUCCGCCUCCUAAGCGGAAGGCCGUGGGUUCGAAUCCCGCCGAGGAC- +>URS00034A3EE2 54 0.830 1.000E+00 9 61 72 1 53 54 +---------GCUCAGUGGUAGAGCAGCGGCCUUUUAAGCCGAAGGUCGUGGGUUCGAUCCCC---------- +>URS0003A233F7 34 0.795 1.000E+00 15 63 72 0 48 54 +---------------UGGUAGAGCAUCACCUUGCCAAGGUGAGGGUCGCGGGUUCGAGUCCCGU-------- +>URS000066A3DC 38 0.666 1.000E+00 7 63 72 4 53 54 +-------UG-------GAUAACGCGUCUGCCUACGGAGCAGAAGAUUGCAGGUUCGAAUCCUGC-------- +>URS0000B0E6FC 56 0.818 1.000E+00 7 61 72 0 54 55 +-------UAGCUCAGUGGUAGAGCAGCGGCCUUUUAAGCCGAAGGUCGUGGGUUCGAUCCCC---------- +>URS00033DF6DE 43 0.690 1.000E+00 9 63 72 0 54 55 +---------XXGCAACGGUAGCGCGUCUGACUCCAGAUCAGAAGGCUGCGUGUUCGAAUCACGU-------- +>URS0003414EE8 54 0.836 1.000E+00 9 63 72 0 54 55 +---------GCUCAGUGGUGGAGCGUCGCCUUGCCAAGGCGAAGGUCGCGGGUUCGAUCCCCGU-------- +>URS000322789E 48 0.745 1.000E+00 9 63 72 0 54 55 +---------XXGCAAUGGUAGCGCGUCUGACUCCAGAUCAGAAGGUUGCGUGUUCGAUUCACGU-------- +>URS0001E5FC0F 36 0.703 1.000E+00 9 62 72 1 54 55 +---------GCGCAAUGGCAGCGCGUGGCACUCCUAAUGCCGAGGCUCCGGGUUCGAGUCCCG--------- +>URS000339DAB0 57 0.781 1.000E+00 9 63 72 0 54 55 +---------GCGCAAUGGUAGCGCGUCUGACUCCAGAUCAGAAGGUUGCGUGUUCGAUUCACGU-------- +>URS0003285451 51 0.690 1.000E+00 9 63 72 0 54 55 +---------GCCUAAUGAUAAGGCGUCUGACUUCGAAUCAGAAGAUUGAGGGUUCGAGUCCCUU-------- +>URS0000E1302A 43 0.745 1.000E+00 7 61 72 0 54 55 +-------UGGCGCAGGGGGAGCGCGCCUCCCUUACAAGGAGGAGGUCGCAGGUUCAAAUCCU---------- +>URS000310CE3A 52 0.763 1.000E+00 9 63 72 0 54 55 +---------XCGCAAUGGUAGCGCGUCUGACUCCAGAUCAGAAGGUUGCGUGUUCGAUUCACGU-------- +>URS000202548C 41 0.800 1.000E+00 7 60 72 0 54 55 +-------UAGCUCAGuUGGUAGAGCAUCUCCCUUUUAAGGAGAGGGUCCUGGGUUCGAGCCC----------- +>URS00034A9A66 30 0.814 1.000E+00 9 60 72 1 54 55 +---------GCUCAGCugGAUAGAGCGACUCCCUCCUAAGGAGUAGGCCGCAGGUUCGAAUCC----------- +>URS00021A9A09 57 0.872 1.000E+00 7 61 72 0 54 55 +-------UGGCUCAGUGGUAGAGCAUCUCCUUGCCAAGGAGAGGGUCGCGGGUUCGAAUCCC---------- +>URS0000635EA1 43 0.690 1.000E+00 9 63 72 0 54 55 +---------GUGAAUGGAUAACGCGUCUGCCUACGGAGCAGAAGAUUGCAGGUUCGAAUCCUGC-------- +>URS0000C27E96 38 0.709 1.000E+00 9 63 72 0 54 55 +---------AGGCAAUGGUAGCGCGUUGGACUUCUAAUCCAAAGGUUGCGGGUCCGAGUCCCGC-------- +>URS000398CE25 50 0.836 1.000E+00 7 61 72 0 54 55 +-------UGGCUCAGUGGUAGAGCAACGCACUCGUAAUGCGUAGGUCGCGGGUUCGAUUCCC---------- +>URS0000844960 20 0.688 1.000E+00 63 19 72 8 52 55 +-------------------AGAGCACCGCCCUGUCAAGGCGGAAGCUGCGGGUUCGAGCCCCGC-------- +>URS00034913E8 33 0.843 1.000E+00 20 70 72 0 50 55 +--------------------GAGCGUCCCCCUCCUAAGGGGAAGGUCUCAGGUUCGAAUCCUGACCGGGGC- +>URS000328FAE9 38 0.690 1.000E+00 9 63 72 1 55 56 +---------GUUAAUGGAUAAGGCGUCUGACUCCGAAUCAGAAGAUUGCGAGUUCGAGUCUCGU-------- +>URS000347CC50 38 0.763 1.000E+00 9 62 72 1 55 56 +---------GCGCAGUcGGUAGCGCGUCUCGUUCGCAACGAGAAGGUCAGGGGUUCGAGUCCCC--------- +>URS0002789734 39 0.732 1.000E+00 63 9 72 0 55 56 +---------GCCGAGUGGCAAGGCACCUGCCCGGUAAGCAGAAGGGUGUGGGUUCgGAUUCCCGC-------- +>URS0003AB54D5 41 0.818 1.000E+00 9 62 72 1 55 56 +---------GCUCAGuUGGUAGAGCAUCACCUUGCCAAGGUGAGGGUCGCGGGUUCGAGUCCCG--------- +>URS0000DD1CF6 41 0.678 1.000E+00 61 6 72 0 55 56 +------GUGGCCAAGUGGCAAGGCAUCGCACUUGUAAUGCGAAGAUCCAGGGUUCAAUCCCC---------- +>URS0002737BBD 32 0.714 1.000E+00 63 9 72 0 55 56 +---------UCCGAGUGGCAAGGCACCUGCCCGGUAAGCAGAAGGGUGUGGGUUCgGAUUCCCGC-------- +>URS0003A91BB3 35 0.865 1.000E+00 19 70 72 0 51 56 +-------------------AGAGCGUCUGCCUCCGGAGCAGAAGGUCGCAGGUUCGAAUCCUGUCCGGGGC- +>URS000317A9A2 41 0.719 1.000E+00 7 63 72 0 55 56 +-------UAG-GCAAUGGUAGCGCGUUAGACUUCUAAUCUAAAGGCUGCGGGUUCGAGUCCCGC-------- +>URS00034B5530 60 0.836 1.000E+00 9 63 72 1 55 56 +---------GCUCAGUGGUAGAGCAUCAGCUUCCCAAGCUGAGGGUCGCGGGUUCGAUCCCCGU-------- +>URS0003293499 38 0.649 1.000E+00 7 63 72 0 55 56 +-------UAGCCUA-UGGUAAGGCGUUGCUUUCCGGUGGCAAAGAUUGCGGGUUCGAGUCCCGU-------- +>URS0000C80017 46 0.763 1.000E+00 9 63 72 1 55 56 +---------GCAUAGUGGUAGAGCGUCUCCUUGCCAAGGAGAAUGUCGAGAGUUCGAAUCUCUU-------- +>URS0000111768 26 0.666 1.000E+00 59 7 72 0 53 56 +-------UGGCCAAGUGGUAAGGCACCUCACUAGUAAUGAGGAGAUCGUCAGaUUCGAAUC------------ +>URS0000D39B29 32 0.791 1.000E+00 16 63 72 0 47 56 +----------------GGUAGAACGUCUCUUUUCCAAAGAGAAGGUCACGGGUUCGAGUCCCGU-------- +>URS000275E5F3 44 0.750 1.000E+00 63 9 72 0 55 56 +---------GCCUAGUGGCAUGGCACCUGCCCGGUAAGCAGAAGGUUGUGGGUUCgGAUUCCCGC-------- +>URS0003A61221 25 0.627 1.000E+00 6 64 72 1 55 56 +------GUAGU----UGGUAACGCGCGACCUUGCCAAGGUCGAGACCGCGGGUUCGAGUCCCGUC------- +>URS0002272986 34 0.745 1.000E+00 13 63 72 0 50 56 +-------------AACGGUAGCGCGUCUGACUCCAGAUCAGAAGGCUGGGGGUUCGAAUCCCUC-------- +>URS0001CA3309 43 0.732 1.000E+00 7 62 72 0 55 56 +-------UGGCGCAAUGGCAGCGCGUAGCACUCCUAAUGCUGAGGUUCCGGGUUCGAAUCCCG--------- +>URS00034B1982 53 0.781 1.000E+00 9 63 72 1 55 56 +---------GCUCAGUGGUAGAGCACCUCCUUGCCAAGGAGGACGUCGCGAGUUCGAGUCUCGU-------- +>URS0003A7B500 49 0.875 1.000E+00 7 61 72 0 55 56 +-------UAGCUCAGUGGaUAGAGCGUCCCCCUCCUAAGGGGAAGGUCGCAGGUUCGAUUCCU---------- +>URS00033EAC3E 42 0.600 1.000E+00 9 63 72 1 55 56 +---------GCCUAAUGGUAAGGCGUUGCUUUCCGGUGGCAAAGACUGUGGGUUCGAGUCCCAU-------- +>URS0000344340 21 0.553 1.000E+00 70 6 72 1 55 56 +------GUC----------AGAGCACCGCCCUGUCAAGGCGGAAGCUGCGGGUUCGAGCCCCGCCAGUCCC- +>URS0001D302AE 38 0.709 1.000E+00 7 61 72 1 55 56 +-------UAGCGCAAUGGUAGUGCGUAUCACUCCUAAUGCUGUGGCUUCGGGUUCGAAUCCC---------- +>URS000325CEDD 34 0.535 1.000E+00 0 70 72 0 54 56 +GGAUCUGUGGCGCAAUGGUAGCGCGUCUGACUCCAGAUCAGAAGGUUG----------------CGUGUUC- +>URS00032BE584 35 0.535 1.000E+00 0 70 72 0 54 56 +GGAUCCGUGGCGCAAUGGUAGCGCGUCUGACUCCAGAUCAGAAGGUUG----------------CGUGUUC- +>URS0000E38720 45 0.803 1.000E+00 7 61 72 0 55 56 +-------UAGCUCAGuUGGUAGAGCAUCUCCCUUUUAAGGAGAGGGUCCUGGGUUCGAGCCCC---------- +>URS00032A144D 35 0.535 1.000E+00 0 70 72 0 54 56 +GGAUCCGUGGCGCAAUGGUAGCGCGUCUGACUCCAGAUCAGAAGGUUG----------------CGUGUUC- +>URS0003A6B375 34 0.846 1.000E+00 19 70 72 0 51 56 +-------------------AGAGCGUCUGCCUCCGGAGCAGAAGGCCGCAGGUUCGAAUCCUGUCCGGGGC- +>URS00033F177E 30 0.521 1.000E+00 0 70 72 0 54 56 +GACUCCGUGGCGCAACGGUAGCGCGUCUGACUCCAGAUCAGAAGGCUG----------------CGUGUUC- +>URS0003294CD3 35 0.535 1.000E+00 0 70 72 0 54 56 +GGAUCCGUGGCGCAAUGGUAGCGCGUCUGACUCCAGAUCAGAAGGUUG----------------CGUGUUC- +>URS0001D60906 44 0.750 1.000E+00 7 62 72 0 55 56 +-------UGGCGCAAUGGUAGCGCGUAGCACUCCUAAUGCUGAGGUUCCGGGUUCGACUCCCG--------- +>URS0003257A6C 36 0.690 1.000E+00 9 63 72 1 55 56 +---------GCCACGGCAUAAGGCAUCGGCCUCCUAAGCCGGGGAUUGCGGGUUCGAGUCCCGC-------- +>URS0000AE3462 46 0.732 1.000E+00 7 62 72 0 55 56 +-------UAGCUCAACGGUAGAGCGACUCCCUGUUAAGGAGUGGGUUGUAGGUUCGAAUCCUA--------- +>URS00026E3F7F 42 0.767 1.000E+00 63 9 72 0 55 56 +---------GCUUAGUGGCAUGGCACCUGCCCGGUAAGCAGAAGGUUGUGGGUUCgGAUUCCCGC-------- +>URS00033B3FA3 34 0.535 1.000E+00 0 70 72 0 54 56 +GGAUCUGUGGCGCAAUGGUAGCGCGUCUGACUCCAGAUCAGAAGGUUG----------------CGUGUUC- +>URS0000CB44EA 45 0.803 1.000E+00 7 61 72 0 55 56 +-------UAGCUCAGcUGGUAGAGCAUCUCCCUUUUAAGGAGAGGGUCCUGGGUUCGAACCCC---------- +>URS00030B21A9 35 0.535 1.000E+00 0 70 72 0 54 56 +GGAUCCGUGGCGCAAUGGUAGCGCGUCUGACUCCAGAUCAGAAGGUUG----------------CGUGUUC- +>URS000322477D 33 0.492 1.000E+00 0 70 72 0 54 56 +GGAUGCGUGGCGUAAUGGUAGCGCGUCUGACUCCAGAUCAGAAGGUUA----------------CGUGUUC- +>URS0000C32BF2 33 0.745 1.000E+00 16 70 72 0 54 56 +----------------GGUAAGGCGUCGGUCUCGUAAACCGAAGAUCGCGGGUUCGAACCCCGUCCGUGCC- +>URS0002044C31 40 0.714 1.000E+00 7 62 72 0 55 56 +-------UGGCGCAACGGCAGCGCGUAGCACUCCUAAUGCUGAGGUUCCGGGUUCGAAUCCCG--------- +>URS00030C8C25 41 0.690 1.000E+00 9 63 72 1 55 56 +---------GCAUAAAGGUAGCGCGUCUGACUCCAGAUCAGAAGGCUGCGUGUUCGAAUCACAU-------- +>URS00032D01F7 57 0.781 1.000E+00 9 63 72 1 55 56 +---------GCGCAAUGGUAGCGCGUCUGACUCCAGAUCAGAAGGUUGCGUGUUCGAUUCACGU-------- +>URS0002E29B08 35 0.727 1.000E+00 9 63 72 3 53 56 +---------GU----UGGUAGAGCAUCACCUUGCCAAGGUGAGGGUCGCGGGUUCGAGUCCCGU-------- +>URS0000C276DF 26 0.569 1.000E+00 0 70 72 0 55 56 +GCCCGCGUGGCCUAAUGGaUAAGGCGUCUGCCUCCUAAGUAGAAGAUUG----------------UGGGUUC- +>URS0000CE94ED 47 0.732 1.000E+00 7 62 72 0 55 56 +-------UAGCUCAAUGGUAGAGCAGCGCCCUGUUAAGGCGUUGGUUGCAGGUUCGAGUCCUG--------- +>URS000327188B 39 0.549 1.000E+00 0 70 72 0 54 56 +GGAUCCGUGGCGCAAUGGUAGCGCGUCUGACUCCAGAUCAGAAGGUUG----------------CGGGUUC- +>URS000334A90E 39 0.600 1.000E+00 9 63 72 1 55 56 +---------GCCUAAUGGCAAGGCGUUGCUUUCCGGUGGCAAAGAUUGCGAGUUCGAAUCUCGU-------- +>URS000085A6ED 23 0.618 1.000E+00 63 9 72 8 56 57 +---------GGUCA------GAGCACCGCCCUGUCAAGGCGGAAGCUGCGGGUUCGAGCCCCGC-------- +>URS000218710F 41 0.684 1.000E+00 63 7 72 0 56 57 +-------UAGCUCAGUGGUAGAGCGUCAGUCUUGUAAACUGAAGGUCCGUAGUUUAAUCCUGGG-------- +>URS000065EEAF 51 0.736 1.000E+00 7 63 72 0 56 57 +-------UGGCGCAAUGGUAGCGCGUCUGACUCCAGAUCAGAAGGUUGGGUGUUCAAAUCACGU-------- +>URS00031E5474 50 0.719 1.000E+00 7 63 72 0 56 57 +-------UGGCGCAACGGUAGCGCGUCUGACUCCAGACCAGAAGGCUGCGUGUUCAAAUCACGU-------- +>URS0000DBE539 59 0.824 1.000E+00 7 63 72 0 56 57 +-------UGGCUCAGCGGUAGAGCAUCGCCUUGCCAAGGCGAGGGUCGCGGGUUCGAUUCCCGU-------- +>URS0000D8B004 50 0.736 1.000E+00 7 63 72 0 56 57 +-------UGGCGUAAUGGUAGCGCGUCUAACUCCAGAUCAGAAGGUUGCGUGUUUGAUUCACGU-------- +>URS0003A1D15F 58 0.763 1.000E+00 9 63 72 1 55 57 +---------GCUCAGUGGUAGAGCACUUCCUUGGUAAGGAAGAGGUCACGGGUUCAAGUCCCGU-------- +>URS00032BD2B1 68 0.807 1.000E+00 7 63 72 0 56 57 +-------UAGCUCAGUGGUAGAGCAUCUGCCUUGCAAGCAGAAGGUCCCAGGUUCAAUUCCUGG-------- +>URS00030D7400 60 0.807 1.000E+00 7 63 72 0 56 57 +-------UAGCUCAGUGGUAGAGCAUCUGCCUUGCAUGCAGAAGGUCCCGGGUUCAAUUCCUGA-------- +>URS000301ABDF 51 0.745 1.000E+00 9 63 72 1 55 57 +---------GCUCAAUGGUAGAGCAGCGCCCUUUUAAGGCGUUGGUUGUGGGUUCGAAUCCCAC-------- +>URS000332070F 50 0.789 1.000E+00 7 63 72 0 56 57 +-------UAGCGUGUUGGUAGCGCGUCUGUCUUGUAAGCACAAAGUCGCGGGUUCGAUUCCCGC-------- +>URS000315CAFC 46 0.701 1.000E+00 7 63 72 0 56 57 +-------UAACUCAACGGCAGAGUGUCACCUUCCCAAGGUGGAGGCUGCGGGUUCAAAUCCCGU-------- +>URS0002799FC3 40 0.678 1.000E+00 63 9 72 0 55 57 +---------GCCUAGUGGCAUGGCAUCUGCCCGGUAAGCGGAACAUUGUGGGUUCgGAUUCCCAU-------- +>URS0003A9FE33 61 0.818 1.000E+00 9 63 72 1 55 57 +---------GCUCAGUGGUAGAGCGCUUCCUUGGUAAGGAAGAGGUCGCGGGUUCAAUCCCCGC-------- +>URS000319ACDE 47 0.684 1.000E+00 7 63 72 0 56 57 +-------UGGCGCAAUGGUAGCGCGCGAGACUUCUAAUCUCGAGGCUGUGGGUUCGAGUCCCAC-------- +>URS000255B2D3 55 0.854 1.000E+00 7 61 72 2 56 57 +-------UGGCUCAAUGGAAGAGCGUCUGCCUCCUAAGCAGAAGGUUCGGGGUUCGACCCCC---------- +>URS00032B51FB 49 0.719 1.000E+00 7 63 72 0 56 57 +-------UGGCGCAACGGUAGCGCGUCUGACUCCAGAUCAGAAGGUUGCGUGUUCAAAUCACUU-------- +>URS00031BFD0B 63 0.754 1.000E+00 7 63 72 0 56 57 +-------UAGCUCAGUGGUAGAGCACUUGCCUAGCAAGCACAAGGUCCUGGGUUCGAUCCCCAG-------- +>URS00032CD528 48 0.754 1.000E+00 7 63 72 0 56 57 +-------UGGCGCAAUGGUAGCGCGUCUAACUCCAUGUCAGAAGGCUGCGUGUUCGAUUCACGC-------- +>URS00030D5366 54 0.701 1.000E+00 7 63 72 0 56 57 +-------UGGUGUAACGGUAGCACGUCCGCCCGGCAAGUGGAAGGUCCCGGGUUCGAUCCCCGA-------- +>URS000336A8F2 62 0.771 1.000E+00 7 63 72 0 56 57 +-------UAGCUCAGUGGUAGAACAUCUGCCUUGCAAGCAGAAGGUUCCAGGUUCAAUUCCUGG-------- +>URS00030B4A98 59 0.771 1.000E+00 7 63 72 0 56 57 +-------UGGCGCAAUGGUAGCGCGUCUGACUCCAGAUCAGAAGGUUGCGUGUUCGAAUCACGU-------- +>URS00032696F7 34 0.660 1.000E+00 9 63 72 1 56 57 +---------GCCUAACGGaUAAGGCAUCGGUCUCCUAAACCGGGGACUGCGGGUUCGAGUCCCGC-------- +>URS00034CDC93 51 0.821 1.000E+00 9 63 72 1 56 57 +---------GCUCAGuUGGUAGAGCAUCAGCCUUCCAAGCUGAGGGUCGCGGGUUCGAGUCCCGU-------- +>URS0003213C41 59 0.701 1.000E+00 7 63 72 0 56 57 +-------UAGUGUAAUGGUAGCACGUCCGCCCGGCAAGUGGAAGGUCUCGGGUUCGAUCCCCGA-------- +>URS000336434D 49 0.754 1.000E+00 7 63 72 0 56 57 +-------UGGCGCAAUGGUAGCGCGUCUGACUCCAGAUCAGAAGGUUGGAUGUUCGAUUCACGU-------- +>URS00032BDE72 46 0.701 1.000E+00 7 63 72 0 56 57 +-------UGGCGCAACGGUAGCGCGUCUGACUCCAGAUCAGAAGGUUGCGUGUUCAAAUCAXXX-------- +>URS0003388C78 36 0.741 1.000E+00 7 63 72 0 56 57 +-------UGGCCUAAUGGaUAAUGCGUCUGCCUCCUAAGUAGAAGAUUGUGGGUUCAAG-CCCAU-------- +>URS0003193F6B 46 0.701 1.000E+00 7 63 72 0 56 57 +-------UGGCGCAACGGUAGCGCGUCUGACUCCAGAUCAGAAGGCUGCGUGCUCAAAUCACAC-------- +>URS0000CAD5E1 53 0.736 1.000E+00 7 63 72 0 56 57 +-------UGGCGCAAUGGUAGCGCGGCUCGCUGUUAACGAGAAGGUUGUAGGUUCGAAUCCUAC-------- +>URS0000D7A576 55 0.736 1.000E+00 7 63 72 0 56 57 +-------UGGCGCAAUGGUAGCGCGUCUGACUCCAGAUCAGAAGGCUGCGUGUUCAAGUCACGU-------- +>URS0000D8110A 46 0.803 1.000E+00 9 63 72 1 56 57 +---------GCAUAGuUGGUAAAGCGUCUGCCUUGUAAGCGCUAGGCCGCGGGUUCGAUUCCCGC-------- +>URS000313278C 57 0.807 1.000E+00 7 63 72 0 56 57 +-------UAGCUCAGUGGUAGAGCAUCUGCCUUGCAAGCAGAAGGUCCCAGGUUCAAUCCCCAC-------- +>URS0000C17070 54 0.736 1.000E+00 7 63 72 0 56 57 +-------UGGCGCAACGGUAGCGCGUCUGACUCCAGAUCAGAAGGUUGCGUGUUCAAAUCACGX-------- +>URS000310331D 55 0.807 1.000E+00 7 63 72 0 56 57 +-------UGGCGCAAUGGUAGCGCGUGCCCCUCCUAAGGGCAAGGUUGCGGGUUCGACUCCCGU-------- +>URS0000C08333 58 0.789 1.000E+00 7 63 72 0 56 57 +-------UGGCUAAGUGGUAUAGCGUCUCACUAGUAAUGAGAAGAUCACUGGUUCGAUUCCAGU-------- +>URS0000E519B8 63 0.824 1.000E+00 7 63 72 0 56 57 +-------UAGCUCAGUGGUAGAGCAUCGGCUUCCCAAGCCGAGGGUCGCGGGUUCGAAUCCCGU-------- +>URS0003A52C0E 46 0.803 1.000E+00 9 63 72 1 56 57 +---------GCUCAGuUGGUAGAGCAUCACCUUGCCAAGGUGAGGGUCGCGGGUUCGAGUCCCGU-------- +>URS00030C5A4F 46 0.719 1.000E+00 7 63 72 0 56 57 +-------UGGCGCAAUGGUAGCGCGUAGUCCUCCUAAGACUAAGGCUCAGGGUUCGAGUCCCUG-------- +>URS000274010F 38 0.696 1.000E+00 63 9 72 0 55 57 +---------GCCUAGUGGCAUAGCAUCUGCCCGGUAAGCGGAACAUUGUGGGUUCgGAUUCCCAU-------- +>URS0003B16EE9 60 0.818 1.000E+00 9 63 72 1 55 57 +---------GCUCAGUGGUAGAGCAGCGGCCUUUUAAGCCGAAGGUCGUGGGUUCGAUCCCCAC-------- +>URS00030B09ED 43 0.775 1.000E+00 6 63 72 3 56 57 +------UUGGU----UGGUAGUGCGUCUGCCUUGUAAGCACUAGGCCGCGGGUUCGAUUCCCGC-------- +>URS00026C16BA 33 0.714 1.000E+00 63 9 72 0 55 57 +---------GCCGUGUGGCAAGGCACCUGCCCGGUAAGCAGAAGGGUGUGGGUUCgGAUUCCCGC-------- +>URS00026D95CA 38 0.660 1.000E+00 63 9 72 0 55 57 +---------GCCUAGUGGCAUGGCAUCUGCCCGGUAAGCGGAAUACUGUGGGUUCgGAUUCCCAU-------- +>URS0003255D63 60 0.754 1.000E+00 7 63 72 0 56 57 +-------UGGCAUAGUGGUAGUGCGUUCGCUUCGUAAGCGAAAGGUCGCGAGUUCGAGCCUCGC-------- +>URS00032A50E5 34 0.736 1.000E+00 7 63 72 0 56 57 +-------UGGCGCAAUGGUAGCGCGUGGCCCUCCUAAGGCCAAGGCUGUGGGUACGAUUCACCU-------- +>URS00032B45FB 45 0.771 1.000E+00 7 63 72 0 56 57 +-------UAGCGUGUUGGUAGCGCGUCUGUCUUGUAAGCACAAAGUCGCGGGUUCGGUUCCCGC-------- +>URS00033E2F3B 37 0.696 1.000E+00 9 63 72 1 56 57 +---------GCCUAAUgGGUAAGGCGUCGGACUUCUAAUCCGAAGAUUGCGGGCUCGAGUCCCAC-------- +>URS0003385C81 45 0.732 1.000E+00 9 63 72 1 56 57 +---------GCCUAAUGGuUAAGGCGUCGGACUUCUAAUCCGAAGAUUGCGGGUUCGAGUCCCGC-------- +>URS0000D7B703 42 0.857 1.000E+00 15 70 72 0 55 57 +---------------UGGUAGCGCGUCUGCCUUGUAAGCGCUAGGCCGCGGGUUCGAUUCCCGCCCGGGGC- +>URS0002720C39 40 0.750 1.000E+00 63 9 72 0 55 57 +---------GCCUAGUGGCAUGGCACCUGCCCCGUAAGCAGAAGGUUAUGGGUUCgGAUUCCCGC-------- +>URS0003A5DDB3 62 0.854 1.000E+00 9 63 72 1 55 57 +---------GCGCAGUGGUAGCGCGUCUCCUUGCCAAGGAGAAGGUCGCGGGUUCGAAUCCCGU-------- +>URS000066BB17 40 0.732 1.000E+00 9 63 72 1 56 57 +---------GUCUAAUGGaUAUGACGUCGGCCUCCUAAGCCUAAGGUUGCGGGUUCGAGUCCCGU-------- +>URS00032A4E8D 54 0.754 1.000E+00 7 63 72 0 56 57 +-------UGGCGCAAUGGUAGCGCGUUUGACUCCAGAUUAGAAGGUUGCGUGUUCGAUACACGU-------- +>URS0003316420 60 0.771 1.000E+00 7 63 72 0 56 57 +-------UAGUUCAGUGGUAGAGCAUCUGCCUUGCAAGCAGAAGGUUCCAGGUUCAAUCCCCGG-------- +>URS00030FC82C 60 0.789 1.000E+00 7 63 72 0 56 57 +-------UGGCGCAAUGGUAGCGCGUCUGACUCCAGAUCAGAAGGUUGCGUGUUCGAUUCACGX-------- +>URS00033E3DE0 60 0.789 1.000E+00 7 63 72 0 56 57 +-------UAGCUCAGUGGUAGAGCAUCUGCCUUGCAAGCAGAAGGUCCCAGGUUCAAUCCCCAG-------- +>URS00019D7D8C 61 0.824 1.000E+00 7 63 72 0 56 57 +-------UGGCUCAGUGGUAGAGCAUCGCCUUGCCAAGGCGAGGGUCGCGGGUUCGAAUCCCGU-------- +>URS0000AEDCAA 43 0.842 1.000E+00 7 62 72 0 56 57 +-------UAGCUCAGuUGGUAGAGCGUCUCGUUGCCAACGAGAAGGUCGCGGGUUCGAAUCCCG--------- +>URS0003A973AC 35 0.727 1.000E+00 9 63 72 1 51 57 +---------GU----UGGUAGAGCAUCACCUUGCCAAGGUGAGGGUCGCGGGUUCGAGUCCCGU-------- +>URS000279F597 41 0.732 1.000E+00 63 9 72 0 55 57 +---------GCCUAGUGGCAUGGCAUCUGCCCGGUAAGCGGAACGUUGUGGGUUCgGAUUCCCGC-------- +>URS0003143951 54 0.736 1.000E+00 7 63 72 0 56 57 +-------UGGCGCAACGGUAGCGCGUCUGACUCCAGAUCAGAAGGUUGCGUGUUCAAAUCACGU-------- +>URS0000C25483 54 0.789 1.000E+00 7 63 72 0 56 57 +-------UGGCUCAAUGGUAGAGCGUUCGACUCAAAAACUAAAGGUCGCGGGUUCGAAUCCCAC-------- +>URS000338F964 39 0.785 1.000E+00 9 63 72 1 56 57 +---------GCCUAGuUAGUAGUGCGUCUGCCUUGUAAGCACUAGACCGCGGGUUCGAUUCCCGC-------- +>URS000329D6F6 47 0.821 1.000E+00 9 63 72 1 56 57 +---------GCGAAGcUGGUAGCGCGUCUGCCUUGUAAGCGCUAGGUCGCGGGUUCGAUUCCCGC-------- +>URS00032E1786 56 0.771 1.000E+00 7 63 72 0 56 57 +-------UGGCGCAAUGGUAGCGCGUCUGACUCCAGAUCAGAAGGUUGCGUGUUCGAUUCACXX-------- +>URS0002748ED5 38 0.696 1.000E+00 63 9 72 0 55 57 +---------GCUUAGUGGUAUGGCAUCUGCCCGGUAAGCGGAACACUGUGGGUUCaGAUUCCCAU-------- +>URS000334F5E0 58 0.859 1.000E+00 7 63 72 0 56 57 +-------UGGCUCAGGGGUAGAGCGUCUGUCUAGUAAACAGAAGGUCGCUGGUUCGAUUCCUGC-------- +>URS000329B1EA 51 0.719 1.000E+00 7 63 72 0 56 57 +-------UGGCGCAACGGUAGCGCGUCUGACUCCAGAUCAGAAGGCUGCGUGUUCGAAUCACXX-------- +>URS00026F1A90 42 0.678 1.000E+00 63 9 72 0 55 57 +---------GCCUAGUGGUAUGGCAUCUGCCCGGUAAGCGGGACAUUGUGGGUUCaGAUUCCCAU-------- +>URS00032B456E 64 0.771 1.000E+00 7 63 72 0 56 57 +-------UGGCUCAGUGGUAGAGCGCUUGCCUAGCAAGCGCAAGGUCCUGGGUUCGAUCCCCAG-------- +>URS0003176CEF 56 0.789 1.000E+00 7 63 72 0 56 57 +-------UAGCUCAUUGGUAAAGCGUUUGCCUGAUAAGUGAGAGAUCGCGGGUUCGAAUCCCGC-------- +>URS000321E3F6 55 0.762 1.000E+00 6 64 72 0 55 57 +------GUAGCUCAGUGGUAGAGCACCUGCUUUGCGUGCAGAAGGUCGCAGGUUCGAUUCC---C------- +>URS00033580AF 57 0.754 1.000E+00 7 63 72 0 56 57 +-------UGGCGCAAUGGUAGCGCGUCUGACUCCAGAUCAGAAGGUUGCGUGUUCAAAUCACGU-------- +>URS0003074B53 62 0.754 1.000E+00 7 63 72 0 56 57 +-------UGGCUCAAUGGCAGAGCACCUGCCUUGCAAGCAGAAGGUCCCAGGUUCAAUCCCUGG-------- +>URS00032CE7C0 44 0.724 1.000E+00 7 63 72 0 57 58 +-------UGGCCUAAUGGuUAAGGCGUCGGUCUUCUAAUCCGAAGAUUGCGGGUUCGAGUCCCAC-------- +>URS0003ABCDE6 38 0.750 1.000E+00 9 63 72 1 56 58 +---------GCGCAGuUGGUAGCGCAUCACCUUGCCAAGGUGAGGGUCGCGGGUUCGGGUCCCGU-------- +>URS0003AC37F3 45 0.842 1.000E+00 9 63 72 1 57 58 +---------GCUCAGUugGCUAGAGCGUCUCCUUGCCAAGGAGAAGGUCGCGGGUUCGAGUCCCGU-------- +>URS0000DA22FE 48 0.793 1.000E+00 7 63 72 0 57 58 +-------UAGCUUAGuUGGUAGCGCGUCUGCCUUGUAAGCGCUAGGCUGCGGGUUCGAUUCCCGC-------- +>URS000084640A 24 0.596 1.000E+00 63 7 72 8 57 58 +-------UG-------GUCAGAGCACCGCCCUGUCAAGGCGGAAGCUGCGGGUUCGAGCCCCGC-------- +>URS00006FDEDA 57 0.781 1.000E+00 7 61 72 0 54 58 +-------UGGCUCAGUGGUAGAGCGCUUGCCUUGCAAGCCUGAGGCCCUGGGUUCGAUUCCC---------- +>URS0002895C80 46 0.857 1.000E+00 9 63 72 1 56 58 +---------GCUCAGUcGGUAGAGCGGCUCCCUUUUAAGGAGAUGGUCGCAGGUUCGAUUCCUGC-------- +>URS00003D1992 22 0.673 1.000E+00 63 15 72 8 56 58 +---------------UGUCAGAGCACCGCCCUGUCAAGGCGGAAGCUGCGGGUUCGAGCCCCGC-------- +>URS000312E86E 44 0.724 1.000E+00 7 63 72 0 57 58 +-------UGGCCUAAUGGaUAAGGCGUCGGACUUCUAAUCCGAAGAUUGCGGGUUCGAGUCCCAC-------- +>URS00004F58CC 27 0.611 1.000E+00 4 70 72 0 56 58 +----CGGUU----------AUCACGUCGCCCUGUCACGGCGAAGAUCGCGGGUUCGAUUCCCGUCGGGACC- +>URS00006FAAAE 55 0.800 1.000E+00 7 61 72 0 54 58 +-------UGGCUCAGUGGUAGAGCGCUUGCCUCGCAAGCAUGAGGCCCUGGGUUCGAUUCCU---------- +>URS0002726ED6 49 0.785 1.000E+00 9 63 72 1 56 58 +---------GCUCAGuUGGUAGAGCAUCUCCCUUUUAAGGAGAGGGUCCUGGGUUCGAGCCCCAG-------- +>URS0000D8E593 50 0.827 1.000E+00 7 63 72 0 57 58 +-------UAGCUUAGuUGGUAGCGCGUCUGCCUUGUAAGCGCUAGGUCGCGGGUUCGAUUCCCGC-------- +>URS00033703F8 48 0.724 1.000E+00 7 63 72 0 57 58 +-------UGGCCUAAUGGaUAAGGCGUCGGCCUUCUAAUCCGAAGAUUGUGGGUUCGAGUCCCAC-------- +>URS0000E54E96 52 0.775 1.000E+00 7 63 72 0 57 58 +-------UAGCUCAGcUGGUAGAGCAUCUCCCUUUUAAGGAGAGGGUCCUGGGUUCGAACCCCAG-------- +>URS0002309C46 42 0.724 1.000E+00 63 7 72 0 57 58 +-------UAGCUCAGUGGUAGAGCAUUGGUCUAAUAAGCCAAAGGUCGUCGGUUCGaAGCCCGGC-------- +>URS000273EA7E 38 0.732 1.000E+00 63 9 72 0 55 58 +---------ACCUAGUGGCAUGGCACCUGCCCGGUAAGCAGAAGGUUGUGGGUUCgGAUUCCCGC-------- +>URS00033995C0 35 0.807 1.000E+00 6 57 72 6 57 58 +------GUGGCGCAAUGGUAGCGCGUCUGACUCCAGAUCAGAAGGUUGCGUGUUCGAU-------------- +>URS0001C1D3AC 45 0.750 1.000E+00 9 63 72 1 56 58 +---------GCGCAGuUGGUAGCGCGCCACCUUGCCAAGGUGGAGGUCGCGGGUUCGAACCCCGU-------- +>URS00030E9815 41 0.706 1.000E+00 7 63 72 0 57 58 +-------UGGUCUAAUGGaUAAGGCGUCGGACUUCUAAUCCGAAGAUUGCGGGUUCGAGUCCCAC-------- +>URS0003AD89A7 38 0.785 1.000E+00 9 63 72 1 56 58 +---------GCUCAUcCGGUAGAGCGACUGCUUCCCAAGCAGUAGGCGGCGGGUUCGAGUCCCGU-------- +>URS00033AE47F 40 0.724 1.000E+00 7 63 72 0 57 58 +-------UGGCGUAAUGGaUAAGGCGUCGGACUUCUAAUCCGAAGAUUGCGGGUUCGAGUCCCAC-------- +>URS0000C3B63D 32 0.719 1.000E+00 9 63 72 1 57 58 +---------GCGUAGCGGucUAACGCAUCUCCGUAUCAAGGAGAUGGUCGCGGGUUCGAAUCCCAC-------- +>URS0000D8B6E1 48 0.793 1.000E+00 7 63 72 0 57 58 +-------UAGCUUAGuUGGUAGUGCGUCUGCCUUGUAAGCGCUAGGCCGCGGGUUCGAGUCCCGC-------- +>URS000349188B 33 0.808 1.000E+00 17 63 72 0 46 58 +-----------------GUAGAGCAUCUCCUUGCCAAGGAGAAGGUCGCGAGUUCGAAUCUCGU-------- +>URS00025668A4 30 0.642 1.000E+00 9 63 72 1 56 58 +---------GCGUAGuUGGUAACGCGCGACCUUGCCAAGGUCGAGACCGCGGGUUCGAGCCCCGU-------- +>URS0001D7BA92 44 0.741 1.000E+00 7 63 72 0 57 58 +-------UGGCCCAAUGGaUAAGGCGCCACCCUCCUAAGGUGGAGAUUCCGGGUUCGAUCCCCGG-------- +>URS0000D9EDEA 49 0.827 1.000E+00 7 63 72 0 57 58 +-------UAGCUUAGuUGGUAGUGCGUCUGCCUUGUAAGCACUAGGCCGCGGGUUCGAUUCCCGC-------- +>URS000319A1FC 48 0.741 1.000E+00 7 63 72 0 57 58 +-------UGGCCUAAUGGaUAAGGCGUCGGACUUCUAAUCCGAAGAUUGCGGGUUCGAGUCCCGC-------- +>URS0000C1FE7B 32 0.689 1.000E+00 7 63 72 0 57 58 +-------UGGCCUAuUUGAUAAGGCGUCGCUCUCCUAAAGUGAAGAUUACAGGUUCGAGUCCUGC-------- +>URS0000DA25AC 50 0.793 1.000E+00 7 63 72 0 57 58 +-------UAGCUUAGuUGGUAAUGCGUCUGCCUUGUAAGCGCUAGGCCGCGGGUUCGAUUCCCGC-------- +>URS0000D9F497 42 0.857 1.000E+00 15 70 72 1 56 58 +---------------UGGUAGUGCGUCUGCCUUGUAAGCGCUAGGCCGCGGGUUCGAUUCCCGCCCGGGGC- +>URS000308B437 48 0.793 1.000E+00 7 63 72 0 57 58 +-------UAGCUUAGuUGGUAGCGCGUCUGCCUUGUAAGCGCUAGGCCGCGGGUUCGAAUCCCGC-------- +>URS0000D806CF 43 0.758 1.000E+00 7 63 72 0 57 58 +-------UAGCUUaAAUGGUAGCGCGUCCGCCUUUUAAGCGCUAGAUCGCGGGUUCGACUCCCGC-------- +>URS0000D7C9B7 51 0.810 1.000E+00 7 63 72 0 57 58 +-------UAGCUUAGuUGGUAGUGCGUCUGCCUUGUAAGCGAUAGGCCGCGGGUUCGAUUCCCGC-------- +>URS0000D89F16 48 0.793 1.000E+00 7 63 72 0 57 58 +-------UAGCUUAGuUGGUAGUGCGUCUGCCUUGUAAGCGCUAGGCUGCGGGUUCGAUUCCCGC-------- +>URS0000DB2A0C 48 0.793 1.000E+00 7 63 72 0 57 58 +-------UAGCAUAGuUGGUAGCGCGUCUGCCUUGUAAGCGCUAGGCCGCGGGUUCGAUUCCCGC-------- +>URS00033A6CF3 32 0.755 1.000E+00 15 63 72 1 49 58 +---------------UGGUAGCGCAUCUGCCUCGUAUGCAGAAGGCUGAGAGUUCGAGCCCCUC-------- +>URS000070D988 39 0.758 1.000E+00 7 63 72 0 57 58 +-------UGGCACAAuUGGCAGUGCGUCUCAGUACUAAUGAGGAGGUUGCGGGUUCAAGUCCCGC-------- +>URS0000D8955F 51 0.827 1.000E+00 7 63 72 0 57 58 +-------UAGCUUAGuUGGUAGUGCGUCUGCCUUGUAAGCGCAAGGCCGCGGGUUCGAUUCCCGC-------- +>URS00006E4431 55 0.781 1.000E+00 7 61 72 0 54 58 +-------UGGCUCAGUGGUAGAGCGCUUGCCUUGCAAGCAUGAGGCCCUGGGUUCGAUUCCU---------- +>URS000331CDC5 35 0.535 1.000E+00 0 70 72 0 54 58 +GGAUCCGUGGCGCAAUGGUAGCGCGUCUGACUCCAGAUCAGAAGGUUG----------------CGUGUUC- +>URS00006C013D 56 0.781 1.000E+00 7 61 72 0 54 58 +-------UGGCUCAGUGGUAGAGCGCUUGCCUUGCAAGCUGGAGGCCCUGGGUUCGAUUCCU---------- +>URS0000D87043 42 0.793 1.000E+00 7 63 72 0 57 58 +-------UAGCCUAGuUGGUAGCGCGUCUGCCUUGUAAGUACUAAGCCGCGGGUUCGAUUCCCGC-------- +>URS0000DAE344 47 0.793 1.000E+00 7 63 72 0 57 58 +-------UAGCUUAGuUGGUAGCGCGUCUGCCUUGUAAGCGCUAGACCGCGGGUUCGAUUCCCGC-------- +>URS0000749311 28 0.745 1.000E+00 7 57 72 7 57 58 +-------UGGCGCAACGGUAGCGCGUCUGACUCCAGAUCAGAAGGCUGGGGGUUCGAA-------------- +>URS00032574CD 49 0.724 1.000E+00 7 63 72 0 57 58 +-------UGGCCUAAUGGaUAAGGCGUCUGACUUCUAAUCCGAAGAUUGUGGGUUCGAGUCCCAC-------- +>URS0000D7B83C 48 0.793 1.000E+00 7 63 72 0 57 58 +-------UAGCUUAGuUGGUAGUGCGUCUGCCUUGUAAGCGCUAGGCCGCGGGUUCGAAUCCCGC-------- +>URS0000D7BA23 48 0.793 1.000E+00 7 63 72 0 57 58 +-------UAGCUUAGuUGGUAGUGCGUCUGCCUUGUAAGCGCUAGACCGCGGGUUCGAUUCCCGC-------- +>URS000287EDB2 51 0.821 1.000E+00 9 63 72 1 56 58 +---------GCUCAGuUGGUAGAGCAUCAGCCUUCCAAGCUGAGGGUCGCGGGUUCGAGUCCCGU-------- +>URS00032472D9 56 0.793 1.000E+00 6 63 72 0 57 58 +------UUGGCUCAGUGGUAGAGCAUCUGCUUCACAUGCAGAAGGUCCCGGGUUCAAUCCCUGA-------- +>URS00032809A6 44 0.724 1.000E+00 7 63 72 0 57 58 +-------UGGCCCAAUGGaUAAGGCACCGGCCUCCUAAGCCGGGGAUUGCGGGUUCGAGUCCCGU-------- +>URS0003A1EE2C 46 0.803 1.000E+00 9 63 72 1 56 58 +---------GCUCAGuUGGUAGAGCAUCACCUUGCCAAGGUGAGGGUCGCGGGUUCGAGUCCCGG-------- +>URS00027094D7 54 0.736 1.000E+00 7 63 72 0 56 58 +-------UGGCUCAAUGGUAGAGCAGCGGCCUUUUAAGCCGUUGGCUGUGGGUUCGAGUCCCAC-------- +>URS0000D7C7D0 44 0.810 1.000E+00 7 63 72 0 57 58 +-------UAGCUUGGuUGGUAGCGCGUCUGCCUUGUAAGCACUAGGCCGCGGGUUCGAUUCCCGC-------- +>URS0000D7D0F6 49 0.827 1.000E+00 7 63 72 0 57 58 +-------UAGCUUAGuUGGUAGUGCGUCUGCCUUCUAAGCGCUAGGCCGCGGGUUCGAUUCCCGC-------- +>URS00032A561B 48 0.706 1.000E+00 7 63 72 0 57 58 +-------UGGCCUAAUGGaUAAGGCAUCUGACUUCGGAUCAGAAGAUUGCGGGUUCGAGUCCCGU-------- +>URS0000DA4319 48 0.810 1.000E+00 7 63 72 0 57 58 +-------UAGCUUAGuUGGUAGCGCGUCUGCCUUGUAAGCGCUAGGCCGCGGGUUCGAUUCCCGC-------- +>URS0002E4F90B 39 0.714 1.000E+00 9 63 72 1 56 58 +---------GCCUAAUGGaUAAGGCGUCGGUCUCCUAAACCGAAGAUUGGGGGUUCGAGUCCCCC-------- +>URS0000DB6206 48 0.810 1.000E+00 7 63 72 0 57 58 +-------UAGCUUAGuUGGUAGUGCGUCUGCCUUGUAAGCACUAGACCGCGGGUUCGAUUCCCGC-------- +>URS0000B0C5C0 42 0.706 1.000E+00 7 63 72 0 57 58 +-------UAGCUUAGuUGGUAAAGCGCCACCUUGCCAAGGUGGAGACCGCGGGUUCGAGCCCCGU-------- +>URS0000720C48 57 0.800 1.000E+00 7 61 72 0 54 58 +-------UGGCUCAGUGGUAGAGCGCUUGCCUUGCAUGCAUGAGGUCCUGGGUUCGAUUCCC---------- +>URS0000DACD4F 55 0.763 1.000E+00 7 61 72 0 54 58 +-------UGGCUCAGUGGUAGAGCACUUGCCUUGCAAGCCGGAGGCCCUGGGUUCGAUUCCU---------- +>URS0000DA4722 49 0.827 1.000E+00 7 63 72 0 57 58 +-------UAGCUUAGuUGGUAGUGCGUCUGCCUCGUAAGCGCUAGGCCGCGGGUUCGAUUCCCGC-------- +>URS0000D92A5F 47 0.793 1.000E+00 7 63 72 0 57 58 +-------UAGCUUAGuUGGUAGUGCGUCUGCCUUUUAAGCGCUAGGCUGCGGGUUCGAUUCCCGC-------- +>URS00027B5C22 48 0.736 1.000E+00 63 9 72 0 56 58 +---------GCCUAGUGGUAUGGCAUCUGCCCGGUAAGCGGAAGGUCGUGGGUUuuCGAUUCCCAU-------- +>URS00034D1344 39 0.661 1.000E+00 6 70 72 0 53 58 +------AUA-----------GAGCGUCCGCCUCCUAAGCGGAAGGUCCCAGGUUCGAGUCCUGGUCGGGGC- +>URS0000C1FE42 35 0.611 1.000E+00 4 70 72 2 57 59 +----CGGUAGC-----------GCGUCUGACUCCAGAUCAGAAGGCUGCGUGUUCGAAUCACGUCGGGCUC- +>URS00006CC7E3 50 0.711 1.000E+00 6 64 72 0 58 59 +------GUGGCGCAAUGGUAGCGCGUCUGACUUCAGAUCAGAAGGUUGCGUGUUCAAAUCAUUUC------- +>URS0003281767 55 0.728 1.000E+00 6 64 72 0 58 59 +------GUGGCGCAACGGUAGCGCGUCUGACUCCAGAUCAGAAGGCUGCGUGUUCAAGUCACGUC------- +>URS00031AFB8D 48 0.661 1.000E+00 6 64 72 0 58 59 +------AUGGCGCAAUGGUAGCGCGCGAGACUUCUAAUCUCGAGGCUGUGGGUUCGAAUCCCACU------- +>URS000320BCA5 55 0.745 1.000E+00 6 64 72 0 58 59 +------GUGGCGCAACGGUAGCGCGUCUGACUCCAGAUCAGAAGGCUGCGUGUUCGAAUCACXCC------- +>URS000310D331 59 0.796 1.000E+00 6 64 72 0 58 59 +------GUGGCGCAAUGGUAGCGCGUCUGACUCCAGAUCAGAAGGUCGCGUGUUUGAUUCACGUC------- +>URS00032F573B 53 0.762 1.000E+00 6 64 72 0 58 59 +------GUGGUGCAGCGGUAGGGCGAUCGACUCCUGAUCGGAAGAUUGCGGGUUCGAUUCCCGCC------- +>URS0001CFD143 39 0.714 1.000E+00 7 62 72 3 58 59 +-------UGGCGCAAUGGCAGCGCGUGGCACUCCUAAUGCCGAGGCUCCGGGUUCGAGUCCCG--------- +>URS0000C5D6B4 61 0.779 1.000E+00 6 64 72 0 58 59 +------GUGGCGCAAUGGUAGCGCGUCUGACUCCAGAUCAGAAGGUUGCGUGUUCAAAUCACGCC------- +>URS00021A8FAE 62 0.779 1.000E+00 6 64 72 0 58 59 +------GUAGUUCAGCGGUAGAAUGUCCGCCUCGCAAGCGAGAGGUCGCGGGUUCGAAUCCCGCC------- +>URS0000829013 61 0.830 1.000E+00 6 64 72 0 58 59 +------GUGGCUCAGCGGUAGAGCAUCGCCUUGCCAAGGCGAGGGUCGCGGGUUCGAUUCCCGUC------- +>URS000331657A 40 0.627 1.000E+00 6 64 72 0 58 59 +------GUGGCCUAAUGGAGAUGCACUGGCCUCCUAAGCCAGGGAUUGUGGGUUCGAGUCCCAUC------- +>URS0001E935C7 40 0.714 1.000E+00 7 62 72 3 58 59 +-------UGGCGCAACGGUAGCGCGUGGCACUCCUAAUGCCGAGGCUCCGGGUUCGAGUCCCG--------- +>URS000335A24B 59 0.762 1.000E+00 6 64 72 0 58 59 +------GUGGCGCAACGGUAGCGCGUCUGACUCCAGAUCAGAAGGUUGCGUGUUCGAAUCACGUC------- +>URS0000C1653A 36 0.719 1.000E+00 7 63 72 1 50 59 +-------UG-------GAUAAGGCGUCUGCCUCCGAAGUAGAAGGGUGCGGGUUCGAGUCCCGU-------- +>URS0000DB3E63 40 0.758 1.000E+00 13 70 72 0 57 59 +-------------AAUGGUAGCGCGUCUGACUCCAGAUCAGAAGGUUGCGUGUUCGAUUCACGUCGGGUUC- +>URS00031FFBEC 55 0.711 1.000E+00 6 64 72 0 58 59 +------GUGGUGUAAUGGUAGCGCAUCUGACUCCAGAUCAGAAGGUUGUGUGUUCGAAUCACACC------- +>URS0000A52962 41 0.741 1.000E+00 7 63 72 0 57 59 +-------UAGCUUAGcUGGUAGAGCAUCUCCCUCAUAAAGAAAAGAUCGGAGGUUCGAAUCCUAU-------- +>URS0000D9508E 57 0.711 1.000E+00 6 64 72 0 58 59 +------GUGGCGCAAUGGUAGCGCGUCCGACUCCAGAUCGGAAGGCUGCGUGUUCAAAUCACGUC------- +>URS00032E0B6F 40 0.758 1.000E+00 13 70 72 0 57 59 +-------------AAUGGUAGCGCGUCUGACUCCAGAUCAGAAGGUUGCGUGUUCGAUUCACGUCAGGUUC- +>URS00001AC495 23 0.529 1.000E+00 70 3 72 1 58 59 +---AUUGUC----------AGAGCACCGCCCUGUCAAGGCGGAAGCUGCGGGUUCGAGCCCCGCCAGUCCC- +>URS0000AC9B9E 48 0.830 1.000E+00 7 63 72 0 58 59 +-------UAGCUCAGUugGCUAGAGCGUCUCCUUGCCAAGGAGAAGGUCGCGGGUUCGAAUCCCGU-------- +>URS000341DD4D 42 0.763 1.000E+00 9 63 72 2 56 59 +---------GCUCAAAGGUAGAGCGGCUGGCUGUUAACCAGACGAGUGCGGGUUCGAGUCCCGC-------- +>URS00033B242C 59 0.762 1.000E+00 6 64 72 0 58 59 +------GUGGCGCAAUGGUAGCGCGUCUGACUCAAGAUCACAAGGUUGCGUGUUCGAUUCACGUC------- +>URS000055D7E4 23 0.627 1.000E+00 63 13 72 8 58 59 +-------------AAUGGUAGAACAAUGGUCUCCAAAACCACAGGUUAAGGGUUCGAAUCCCUU-------- +>URS00032D542E 55 0.745 1.000E+00 6 64 72 0 58 59 +------GUAGCUCAGUGGUAGAGCAUCUGCCUUGCAUGCAGGAGAUCCCAGGUUCAAUCCCCAAC------- +>URS00032B43C7 59 0.800 1.000E+00 0 64 72 0 64 65 +GCCUCAUUGGCUCAGGGGCAGAGCGUCUGUCUAGUAAACAGAAGGUCGCUGGUUCGAUUCCAGCA------- +>URS0003413856 57 0.769 1.000E+00 3 67 72 0 64 65 +---CCGGUAGCUCAGGGGUAGAGCAAAUGACUCUUAAUCAUUAGGUCGAGGGUUCGAACCCCUCCCGG---- +>URS00034079CB 55 0.661 1.000E+00 3 67 72 0 64 65 +---GGCGUAGCUCAACGGUAGAGCAGCUGAUUUGUAAUCAGCAGGUUGCGGGUUCGAAUCCCAUCGCC---- +>URS00026959B4 61 0.692 1.000E+00 3 67 72 0 64 65 +---GGCGUAGCUCAACGGUAGAGCAUCUGCCUUGUAAGCAGAGGGUUGGGGGUUCAAAUCCCUUCGCC---- +>URS000340BC5C 59 0.784 1.000E+00 3 67 72 0 64 65 +---CCGGUAGCUCAGCGGUAGAGCAAGUGACUCUUAAUCACUAGGUCGUGGGUUCGAAUCCCACCCGG---- +>URS0002690A44 61 0.753 1.000E+00 3 67 72 0 64 65 +---UCUAUAGCUCAGGGGUAGAGCAUUUCCCUCGUAAAGAAAAGGUCGUGGGUUCGAAUCCCACUGGG---- +>URS000340E377 60 0.707 1.000E+00 3 67 72 0 64 65 +---GCCGUAGCUCAGUGGUAGAGCGCACCCUUGGUAAGGGUGAGGUCACGGGUUCAAAUCCCGUCGGU---- +>URS000269DFCE 59 0.676 1.000E+00 3 67 72 0 64 65 +---GGAGUGGCGCAACGGUAGCGCACCUGUCUUGUAAACAGGCGGUUGCGGGUUCGAUUCCCGUCUCC---- +>URS000327055D 55 0.692 1.000E+00 3 67 72 0 64 65 +---CUCGUGGCGCAACGGUAGCGUGUCUGACUCCAGAUCAGAAGAUUGCGUGUUCAAAUCACGUCGGG---- +>URS000268EEEE 55 0.692 1.000E+00 3 67 72 0 64 65 +---GGCGUAGCUCAACGGCAGAGCAGCUGCUUUGUAAGCAGCAGGUUGCGGGUUCGAAUCCCACCGCC---- +>URS000340585A 54 0.676 1.000E+00 3 67 72 0 64 65 +---GGUGUGGCCCAACGGUAGGGCAGCUCCCUGUUAAGGAGACGAUUGCAGGUUCGAAUCCUGCCACC---- +>URS0002F0D616 60 0.723 1.000E+00 3 67 72 0 64 65 +---ACUGUGGCUCAAUGGUAGAGCACAUCACUCGUAAUGAUGGGGUUGUGGGUUCGAUUCCCACCAGU---- +>URS00026938A0 56 0.707 1.000E+00 3 67 72 0 64 65 +---AGUGUGGCUCAAUGGUAGAGCAGCUCAUUCGUAAUGAGCAGGUUGGGGGUUCGAUUCCCUUCACU---- +>URS00026949F5 61 0.769 1.000E+00 3 67 72 0 64 65 +---CGAGUGGCUCAGUGGUAGAGCAUCGCCUUGCCAAGGCGAGGGUCGCGGGUCCGAAUCCCGUCUCG---- +>URS00026958C4 60 0.676 1.000E+00 3 67 72 0 64 65 +---GGAGUGGCGCAACGGUAGCGCACCUGUCUUGUAAACAGGUGGUUGCGGGUUCGAUUCCCGUCUCC---- +>URS000341CB40 68 0.753 1.000E+00 3 67 72 0 64 65 +---CAGGUAGCUCAGUGGUAGAGCACCUCCUUGGUAAGGAGGAGGUCGUGAGUUCGAUCCUCAUCCUG---- +>URS000010BFB5 57 0.707 1.000E+00 67 3 72 0 64 65 +---GCGGUGGCUCAAUGGUAGAGCUUUCGACUCCAGAUCGAAGGGUUGCAGGUUCAAUUCCUGUCCGU---- +>URS000340340D 59 0.707 1.000E+00 3 67 72 0 64 65 +---AGGGUAGCUCAGUGGCAGAGCACCGCUCUGGUAAUGCGGGGGUCGCGGGUUCGAAUCCCGUCCCU---- +>URS0002683E4C 54 0.692 1.000E+00 3 67 72 0 64 65 +---AGGGUAGCACAAUGGUAGUGCAUCUCACUUGUAAUGAGACGGUUGCGGGUUCGACUCCUGUUCCU---- +>URS000269542F 66 0.661 1.000E+00 3 67 72 0 64 65 +---AGCGUGGCUCAAUGGUAGAGCACCGGCCUUGUAAUCCGGUGGUUAUGGGUUCGAUUCCCAUCGCU---- +>URS0002681745 55 0.676 1.000E+00 3 67 72 0 64 65 +---GGCGUAGCUCAACGGUAGAGCGGCUGAUUUGUAAUCAGCAGGUUGCGGGUUCGAAUCCCAUCGCC---- +>URS0002687D34 59 0.707 1.000E+00 3 67 72 0 64 65 +---AGAAUAGCUCAGAGGUAGAGCAUCAGCUUCCCAAGCUGAGGGUCGCGGGUUCGAUCCCCGUUUCU---- +>URS00034022CF 60 0.692 1.000E+00 3 67 72 0 64 65 +---GCCGUAGCUCAGUGGUAGAGCGCUCCCUUGGUAAGGGCGAGGUCGUGGGUUCAAAUCCCAUCGGU---- +>URS000323AA0F 50 0.707 1.000E+00 3 67 72 0 64 65 +---AUGGUGGCCAAGUGGUAAGGCGUCGGUCUCGUAAACUGAAGAUCAUGGUUUCGAAUCCCGUCCGU---- +>URS0003411A97 57 0.753 1.000E+00 3 67 72 0 64 65 +---CCGGUAGCUCAGUGGUAGAGCAGCGGGCUCUUAACCCGUUGGUCGGGGGUUCAAAUCCCUCUCGG---- +>URS0000E24B28 49 0.753 1.000E+00 0 64 72 0 64 65 +GCCUCUGUAGCUCAAUGGAAGAGCAGUUCCGUCCUAAGGAAACGGUUGGGGGUUCGAGUCCCUCC------- +>URS0002695A5F 60 0.753 1.000E+00 3 67 72 0 64 65 +---CCGGUAGCUCAGCGGUAGAGCACGUGACUUUUAAUCAUGUGGUCGAGGGUUCGAUCCCCUCCCGG---- +>URS0003409D5B 63 0.769 1.000E+00 3 67 72 0 64 65 +---ACAGUAGCUCAGGGGUAGAGCGUCUGCUUCCCAAGCAGGAUGUCGAGGGUUCGAAUCCCUUCUGU---- +>URS0003403670 55 0.738 1.000E+00 3 67 72 0 64 65 +---CCGGUAGCUCAGGGGUAGAGCAGCGGACUUUUAAUCCGCGUGUCGUGGGUUCGAUUCCCAUCCGG---- +>URS000269DA07 58 0.784 1.000E+00 3 67 72 0 64 65 +---CCGGUAGCUCAGAGGUAGAGCAAAUGACUCUUAAUCAUUAGGUCGAGGGUUCGAAUCCCUCCCGG---- +>URS0003419634 69 0.769 1.000E+00 3 67 72 0 64 65 +---CGCGUAGCUCAGUGGUAGAGCAUCUGACUUUUAAUCAGAUGGUCGUGGGUUCGAUCCCCACCGCG---- +>URS000268AC9C 67 0.723 1.000E+00 3 67 72 0 64 65 +---UACGUGGCUCAGUGGUAGAGCACUUCCUUGGUAAGGAAGAGGUCACGGGUUCAAUUCCCGUCGUA---- +>URS00022F9938 56 0.692 1.000E+00 3 67 72 0 64 65 +---UGCUUAGCUCAGCGGUAGAGCGUCGCCCUUACAAGGCGAGGGUCGGGGGUUCGAUCCCCUCAGCA---- +>URS000269F61A 57 0.707 1.000E+00 3 67 72 0 64 65 +---UCGUUAGCUCAGUGGUAGAGCUGCGGACUUUUAAUCCGUAGGUCGUGGGUUCGACCCCCACACGA---- +>URS000340EF1F 64 0.769 1.000E+00 3 67 72 0 64 65 +---CCGGUAGCUCAGUGGUAGAGCAGCGGACUCUUAAUCCGCCGGCCGUGGGUUCGAAUCCCACCCGG---- +>URS00026998D5 65 0.661 1.000E+00 3 67 72 0 64 65 +---AGAGUGGCUCAAUGGUAGAGCACCGGCCUUGUAAUCCGGUGGUUAUGGGUUCGAUUCCCAUCUCU---- +>URS0002111EAF 65 0.753 1.000E+00 3 67 72 0 64 65 +---CGGUUAGCUCAGUGGUAGAGCGUCUGCCUUACAAGCAGAGGGUCACUGGUUCAAGUCCAGUACCG---- +>URS0003417117 55 0.692 1.000E+00 3 67 72 0 64 65 +---CGCGUAGCUCAAUGGUAGAGCAGUGGCCUUUUAAGCCAUUGGUUGCGGGUUCGAGUCCCUCCGCG---- +>URS0003402F95 50 0.646 1.000E+00 3 67 72 0 64 65 +---GGUGUGGCCAAAUGGUAAGGCAGCUCCCUGUUAAGGAGACGAUUGCAGGUUCGAAUCCUGCUACC---- +>URS0003402328 66 0.692 1.000E+00 3 67 72 0 64 65 +---UACGUGGCUCAGUGGUAGAGCACUCCCUUGGUAAGGGAGAGGUCGUGGGUUCAAUUCCCACCGUA---- +>URS000340906B 66 0.723 1.000E+00 3 67 72 0 64 65 +---CAUGUAGCUCAGGGGUAGAGCACUUCCUUGGUAAGGAAGAGGUCACGGGUUCGAAUCCCGUCAUG---- +>URS0002F0C061 54 0.723 1.000E+00 3 67 72 0 64 65 +---AGAGUAGUUCAGAGGUAGAGCGUCAGCUUCCCAAGCUGAAUGUCGCGGGUUCGAACCCCGUCUCU---- +>URS00026A11C2 69 0.784 1.000E+00 3 67 72 0 64 65 +---GUGUUAGCUCAGUGGUAGAGCAUCCGCCUCAUAAGCGGAAGGUCGCCGGUUCGAUUCCGGCACAC---- +>URS00027CCEB0 54 0.692 1.000E+00 3 67 72 0 64 65 +---UGAGUGACUCAAUGGUAGAGUGCCACCUUGCCAAGGUGGAAGUUGCGGGUUCGAGUCCCGUCUCG---- +>URS00033F3B48 54 0.692 1.000E+00 3 67 72 0 64 65 +---CUCGUGGCGCAACGGUAGCGCGUCUGACUCCAGCUCAGAAGGUUGUGUGUUCAAAUCACGUCGGG---- +>URS0000702FDE 56 0.707 1.000E+00 3 67 72 0 64 65 +---AUGGUGGCCAAGUGGUAAGGCGUCGGUCUCGUAAACCGAAGAUCAUGGGUUCAAAUCCCGUCCGU---- +>URS0000CD39CA 62 0.692 1.000E+00 3 67 72 0 64 65 +---UCGGUAGCUCAAUGGUAGAGCAUUCGGCUGUUAACCGAAGGGUUGUAGGUUCGAAUCCUACCCGG---- +>URS000268CBA1 55 0.661 1.000E+00 3 67 72 0 64 65 +---GGAGUGGCGCAACGGUAGCGCAACGGUCUUGUAAACCGUAGGUUGCGGGUUCGAUUCCCGUCUCC---- +>URS0003417CA7 56 0.723 1.000E+00 3 67 72 0 64 65 +---CCGGUAGCUCAGCGGAAGAGCUCCGGACUUUUAAUCCGGGAGUCGUGGGUUCGAAUCCCACCCGG---- +>URS0003101320 58 0.723 1.000E+00 3 67 72 0 64 65 +---CUCGUGGCGCAAUGGUAGCGCGUCUGACUCCAGAUCAGAAUGUUGCGUGUUCAAAUCACGUCGGG---- +>URS000269093C 67 0.784 1.000E+00 3 67 72 0 64 65 +---ACUGUGGCUCAAUGGCAGAGCAUCUCACUCGUAAUGAGAAGGUUGUGGGUUCGAUUCCCACCAGU---- +>URS0003413B0D 55 0.738 1.000E+00 3 67 72 0 64 65 +---CCGGUAGCUCAACGGCAGAGCGUCGGACUUUUAAUCCGAGCGUUGUGGGUUCGACUCCCACCCGG---- +>URS000268362F 63 0.723 1.000E+00 3 67 72 0 64 65 +---GCCGUGGCUCAAUGGUAGAGCACUUCACUCGUAAUGAAGGGGUUGCGAGUUCGAUUCUCGCCGGC---- +>URS0002F0F92B 67 0.769 1.000E+00 3 67 72 0 64 65 +---ACUGUGGCUCAAUGGUAGAGCAUCUCACUCGUAAUGAGAAGGUUGUGAGUUCGAUUCUCACCAGU---- +>URS0003415D87 68 0.769 1.000E+00 3 67 72 0 64 65 +---CAGGUAGCUCAGUGGUAGAGCACCUCCUUGGUAAGGAGGAGGUCGUGGGUUCAAGUCCCAUCCUG---- +>URS00030F58F6 65 0.753 1.000E+00 3 67 72 0 64 65 +---CCCGUGGCGCAACGGUAGCGCGUCUGACUCCAGAUCAGAAGGUUGCGUGUUCGAAUCACGUCGGG---- +>URS0002694C60 65 0.738 1.000E+00 3 67 72 0 64 65 +---AGAGUGGCUCAGUGGUAGAGCAUCGGCUUCCCAAGCCGAUGGUCGCGAGUUCGAAUCUCGUCUCU---- +>URS000341D350 58 0.784 1.000E+00 3 67 72 0 64 65 +---CCGGUAGCUCAGGGGUAGAGCAAAUGACUCUUAAUCAUUAGGUCGAGGGUUCGAAUCCCUCCCGG---- +>URS0002F0F5D3 58 0.692 1.000E+00 3 67 72 0 64 65 +---GGCGUGGCUCAACGGUAGAGCAGCUGACUUGUAAUCAGCAGGUUGGGGGUUCGAUUCCCUUCGCC---- +>URS00019F5752 58 0.738 1.000E+00 3 67 72 0 64 65 +---GGUGUAGCUCAGUGGUAGAGCAGCUGUUUCGUAAACAGCAGGUCGCGGGUUCGAUUCCCAUCACC---- +>URS000341B9B9 69 0.800 1.000E+00 3 67 72 0 64 65 +---CCGGUAGCUCAGAGGUAGAGCACCUGACUUUUAAUCAGAGGGUCGUGGGUUCGAUCCCCACCCGG---- +>URS0002688DD4 64 0.769 1.000E+00 3 67 72 0 64 65 +---ACUGUGGCUCAGCGGUAGAGCACCACCUUGGUAAGGUGGGGGUCGCGGGUUCGAUUCCCGCCAGU---- +>URS0002687400 62 0.723 1.000E+00 3 67 72 0 64 65 +---CAUAUGGCUCAGAGGUAGAGCACUUCCUUGGUAAGGAAGAGGUCGCGGGUUCAAGUCCCGCUAUG---- +>URS000340EED8 56 0.661 1.000E+00 3 67 72 0 64 65 +---GGCGUAGCUCAAUGGUAGAGCACCUGUUUUGUAAACAGGCGGUUGCGGGUUCGAGUCCCAUCGCC---- +>URS00034036B1 56 0.661 1.000E+00 3 67 72 0 64 65 +---GGCGUAGCUCAACGGUAGAGCACCUGUCUUGUAAACAGGGGGUUGCGGGUUCGAAUCCCUUCGCC---- +>URS00039925E5 58 0.676 1.000E+00 3 67 72 0 64 65 +---GGAAUAGCUCAAUGGUAGAGCAUCAGCCUUCCAAGCUGAGGGUUGCGGGUUCGAGUCUCGUUUCC---- +>URS0002689F58 66 0.661 1.000E+00 3 67 72 0 64 65 +---AGAGUGGCUCAAUGGUAGAGCACCGGCCUUGUAAUCCGGCGGUUAUGGGUUCGAUUCCCAUCUCU---- +>URS0002690E46 67 0.753 1.000E+00 3 67 72 0 64 65 +---CCGGUAGCUCAGUGGUAGAGCAUUCGACUUUUAAUCGAAUGGUCGUGGGUUCGAACCCCACCCGG---- +>URS0002699E40 64 0.753 1.000E+00 3 67 72 0 64 65 +---AGUGUGGCUCAAUGGCAGAGCAUCUCACUCGUAAUGAGAAGGUUGUGGGUUCGAUUCCCACCACU---- +>URS00026A14FF 58 0.692 1.000E+00 3 67 72 0 64 65 +---AGAGUAGCUCAGUGGUAGAGCACCGCUUUCGUAAAGCGAAGGUCACGGGUUCAAAUCCCGUCUCU---- +>URS000268A3D3 67 0.784 1.000E+00 3 67 72 0 64 65 +---GAAGUGGCUCAGUGGUAGAGCAUCUCCUUGCCAAGGAGAAGGCCGCGGGUUCGAAUCCCGUCUUC---- +>URS00026A0FF1 55 0.742 1.000E+00 3 67 72 0 65 66 +---CAGAUAGCUCAGuUGGUAGAGCAUCUCCCUUUUAAGGAGAUGGUCCUGGGUUCGAGCCCCAGUCUG---- +>URS00028A50F1 60 0.723 1.000E+00 6 70 72 0 64 66 +------GUAGCCUAAUGGUAAAGCGUCCGCCUCGGAAGCGGGAGGUCGUGGGUUCGAUCCCAGGCCAAGUC- +>URS00026872BD 56 0.772 1.000E+00 3 67 72 0 65 66 +---ACAGUGGCUCAGuUGGUAGAGCACCUCCUUGCCAAGGAGGAAGUCGCGGGUUCGAGUCCCGUCUGU---- +>URS000269139D 56 0.738 1.000E+00 3 67 72 0 64 66 +---AAAGUAGCUCAGUGGUAGAGCAGCUCUCUCGUAAAGAGCAGGUCGCGGGUUCAAUUCCCAUCUUU---- +>URS0000DB0CEA 60 0.753 1.000E+00 6 70 72 0 64 66 +------GUGGCGCAACGGUAGCGCGUCUGACUCCAGAUCAGAAGGUUGCGUGUUCAAAUCACGUCCGGGUC- +>URS0002F0FEED 62 0.707 1.000E+00 3 67 72 0 64 66 +---CCUUUAGCUCAGCGGUAGAGCAUCGGACUUUUAAUCCGUUGGUCGUGGGUUCGAUCCCCACAGGG---- +>URS00030CDC5B 44 0.647 1.000E+00 0 70 72 0 64 66 +GGAUCCGUGGCGCAAUGGUAGCGCGUCUGACUCCAGAUCAGAAGGUUGCGUGUUCG---CU---UGGGUUC- +>URS0001DE79DD 64 0.753 1.000E+00 3 67 72 0 64 66 +---ACCUUAGCUUAGUGGUAGAGCGUCUGCUUCGUAAGCAGAAGGUCGUCGGUUCAAUCCCGGCAGGU---- +>URS00026892F7 58 0.738 1.000E+00 3 67 72 0 64 66 +---AAAGUAGCUCAGUGGUAGAGCAGCUCACUCGUAAUGAGCAGGUCGCGGGUUCAAUUCCCAUCUUU---- +>URS0002685ED6 63 0.769 1.000E+00 3 67 72 0 64 66 +---CGAGUGGCUCAGUGGUAGAGCAUCGCCUUGCCAAGGCGAGGGCCGCGGGUUCGAAUCCCGUCUCG---- +>URS0000BFD261 55 0.712 1.000E+00 3 67 72 0 65 66 +---CGCGUGGCGCAAUgGGUAACGCGUCUGCCUACCGAGCAGAAGAUUGCAGGUUCGAGCCCUGCCGUG---- +>URS00026929A4 51 0.727 1.000E+00 3 67 72 0 65 66 +---AUAAUAGCUCAGuUGGUAGAGCGUCGGCUUCCCAAGCCGAAUGUCGCGGGUUCGAGUCCCGUUUAU---- +>URS0000825F2D 45 0.630 1.000E+00 67 3 72 1 65 66 +---CGCUUGGCGCAAUGGUAGCGCGUUCGACUCCAGAUCGAAAGGUUAGGCGUUCGAUCCCCUCAGUG---- +>URS000269F2E3 65 0.712 1.000E+00 2 67 72 0 65 66 +--UGCUGUAGCUCAGUGGUAGAGCGCUUCCUUGGUAAGGACGAGGUCAUGGGUUCGAGUCCCAUCAGC---- +>URS0003409DA1 51 0.712 1.000E+00 3 67 72 0 65 66 +---GGCGUAGCUCAAuUGGUAGAGCAACUGCCUUGUAAGCAGUAGGUUGCAGGUUCGAGUCCCGUCGCC---- +>URS000340CB36 65 0.738 1.000E+00 3 67 72 1 65 66 +---GGAGUAGCUCAGUGGUAGAGCAUCAGCCUUCCAAGCUGAGGGUCGCGGGUUCGACCCCCGUCUCC---- +>URS0003A14FDE 46 0.615 1.000E+00 6 70 72 0 64 66 +------GUGGUCUAGUGGUAUGAUACCUCCCUUACAAGGAGGGGAUCACGAGUUCGAGUCUCGUCGGAACC- +>URS0002682DFC 65 0.727 1.000E+00 2 67 72 0 65 66 +--UGCUGUAGCUCAGGGGUAGAGCACCUCCUUGGUAAGGAGGAGGUCACGGGUUCAAAUCCCGUCAGC---- +>URS00026827D5 57 0.723 1.000E+00 3 67 72 1 65 66 +---CCUGUAGCUCAAUGGUAGAGCCGCGGACUUUUAAUCCGUAGGUUGUGGGUUCGAGUCCCACCGGG---- +>URS0003ACF41B 65 0.753 1.000E+00 3 67 72 0 64 66 +---CGAGUAGUAGAGUGGUACUACGUCUCCUUGCCAAGGAGAAGGUCGCGGGUUCGAUUCCCGUCUCG---- +>URS000269E86C 42 0.651 1.000E+00 3 67 72 0 65 66 +---AUAGUAGCUUAGuUGGUAAAGCGCCACCUUGCCAAGGUGGAGAGCGCGGGUUCGAGCCCCGUCUAU---- +>URS000341E12E 60 0.707 1.000E+00 3 67 72 1 65 66 +---CCUGUAGUUCAACGGUAGAACGUCGCCCUUGCAAGGCGGAGGUUUCGGGUUCAAAUCCCGACGGG---- +>URS0002692E6B 59 0.753 1.000E+00 3 67 72 0 64 66 +---AAAGUGGCUCAGGGGUAGAGCAAUUCACUCGUAAUGAAUAGGUCGCGGGUUCGAUUCCCGCCUUU---- +>URS0003409358 61 0.707 1.000E+00 3 67 72 0 64 66 +---GCCGUAGCUCAGUGGUAGAGCGCACCCUUGGUAAGGGUGAGGUCACGGGUUCAAAUCCCGUCGGC---- +>URS000340CBDA 63 0.727 1.000E+00 2 67 72 0 65 66 +--CGUUGUAGCUCAGGGGUAGAGCGUUUCCUUGGUAAGGAAGAGGUCACGGGUUCAAUUCCCGUCAAU---- +>URS00030A62AC 51 0.696 1.000E+00 3 67 72 0 65 66 +---UCCGUGGCCCAAUGGaUAAGGCACCGGCCUCCUAAGCCGGGGAUUGCGGGUUCGAGUCCCGUCGGA---- +>URS0002685A70 58 0.738 1.000E+00 3 67 72 0 64 66 +---AAAGUAGCUCAGUGGUAGAGCAGCUCACUCGUAAUGAGCAGGUCGCGGGUUCGACUCCCAUCUUU---- +>URS000269554F 50 0.757 1.000E+00 3 67 72 0 65 66 +---ACAGUGGCUCAGuUGGUAGAGCACCUCCUUGCCAAGGAGGAAGUCGCUGGUUCGAGUCCCGUCUGU---- +>URS000341216D 59 0.787 1.000E+00 3 67 72 0 65 66 +---UCGUUAGCUCAGUcGGUAGAGCAUCUGCCUUUUAAGCAGAGGGUCGCCGGUUCGAGUCCGGCACGA---- +>URS000349340D 55 0.757 1.000E+00 3 67 72 0 65 66 +---GACGUAGCUCAGuUGGUAGAGCGUCGGCUUCCCAAGCCGAAGGUCGCGGGUUCGAGACCCGUCGUC---- +>URS00031241C9 50 0.696 1.000E+00 3 67 72 0 65 66 +---CCCUUGGCCUAAUGGaUAAGGCGUCGGACUGCUAAUCCGAAGAUUGCGGGUUCGAGUCCCACAGGG---- +>URS000340B22A 65 0.712 1.000E+00 2 67 72 0 65 66 +--UGCUGUAGCUCAGUGGUAGAGCGCCUCCUUGGUAAGGAUGAGGUCAUGGGUUCAAAUCCCAUCAGC---- +>URS0002686C3F 64 0.723 1.000E+00 3 67 72 1 65 66 +---GGAGUAGCUCAGUGGUAGAGCGCCUGCCUUCCAAGCAGGAUGUCGCGAGUUCGACCCUCGUCUCC---- +>URS0002F0A20A 59 0.707 1.000E+00 3 67 72 0 64 66 +---CGAGUGGCGAAGUGGUAUCGCAUCGCCUUGCCAAGGCGAGGGCCGCGGGUUCGAAUCCCGUCUCG---- +>URS0001E21388 64 0.738 1.000E+00 3 67 72 0 64 66 +---CGGUUAGCUCAGCGGUAGAGCGUCUGCCUUACAAGCAGAGGGUCACAGGUUCAAAUCCUGUACCG---- +>URS000269AE1E 55 0.676 1.000E+00 3 67 72 1 65 66 +---AGGAUAGCUCAAUGGUAGAGCUCCAGCCUUCCAAGCUGGAUGUUGCGGGUUCGAGCCCCGUUCCU---- +>URS0002698047 65 0.727 1.000E+00 2 67 72 0 65 66 +--UGGUGUGGCUCAAUGGUAGAGCAUCUCACUCGUAAUGAGAAGGUUGUGAGUUCGAUUCUCACCACC---- +>URS00034083AA 56 0.707 1.000E+00 3 67 72 0 64 66 +---UCGUUAGCUCAAUGGUAGAGCUGCUCCCUUUUAAGGAGUAGGUUGUAGGUUCAAAUCCUACACGA---- +>URS00026868BD 55 0.727 1.000E+00 3 67 72 0 65 66 +---AUAAUAGCUCAGuUGGUAGAGCGUUGGCUUCCCAAGCCGAAGGUCGCGGGUUCGAGUCCCGUUUAU---- +>URS000268A1E5 67 0.772 1.000E+00 2 67 72 0 65 66 +--UGCUGUGGCUCAAUGGCAGAGCAUCUCACUCGUAAUGAGAAGGUUGUGGGUUCGAUUCCCACCAGC---- +>URS0000D8D758 52 0.742 1.000E+00 3 67 72 0 65 66 +---UAGUUAGCUUAGuUGGUAGUGCGUCUGCCUUGUAAGCGCUAGGCCGCGGGUUCGAUUCCCGCACUA---- +>URS000331FA26 57 0.633 1.000E+00 0 70 72 0 64 66 +GGCCUUGUGGCGUA------ACGCGUCUGCCUACGGAGCAGAAGAUUGCUGGUUCGAAUCCAGUCAAGGUC- +>URS0002689B59 65 0.742 1.000E+00 2 67 72 0 65 66 +--UGGUGUGGCUCAAUGGCAGAGCAUCUCACUCGUAAUGAGAAGGUUGUGGGUUCGAUUCCCACCACC---- +>URS0002688F3F 61 0.818 1.000E+00 3 67 72 0 65 66 +---GCCUUAGCUCAGUcGGUAGAGCGUCUCACUCGUAAUGAGAAGGUCGCGGGUUCGAUUCCCGCAGGC---- +>URS000347A7D5 69 0.800 1.000E+00 3 67 72 0 64 66 +---CCCGUAGCUCAGUGGUAGAGCAUCUGCCUUUUAAGCAGAGGGUCGUUGGUUCGAUACCAACCGGG---- +>URS0003410AB7 61 0.738 1.000E+00 3 67 72 1 65 66 +---GUGGUAGUUCAGAGGCAGAACGCCUGCUUCCCAAGCAGGAGGUCGCGGGUUCAAAUCCCGUCCAC---- +>URS000341F51C 59 0.723 1.000E+00 3 67 72 1 65 66 +---UUGGUAGCUCAAUGGUAGAGCAGCGGCCUUUUAAGCCGUUGGCUGUGGGUUCGAGUCCCACCCGG---- +>URS0003B14770 53 0.727 1.000E+00 3 67 72 0 65 66 +---GCUAUGGCGCAGuUGGUAGCGCGUCUCGUUCGCAAUGAGAAGGUCGGGGGUUCGAAUCCCCCUAGC---- +>URS0002697AA6 58 0.692 1.000E+00 3 67 72 1 65 66 +---CCUAUAGCUCAAUGGUAGAGCAGCGGACUUUUAAUCCGUAGGUUGGGGGUUCGAGUCCCCCUGGG---- +>URS00021D75E9 57 0.757 1.000E+00 3 67 72 0 65 66 +---GGCGUGGUUCAGuUGGUAGAACGUCUCCUUGCCAAGGAGAAGGUCACGGGUUCGAGUCCCGUCGCC---- +>URS000268A712 66 0.800 1.000E+00 3 67 72 1 65 66 +---CGGGUAGCUCAGUGGUAGAGCAGCUGACUUUUAAUCAGCUGGUCGCGGGUUCGAUCCCCGCCCCG---- +>URS0002F0E596 45 0.696 1.000E+00 3 67 72 0 65 66 +---AUAGUGGCUCAGuUGGUAGAGCGCCACCUUGCCAAGGUGGAAACCGCGGGUUCGAGUCCCGUCUAU---- +>URS0003A38430 64 0.863 1.000E+00 3 67 72 0 65 66 +---CCUGUAGCUCAGUGGaUAGAGCGUCCCCCUCCUAAGGGGAAGGUCGCAGGUUCGAUUCCUGCCGGG---- +>URS00034211C0 64 0.696 1.000E+00 2 67 72 0 65 66 +--UGCUGUAGCUCAGUGGUAGAGCACUUCCUUGGUAAGGAAGAGGCCACGGGUUCAAGUCCCGUCAGC---- +>URS00034113B2 57 0.676 1.000E+00 3 67 72 0 64 66 +---UCGUUAGCUCAAUGGUAGAGCAGCGGACUCUUAAUCCGUUGGUUGCAGGUUCAAGUCCUGCACGA---- +>URS0002F0A81A 63 0.681 1.000E+00 2 67 72 0 65 66 +--UGUUAUAGCUCAGUGGUAGAGCACUUCCUUGGUAAGGAAGAGGUCGCGGGUUCAACUCCCGCUAAC---- +>URS0002682157 55 0.738 1.000E+00 3 67 72 1 65 66 +---GUAGUAACUCAGUGGUAGAGCGUCAGCUUCCCAAGCUGAAAGUCGCGGGUUCGACCCCCGUCUAC---- +>URS0002F0F106 66 0.784 1.000E+00 3 67 72 0 64 66 +---CGAGUGGCUCAGUGGUAGAGCAUCGCCUUGCCAAGGCGAGGGUCGCGGGUUCGAAUCCCGUCUCG---- +>URS0003407986 66 0.738 1.000E+00 3 67 72 1 65 66 +---GGAGUAGCUCAGUGGUAGAGCAUCUGCUUCCCAAGCAGAGGGUCGCGAGUUCGAGCCUCGUCUCC---- +>URS0000D0123E 54 0.769 1.000E+00 1 65 72 1 65 66 +-CCGGGGUAGCUCAACGGUAGAGCGACUCCCUGUUAAGGAGUGGGUUGCGCGUUCGAAUCGCGCCC------ +>URS0002698247 55 0.692 1.000E+00 3 67 72 0 64 66 +---UGCUUAGCUCAGUGGUAGAGCGUCGCCCUUACAAGGCGAGGGUCGGGGGUUCGAAACCCUCAGCA---- +>URS000268FB8E 42 0.615 1.000E+00 3 67 72 0 64 66 +---GGCAUCGCCAAGUGGUAAGGCAUCAGCUUCCCAAGCUGACAUCCGCGGGUUCGAGUCCCGUUGCC---- +>URS000341B930 67 0.681 1.000E+00 2 67 72 0 65 66 +--UGCUGUAGCUCAGUGGUAGAGCACUUCCUUGGUAAGGAAGAGGUCAUGGGUUCGAGCCCCAUCAGC---- +>URS000340991F 67 0.696 1.000E+00 2 67 72 0 65 66 +--UGCUAUAGCUCAGUGGUAGAGCACCUCCUUGGUAAGGAGGAGGUCCUGGGUUCGAGUCCCAGUAGC---- +>URS00019E8137 50 0.727 1.000E+00 3 67 72 0 65 66 +---UCGUUAGCUCAGcCGGUAGAGCAUCUCCCUUUUAAGGAGGGGGCCGUUGGUUCGAAUCCAACACGG---- +>URS0003415C46 57 0.636 1.000E+00 2 67 72 0 65 66 +--UGGUGUAGCUCAACGGUAGAGCACCUGAUUUGUAAUCAGGGGGUUGCGGGUUCGAAUCCCAUCGCC---- +>URS0002697E44 59 0.707 1.000E+00 3 67 72 0 64 66 +---AGAAUAGCUCAGAGGUAGAGCAUCAGCUUCCCAAGCUGAGGGUCGCGGGUUCGAUCCCCGUUUCU---- +>URS0003414093 51 0.712 1.000E+00 3 67 72 0 65 66 +---AGAGUAGCUCAAuUGGUAGAGCGGCAGCCUUCCAAGCUGUAGGUUGCGGGUUCGAGUCCCGUCUCU---- +>URS00031714D1 38 0.661 1.000E+00 0 70 72 0 64 66 +GAUACAGUAG------GAUAGAGCGCCUCCCUCCUAAGGAGGAAGUCAGGGGUUAGAAUCCCCUCUGGGAC- +>URS000340B584 57 0.676 1.000E+00 3 67 72 1 65 66 +---GGCGUAGCUCAAUGGUAGAGCGUCAGCCUUCCAAGCUGAAUGUUGUCGGUUCGAGUCCGAUCGCC---- +>URS000269EB45 45 0.651 1.000E+00 3 67 72 0 65 66 +---AUAGUAGCUUAGuUGGUAAAGCGCCACCUUGCCAAGGUGGAGACCGCGGGUUCGAGCCCCGUCUAU---- +>URS000269F882 45 0.630 1.000E+00 3 67 72 0 64 66 +---GGCAUCGCCAAGUGGUAAGGCAUCAGCUUCCCAAGCUGACAUUCGCGGGUUCGAGUCCCGUUGCC---- +>URS00034054A5 61 0.727 1.000E+00 2 67 72 0 65 66 +--UGCUGUAGCUCAGUGGUAGAGCGCGUCCUUGGUAAGGACGAGGUCACGGGUUCAAGUCCCGUCAGC---- +>URS000268F218 52 0.696 1.000E+00 3 67 72 0 65 66 +---UCUGUGGUGUAGUGGuUAACACGUCUGCCUGUCACGCAGAAGAUCGCGGGUUCAAGUCCCGUCGGA---- +>URS000269D583 58 0.696 1.000E+00 2 67 72 0 65 66 +--UGGUGUGGCUCAAUGGUAGAGCACAUCAUUCGUAAUGAUGGGGUUGCGGGUUCGAUUCCCGCCACC---- +>URS0003097874 47 0.727 1.000E+00 0 64 72 0 65 66 +GCCCUGCUGGCCCAAUGGaUAAGGCGCCACCCUCCUAAGGUGGAGAUUCCGGGUUCGAUCCCCGGG------- +>URS000224B6D0 46 0.630 1.000E+00 3 67 72 1 65 66 +---GGAGUGGUGUAGUGGUAUCACAUCUUCCUUACCCGCAGAAGGCCCCGGGUUCGACUCCCGGCUCC---- +>URS0002682EA2 45 0.545 1.000E+00 2 67 72 0 65 66 +--UGGUAUGGCCUAACGGUAAGGCAUCUCAUUCGUAAUGAGAGGAUUGUAAGUUCGAAUCUCACUACC---- +>URS0001EA8191 65 0.681 1.000E+00 2 67 72 0 65 66 +--UGCUGUAGCUCAGUGGUAGAGCACUUCCUUGGUAAGGAAGAGGUCAUGGGUUCAAGUCCCAUCAGC---- +>URS0003405B10 59 0.723 1.000E+00 3 67 72 1 65 66 +---GGCGUAGCUCAGCGGUAGAGCGUCAGCCUUCCAAGCUGAAUGUCGCGAGUUCGAUCCUCGUCGCC---- +>URS0002F0B063 57 0.692 1.000E+00 3 67 72 0 64 66 +---AGUGUGGCUCAACGGUAGAGCAACUGAUUUGUAAUCAGUAGGUUGCGGGUUCGAUUCCUGUCACU---- +>URS000341BA58 58 0.757 1.000E+00 3 67 72 0 65 66 +---AGAGUAGCUCAGuUGGUAGAGCGUCAGCCUUCCAAGCUGAGGGUCGCGGGUUCGAAUCCCGUCUCU---- +>URS000269CB54 65 0.661 1.000E+00 3 67 72 0 64 66 +---AGUGUGGCUCAAUGGUAGAGCACCGGCCUUGUAAUCCGGUGGUUAUGGGUUCGAUUCCCAUCACU---- +>URS0002685BF0 52 0.727 1.000E+00 3 67 72 0 65 66 +---ACUAUGGCUCAGGaGGUAGAGCGUCGCCUUGGUAAGGCGGAGGUCACGGGUUCGAGUCCCGUUAGU---- +>URS000268B36E 46 0.712 1.000E+00 3 67 72 0 65 66 +---AUAGUGGCUCAGuUGGUAGAGCGCCACCUUGCCAAGGUGGAAAUCGCGGGUUCGAGUCCCGUCUAU---- +>URS0002F0C7A6 44 0.712 1.000E+00 3 67 72 0 65 66 +---AUAGUGGCUCAGuUGGUAGAGCGCCACCUUGCCAAGGUGGAAACCGCGGGUUCGAGUCCCGUCUGU---- +>URS000395A836 53 0.723 1.000E+00 6 70 72 0 64 66 +------GUGGCGUAGCGGUAGCGCGUCUCCCUAGUAAGGAGAAGGUCAUCAGUUCAAUCCUGGUCUGAGGC- +>URS000269975B 56 0.676 1.000E+00 3 67 72 1 65 66 +---GGCGUAGCUCAAUGGUAGAGCGUCAGCCUUCCAAGCUGAAUGUUGACGGUUCGAGUCCGUUCGCC---- +>URS000341522F 53 0.661 1.000E+00 3 67 72 0 64 66 +---AGUGUAGCUCAAUGGUAGAGCACCGGUCUCCAAAACCGGGGGCUGCGGGUUCGAGUCCUGCCACU---- +>URS000340612C 46 0.712 1.000E+00 3 67 72 0 65 66 +---AGCGUAGCUCAAuUGGUAGAGCGUCACCCUUCCAAGGUGAAUGUUGCCGGUUCGAUUCCGGUCGCU---- +>URS00026916DA 62 0.818 1.000E+00 3 67 72 0 65 66 +---GCCUUAGCUCAGuUGGUAGAGCGUCUCACUCGUAAUGAGAAGGUCGCGGGUUCGAUUCCCGCAGGC---- +>URS0002693162 63 0.707 1.000E+00 3 67 72 1 65 66 +---GGAGUAGCUCAGUGGUAGAGCACCUGCCUUCCAAGCAGGAUGUCGCGAGUUCGACCCUCGUCUCC---- +>URS00034066DB 58 0.707 1.000E+00 3 67 72 1 65 66 +---GGCGUAGCUCAAUGGUAGAGCGUCAGCCUUCCAAGCUGAAUGUUGCCGGUUCGAGUCCGGUCGCC---- +>URS00026896AF 62 0.723 1.000E+00 3 67 72 0 64 66 +---CCUUUAGCUCAGCGGUAGAGCAUCGGACUUUUAAUCCGCAGGUCGUGGGUUCGAUCCCCACAGGG---- +>URS000069B162 65 0.769 1.000E+00 6 70 72 0 64 66 +------GUGGCGCAAUGGUAGCGCGUCUGACUCCAGAUCAGAAGGUUGCGUGUUCGAUUCACGUCGGGUUC- +>URS00034028E3 67 0.696 1.000E+00 2 67 72 0 65 66 +--UGCUGUAGCUCAGUGGUAGAGCACUUCCUUGGUAAGGAAGAGGUCAUGGGUUCGAGUCCCAUCAGC---- +>URS00034174F7 63 0.651 1.000E+00 2 67 72 0 65 66 +--UGUUGUAGCUCAGUGGUAGAGCACUUCCUUGGUAAGGAAGAGGUCAUGGGUUCAAGUCCCAUCAAC---- +>URS0002691ACE 61 0.666 1.000E+00 2 67 72 0 65 66 +--UGGUGUGGCUCAAUGGUAGAGCAGCUGACUUGUAAUCAGCAGGUUGUAGGUUCGAUUCCUAUCACC---- +>URS000340384C 66 0.712 1.000E+00 2 67 72 0 65 66 +--UGCUGUAGCUCAGUGGUAGAGCACUUCAUUGGUAAUGAAGAGGUCGCGGGUUCAAAUCCCGCCAGC---- +>URS000341B340 66 0.738 1.000E+00 3 67 72 0 64 66 +---CCCAUAGCUCAGCGGGAGAGCGCCUGCCUUGCAAGCAGGAGGUCUCGGGUUCAAAUCCCGAUGGG---- +>URS000340E8D1 56 0.707 1.000E+00 3 67 72 0 64 66 +---UCGUUAGCUCAAUGGUAGAGCAAUUGCCUUUUAAGCAAUUGGUUGGGGGUUCGAGUCCCUCACGA---- +>URS0003A5062F 58 0.676 1.000E+00 3 67 72 0 64 66 +---GCCUUGGCGCAACGGUAGCGCGUCUGACUCCAGAUCAGAAGGUUGCGUGUUCAAAUCACGUAGGC---- +>URS0001FF413D 66 0.738 1.000E+00 3 67 72 0 64 66 +---CGGUUAGCUCAGUGGUAGAGCGCCUGCCUUACAAGCAGGUGGUCACAGGUUCGAAUCCUGUACCG---- +>URS0003255A25 56 0.738 1.000E+00 6 70 72 0 64 66 +------UUGGCCAAGUGGUAAGGCGUCGCACUCGUAAUGCGAAGAUCACUGGUUCGAUUCCAGUACGGGGC- +>URS000268BCD7 62 0.738 1.000E+00 3 67 72 0 64 66 +---CGCGUGGCUCAGUGGUGGAGCACUGGCUUCCCAAGCCGGGGGUCGCGGGUUCGAAUCCCGUCGCG---- +>URS000340D302 63 0.772 1.000E+00 2 67 72 0 65 66 +--UGCUGUGGCUCAGUGGUAGAGCGCGUCCUUGGUAAGGACGAGGUCGCGGGUUCAAGUCCCGCCAGC---- +>URS0003390091 43 0.605 1.000E+00 0 70 72 0 64 66 +GAAUCUGUGGCGUAAUGGUAGCGCGUCUGACUCCGAGUCAGAAGGUUGCG------AGUUCCGUUAGAUUC- +>URS0002689143 52 0.696 1.000E+00 2 67 72 0 65 66 +--UGGUGUGGCUCAAUGGCAGAGCAGCUCAUUCGUAAUGAGCAGGUUGCAGGUUCGAUUCCCGUCACC---- +>URS0002682BCE 59 0.646 1.000E+00 3 67 72 0 64 66 +---AGUGUGGCGCAAUGGUAGCGCAUCUGAUUUGUAAUCAGGCGGUUGCAGGUUCGAGUCCUGUCACU---- +>URS000268C694 64 0.727 1.000E+00 2 67 72 0 65 66 +--UGGUGUGGCUCAAUGGCAGAGCAUCUCACUCGUAAUGAGAAGGUUGUGGGUUCGAUUCCCAUCACC---- +>URS0002F0EA70 54 0.712 1.000E+00 3 67 72 0 65 66 +---AUAAUAGCUCAGuUGGUAGAGCGUUGGCUUCCCAAGCCGAAGGUCGCGGGUUCGAGCCCCGUUUAU---- +>URS000269C29A 53 0.738 1.000E+00 3 67 72 0 64 66 +---CGGUUAGCUCAGUGGUAGAGCGUCUCGUUUACACCGAGAGGGUCGGGGGUUCGAGUCCCUCACCG---- +>URS000269D5EF 57 0.666 1.000E+00 2 67 72 0 65 66 +--UGGUGUAGCUCAAUGGUAGAGCAACUGAUUUGUAAUCAGUAGGUUGCGGGUUCGAGUCCCAUCACC---- +>URS0002685C94 66 0.753 1.000E+00 3 67 72 1 65 66 +---GGAGUAGCUCAGUGGUAGAGCGUCAGCCUUCCAAGCUGAGGGUCGCGGGUUCGACCCCCGUCUCC---- +>URS000341012E 64 0.753 1.000E+00 3 67 72 1 65 66 +---GGAGUAGCUCAGUGGUAGAGCAUCUGCUUCCCAAGCAGAGGGUCGCGAGUUCGAUACUCGUCUCC---- +>URS000268D213 53 0.727 1.000E+00 3 67 72 0 65 66 +---ACUAUGGCUCAgGCGGUAGAGCGUCGCCUUGGUAAGGCGGAGGUCACGGGUUCGAGUCCCGUUAGU---- +>URS0002694482 58 0.787 1.000E+00 3 67 72 0 65 66 +---GCCUUAGCUCAGUcGGUAGAGCGUCUCACUCGUAAUGAGAAGGUCGCGAGUUCGAUUCUCGCAGGC---- +>URS000340EA2D 62 0.738 1.000E+00 3 67 72 1 65 66 +---GGCGUAGUUUAGUGGUAGAAUGUCUGCUUCCCAAGCAGAAGGUCGCCGGUUCGAUUCCGGUCGCC---- +>URS000269540B 67 0.712 1.000E+00 2 67 72 0 65 66 +--UGCUGUAGCUCAGUGGUAGAGCACUUCCUUGGUAAGGAAGAGGUCACGGGUUCAAGUCCCGUCAGC---- +>URS00026921DB 62 0.712 1.000E+00 2 67 72 0 65 66 +--UGGUGUGGCUCAAUGGCAGAGCAUCUCACUCGUAAUGAGAAGGUUGUGAGUUCGAUUCUCACCACC---- +>URS000268C0A8 66 0.712 1.000E+00 2 67 72 0 65 66 +--UGGUGUAGCUCAGUGGUAGAGCACCUCCUUGGUAAGGAGGAGGUCGAGGGUUCGAGCCCCUCCACC---- +>URS000341FDE8 57 0.757 1.000E+00 3 67 72 0 65 66 +---AGAGUAGCUCAGcUGGUAGAGCGUCAGCCUUCCAAGCUGAGGGUCGCGGGUUCGAAUCCCGUCUCU---- +>URS0003416D20 67 0.742 1.000E+00 2 67 72 0 65 66 +--CGGCGUAGCUCAGUGGUAGAGCAACCGCCUUGUAAGUGGAAGGUCGGGGGUUCGAAUCCCCCCGCC---- +>URS000269A158 61 0.723 1.000E+00 3 67 72 1 65 66 +---GUAGUAACUCAGUGGUAGAGCGUUGGCUUCCCAAGCCAAAAGUCGCGGGUUCGAAUCCCGUCUAC---- +>URS0003417999 58 0.738 1.000E+00 3 67 72 0 64 66 +---UCUGUAGCUCAGCGGAAGAGCGCUGCCUUGCCAUGGCGGAGGUCGCGGGUUCGAAUCCCGCCAGG---- +>URS000269D9DE 52 0.723 1.000E+00 3 67 72 0 64 66 +---AAAGUCGCUCAAUGGUAGAGCGUCGGCUUCCCAAGCCGAUUCCCGCGGGUUCGAUUCCCGUCUUU---- +>URS00034036E2 65 0.727 1.000E+00 2 67 72 0 65 66 +--CGGCGUAGCUCAGCGGUAGAGCAGCCGCCUUGUAAGUGGAAGGUCGGGGGUUCGAAUCCCCCCGCC---- +>URS000269947A 66 0.723 1.000E+00 3 67 72 0 64 66 +---CAUGUAGCUCAGUGGUAGAGCACUUCCUUGGUAAGGAAGAGGUCACGGGUUCAAGUCCCGUCGUG---- +>URS0003418EF4 63 0.753 1.000E+00 3 67 72 1 65 66 +---GGGGUAGCUCAGUGGUAGAGCUCCUGCCUUCCAAGUAGGUGGUCGCGAGUUCGAAUCUCGUCCCC---- +>URS0003339979 46 0.707 1.000E+00 6 70 72 0 64 66 +------GUGGCGCAACGGUAGGGCGUCUGACUCCAGAUCAGAAGGUUGCGUAUUCAAAUCAUGUCGGGGUC- +>URS000341C8C5 63 0.753 1.000E+00 3 67 72 0 64 66 +---GCCGUGGCUCAGAGGUAGAGCGCCUGCUUGGUAAGCAGGAGAUCGGGGGUUCAAGUCCCCCCGGC---- +>URS000268A5A8 57 0.707 1.000E+00 3 67 72 1 65 66 +---GGAGUAGCUCAGCGGUAGAGCAUCACCUUGCCAAGGUGAGGGUCGCGGGUUCAAUCCCCGUCUCC---- +>URS0003420BD4 68 0.742 1.000E+00 2 67 72 0 65 66 +--UGCUGUAGCUCAGUGGUAGAGCACCUCCUUGGUAAGGAGGAGGUCACGGGUUCAAGUCCCGUCAGC---- +>URS0003414B70 58 0.707 1.000E+00 3 67 72 0 64 66 +---CCUUUAGCUCAGCGGUAGAGCAACGGACUUUUAAUCCGCAGGUCGUGGGUUCGAUCCCCACAGGG---- +>URS0001DFDA6E 56 0.707 1.000E+00 6 70 72 0 64 66 +------GUGGCGCAACGGUAGCGCAUCUGACUCCAGAUCAGAAGGUUGUCCGUUCGAAUCGGGCCGGGGUC- +>URS00026826AD 47 0.681 1.000E+00 3 67 72 0 65 66 +---ACAGUAGCUUAGuUGGUAAAGCGCCACCUUGCCAAGGUGGAGACCGCGGGUUCGAGCCCCGUCUGU---- +>URS0002694B0F 53 0.727 1.000E+00 3 67 72 0 65 66 +---AUAAUAGCUCAGuUGGUAGAGCGUCGGCUUCCCAAGCCGAAAGUCGCGGGUUCGAGUCCCGUUUAU---- +>URS000341402F 66 0.681 1.000E+00 2 67 72 0 65 66 +--UGCUGUAGCUCAGUGGUAGAGCACUUCCUUGGUAAGGAAGAGGUCAUGGGUUCAAAUCCCAUCAGC---- +>URS00033C9F1E 46 0.696 1.000E+00 3 67 72 0 65 66 +---UCCGUGGCCCAAUGGaUAAGGCACCGGCCUCCUAAGCCGGGCAUUGCGGGUUCGAGUCCCGCCGGA---- +>URS000336F2CF 60 0.787 1.000E+00 0 65 72 0 65 66 +GCCUCAUUGGCUCAGGGGCAGAGCGUCUGUCUAGUAAACAGAAGGUCGCUGGUUCGAUUCCAGCAU------ +>URS00034157A6 58 0.772 1.000E+00 3 67 72 0 65 66 +---AGAGUAGCUCAGcUGGUAGAGCGUCAGCCUUCCAAGCUGAGGGUCGCGGGUUCGAUUCCCGUCUCU---- +>URS0002695D38 53 0.772 1.000E+00 3 67 72 0 65 66 +---GCCAUGGCUCAGUcGGUAGAGCGUCACCUUGGUAAGGUGAAGGUCACGGGUUCGAUUCCCGUUGGU---- +>URS0002692C77 46 0.666 1.000E+00 3 67 72 0 65 66 +---AUAGUAGCUUAGcUGGUAAAGCGCCACCUUGCCAAGGUGGAGACCGCGGGUUCGAAUCCCGUCUAU---- +>URS0003406B3E 57 0.723 1.000E+00 3 67 72 1 65 66 +---GGCGUAGCUCAGUGGUAGAGCGUCUCGUUGCCAACGAGAAGGUCGAGGGUUCGACCCCCUUCGCC---- +>URS0003419DF8 60 0.712 1.000E+00 2 67 72 0 65 66 +--UGCUGUAGCUCAGUGGUAGAGCACGUCCUUGGUAAGGACGAGGUCACGGGUUCAAGUCCCGUCAGC---- +>URS000349D51E 50 0.633 1.000E+00 0 70 72 0 64 66 +GCGGGCAUGGUUCA------GAAUGUCUCCUUGCCAAGGAGAAGGUCACGGGUUCGAAUCCCGUUGCCCGC- +>URS00026A1544 62 0.692 1.000E+00 3 67 72 0 64 66 +---AGUGUGGCGCAAUGGUAGCGCAUCUGCCUUGUAAACAGACGGUUGCAGGUUCGAGUCCUGUCACU---- +>URS0002681570 45 0.681 1.000E+00 3 67 72 0 65 66 +---AUAGUAGCUUAGcUGGUAAAGCGCCACCUUGCCAAGGUGGAGACCGCGGGUUCGAAUCCCGUCUGU---- +>URS000269E450 64 0.716 1.000E+00 2 68 72 0 66 67 +--CGGUGUAGCUCAAUGGCAGAGCAUCCGCCUUGUAAGCGGAAGGUUGCGGGUUCGACUCCCAUCACCG--- +>URS000340D473 59 0.686 1.000E+00 2 68 72 0 66 67 +--CGGCGUAGCUCAAUGGUAGAGCACCUGUUUUGUAAACAGGCGGUUGCGGGUUCGAUUCCCAUCGCCG--- +>URS000269AADD 57 0.707 1.000E+00 3 67 72 1 65 67 +---GGCAUAGCUCAGUGGUAGAGCGUCUCGUUGCCAACGAGAAGGUCGUGGGUUCGAGUCCCAUUGCC---- +>URS0003415ECA 67 0.753 1.000E+00 3 67 72 1 65 67 +---GGCGUAGUUCAGUGGUAGAACGUCUCCUUGCCAAGGAGAAGGUCACGGGUUCGAAUCCCGUCGCC---- +>URS0002699A2E 58 0.738 1.000E+00 3 67 72 1 65 67 +---AAAGUGGCUCAGUGGUAGAGCGUCGCCUUGCCAAGGCGAAUGUCGCGAGUUCGAAUCUCGUCUUU---- +>URS0003409161 61 0.723 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAAUGGCAGAGCAGCUGCCUUCCAAGCAGCAGGUUGCGGGUUCGAGUCCCGUCUCC---- +>URS00026867F8 57 0.738 1.000E+00 3 67 72 1 65 67 +---AAAGUGGCUCAAUGGUAGAGCGUCACCUUCCCAAGGUGGAUGUUGCGGGUUCGAGUCCCGUCUUU---- +>URS000268F937 56 0.707 1.000E+00 3 67 72 1 65 67 +---GGAAUGGCUCAGUGGUAGAGCGUCGCCUUGCCAAGGCGAAUGUCGCGAGUUCGAAUCUCGUUUCC---- +>URS0003408201 54 0.676 1.000E+00 3 67 72 1 65 67 +---GGCAUAGCUCAGUGGUAGAGCGUCUCGUUGCCAACGAGAAGGCCGUGGGUUCAAGUCCCAUUGCC---- +>URS000269DEB9 66 0.723 1.000E+00 3 67 72 1 65 67 +---GGAGUGGCUCAGUGGUAGAGCAUCGGCUUCCCAAGCCGGCGGUCGCGAGUUCGAAUCUCGUCUCC---- +>URS000279F190 47 0.605 1.000E+00 70 0 72 1 66 67 +UCCAGGGUAGCCUAG-----UGGCAUCUGCCCGGUAAGCGGAACAUUGUGGGUUCGAUUCCCAUCCCUGGA- +>URS0000DA86BE 51 0.791 1.000E+00 0 70 72 0 65 67 +GCC------GCUCAGuUGGUAGUGCGUCUGCCUUGUAAGCGCUAGGCCGCGGGUUCGAUUCCCGCCCGGGGC- +>URS000341ACEA 62 0.692 1.000E+00 3 67 72 1 65 67 +---GGCGUAGCUCAGUGGUAGAGCAUCGGGUUGCCAAUCCGAAGGUCGUGGGUUCGAAUCCCAUCGCC---- +>URS000268FE79 62 0.738 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGGGGUAGAGCGUCAGCCUUCCAAGCUGAGGGUCGCGGGUUCGAAUCCCGUUUCC---- +>URS00034079B3 58 0.723 1.000E+00 3 67 72 0 64 67 +---CCUUUAGCUCAGGGGUAGAGCAACGGACUUUUAAUCCGCAGGUCGUGGGUUCGAUUCCCACAGGG---- +>URS000268381B 66 0.738 1.000E+00 3 67 72 1 65 67 +---GGAAUAGCUCAGUGGUAGAGCGUCAGCCUUCCAAGCUGAGGGUCGCGGGUUCGAAUCCCGUUUCC---- +>URS000268B8B1 53 0.731 1.000E+00 2 67 72 0 66 67 +--UGCUGUAGCUCAGuUGGUAGAGCGCGUCCUUGGUAAGGACGAGGUCACGGGUUCGAGUCCCGUCAGC---- +>URS000341B60E 57 0.723 1.000E+00 3 67 72 1 65 67 +---AAAGUAGCUCAGGGGUAGAGCAUCACCUUGCCAAGGUGAGGGUCGCGGGUUCAAUUCCCGUCUUU---- +>URS000269D58C 55 0.738 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGUGGUAGAGCUUCUCGUUGCCAACGAGAAGGUCGCGGGUUCAAGUCCCGUCUCC---- +>URS0003410AC3 65 0.707 1.000E+00 3 67 72 1 65 67 +---GGCGUAGCUCAAUGGUAGAGCAUCAGCCUUCCAAGCUGAGGGUUGCGGGUUCGAGCCCCGUCGCC---- +>URS0003405F18 62 0.753 1.000E+00 3 67 72 0 64 67 +---CUGGUAGCUCAGAGGUAGAGCAGCGGUCUUUUAAACCGAUGGUCGUGGGUUCGAUCCCCACCCGG---- +>URS0003418A7F 65 0.712 1.000E+00 2 67 72 0 65 67 +--UGCUAUAGCUCAGCGGUAGAGCACCUCCUUGGUAAGGAGGAGGUCGUGGGUUCGAGUCCCACUAGC---- +>URS0002655408 49 0.681 1.000E+00 6 70 72 0 65 67 +------GAGGCCCAAUGGaUAAGGCAUCCGCCUACGAAGCGGGAGAUUGCGGGUUCGAGUCCCGUCGAAGAC- +>URS000269B2C9 59 0.692 1.000E+00 3 67 72 1 65 67 +---GGAAUAGCUCAGUGGUAGAGCAUCGCCUUGCCAAGGCGAGGGUCGCGGGUUCAAAUCCCGUUUCC---- +>URS000341CC48 62 0.753 1.000E+00 3 67 72 1 65 67 +---GGAGUAACUCAGCGGUAGAGUGUCAGCUUCCCAAGCUGAAGGUCGCGGGUUCGAUUCCCGUCUCC---- +>URS000269FE52 59 0.630 1.000E+00 3 67 72 2 66 67 +---GGCGUAGCUCAAUGGUAGAGCACCAGACUUUUAAUCUGUAGGUUCUGGGUUCGAGUCCCAGCGCC---- +>URS00034BAAA4 59 0.701 1.000E+00 2 68 72 0 66 67 +--CUCGGUAGCUCAAUGGUAGAGCAUGCGGCUGUUAACCGCAGGGUUGUAGGUUCGAAUCCUACCCGGG--- +>URS0003201A07 57 0.731 1.000E+00 2 68 72 0 66 67 +--CACGGUGGCCAAGUGGUAAGGCGUCGGUUUCGUAAACCGAAGAUCGCGGGUCCGAACCCCGUCCGUG--- +>URS0003420849 63 0.738 1.000E+00 3 67 72 1 65 67 +---GGAGUAACUCAGUGGUAGAGUAUCUGCUUCCCAAGCAGAAUGUCGCGGGUUCGAACCCCGUCUCC---- +>URS000341905B 59 0.692 1.000E+00 3 67 72 1 65 67 +---GGAAUAGCUCAGCGGUAGAGCAUCGGCUUCCCAAGCCGAGGGUCGCGGGUUCGACCCCCGUUUCC---- +>URS000268EF5F 49 0.666 1.000E+00 3 67 72 1 66 67 +---GGCUUGGCGCAGuUGGUAGCGCGCCACCUUGCCAAGGUGGAGGCCGCGGGUUCGAAUCCCGUAGCC---- +>URS00034114EA 62 0.707 1.000E+00 3 67 72 1 65 67 +---GGCGUAGCUCAGGGGUAGAGCACCAGCUUCCCAAGCUGGGGGUCGCGGGUUCGAAUCCCGUCGCC---- +>URS0001FCFBFF 60 0.716 1.000E+00 2 68 72 0 66 67 +--CCCUGUGGCGCAACGGUAGCGCAUCUGACUCCAGAUCAGAAGGUUGUCCGUUCGAAUCGGGCCGGGG--- +>URS000269EBCD 56 0.723 1.000E+00 3 67 72 1 65 67 +---GGAGUAGUUCAGUGGUAGAGCGUCAGCUUCCCAAGCUGAAUGUCGCGGGUUCGAACCCCGUUUCC---- +>URS00026847C8 62 0.738 1.000E+00 3 67 72 1 65 67 +---AAAGUGGCUCAGUGGUAGAGCAUCGCCUUGCCAAGGCGAGGGUCGCGGGUUCAAAUCCCGUCUUU---- +>URS00026994AB 54 0.661 1.000E+00 3 67 72 1 65 67 +---GGAAUAACUCAACGGUAGAGUGUCAGCUUCCCAAGCUGAAGGUUGCGGGUUCAAGUCCCGUUUCC---- +>URS0002686E41 56 0.707 1.000E+00 3 67 72 1 65 67 +---AAAGUAGCUCAGGGGUAGAGCAUCACCUUGCCAAGGUGAGGGUCGCGGGUUCAAAUCCCGUCUUU---- +>URS0002F0EDE4 57 0.661 1.000E+00 3 67 72 1 65 67 +---AAAGUGGCCUAAUGGUAUGGCAUCAGCUUCCCAAGCUGAGGGUUGCGGGUUCGAGCCCCGUCUUU---- +>URS0002698208 65 0.738 1.000E+00 3 67 72 1 65 67 +---GGCGUAGUUCAGUGGUAGAACGUCUCCUUGCCAAGGAGAAGGUCGUGGGUUCGAAUCCCAUCGCC---- +>URS0002683640 61 0.738 1.000E+00 3 67 72 1 65 67 +---AAAGUAGCUCAGUGGUAGAGCGUCACCUUGCCAAGGUGAGGGUCGCGGGUUCAAAUCCCGUCUUU---- +>URS000340DA41 57 0.738 1.000E+00 3 67 72 1 65 67 +---GGGGUGGUUCAGUGGUAGAACGCCACCUUGCCAAGGUGGAUGUCGCGGGUUCGAGUCCCGUCCCC---- +>URS0003418598 66 0.723 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCACAGUGGUAGUGCGUCUGCUUGCCAUGCAGAAGGUCGUGGGUUCGAUCCCCAUCUCC---- +>URS0003406CC3 64 0.723 1.000E+00 3 67 72 1 65 67 +---GGCAUAGCUCAGUGGUAGAGCAUCAGCUUCCCAAGCUGAGGGUCGCGGGUUCGAGUCCCGUUGCC---- +>URS000268C358 55 0.738 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGGGGUAGAGCAUCACCCUUCCAAGGUGAACGUCGCGGGUUCGAAUCCCGUCUCC---- +>URS000268BEAB 55 0.731 1.000E+00 2 67 72 0 66 67 +--UGGUGUAGCUCAGuUGGUAGAGCAUCUCACUCGUAAUGAGAAGGUCGCGUGUUCGAGUCACGUCACC---- +>URS00026893CE 55 0.742 1.000E+00 3 67 72 1 66 67 +---GUAGUAGCUCAGuUGGUAGAGCAUCAGCUUCCCAAGCUGAGGGUCGCGGGUUCGAGUCCCGUCUAC---- +>URS000268FD84 63 0.738 1.000E+00 3 67 72 1 65 67 +---GGCGUAACUCAGUGGUAGAGUGUUAGCUUCCCAAGCUGAAGGUCGCGGGUUCGAAUCCCGUCGCC---- +>URS0003420B70 58 0.723 1.000E+00 3 67 72 1 65 67 +---AAAGUAGCUCAGCGGUAGAGCGUCACCUUGCCAAGGUGAGGGUCGCGGGUUCAAAUCCCGUCUUU---- +>URS0002681958 64 0.723 1.000E+00 3 67 72 0 64 67 +---CCAGUAGCUCAAUGGUAGAGCAGCAGCCUUUUAAGCCGUUGGUUCCGGGUUCAAGUCCCGGCUGG---- +>URS00026995C2 57 0.692 1.000E+00 3 67 72 1 65 67 +---GGAGUGGCUCAGUGGUAGAGCGCUGCCUUGCCAAGGCAGAUAUCGCGGGUUCGAAUCCCGUCUCC---- +>URS0003221805 52 0.723 1.000E+00 2 66 72 2 66 67 +--CACGGUGGCCAAGUGGUAAGGCAUCGGUCUCGUAAACUGAAGAUCACGGGUUCGAACCCCUGCCG----- +>URS00022724E5 62 0.716 1.000E+00 2 68 72 0 66 67 +--CGACGUAGCUCAGGGGUAGAGCGUUUCCUUGGUAAGGAAGAGGUCGUGGGUUCAAUUCCCAUCGUUG--- +>URS000341A95F 55 0.676 1.000E+00 3 67 72 1 65 67 +---UUGGUAGCUCAAUGGUAGAGCAGUGCCCUUUUAAGGCAUUGGUUGUGGGUUCGAGUCCCACCCAA---- +>URS000340A88B 64 0.731 1.000E+00 2 68 72 0 66 67 +--CGACGUAGCUCAGGGGUAGAGCGUUUCCUUGGUAAGGAAGAGGUCACGGGUUCAAUUCCCGUCGUUG--- +>URS000269214B 57 0.738 1.000E+00 3 67 72 1 65 67 +---GGAGUGACUCAGUGGUAGAGUGUCACCUUGCCAAGGUGAAAGUCGCGGGUUCGAGUCCCGUCUCC---- +>URS000269F395 64 0.723 1.000E+00 3 67 72 1 65 67 +---GGAGUGGCUCAGUGGUAGAGCAUCGGCUUCCCAAGCCGACGGUCGCGAGUUCGAAUCUCGUUUCC---- +>URS000269F376 60 0.746 1.000E+00 2 68 72 0 66 67 +--CGGUGUAGCUCAGUGGUAGAGCAGCUGUUUCGUAAACAGCAGGUCGCGGGUUCGAUUCCCAUCACCG--- +>URS0002688619 57 0.692 1.000E+00 3 67 72 1 65 67 +---AAAGUAGCUCAGCGGUAGAGCAUCACCUUGCCAAGGUGGGGGUCGCGGGUUCAAAUCCCGUCUUU---- +>URS0003411EAB 61 0.738 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGAGGCAGAGCAUUUGCUUCCCAAGCAAAAGGCCGCGGGUUCGAUUCCCGUCUCC---- +>URS000340C49D 63 0.731 1.000E+00 2 68 72 0 66 67 +--CGGGGUAGCUCAGUGGCAGAGCAACCGCCUUGUAAGCGGUAGGUCGUGGGUUCAAUCCCCACCCUCG--- +>URS0003420CDC 60 0.707 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGGGGUAGAGCGCCUCCUUGCCAAGGAGGAGGUCGGGGGUUCAAAUCCCCUCUCC---- +>URS0002696B6E 59 0.676 1.000E+00 3 67 72 1 65 67 +---GGAAUAGCUCAGCGGUAGAGCACCUCCUUGCCAAGGAGGGGGUCGCGGGUUCAAAUCCCGUUUCC---- +>URS000340E246 54 0.727 1.000E+00 3 67 72 1 66 67 +---GGCGUGGUUCAGuUGGUAGAACGUCUCCUUGCCAAGGAGAAGGUCAGGGGUUCGAGUCCCCUCGCC---- +>URS000341A4A2 63 0.738 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGGGGUAGAGCAUCUGCUUCCCAAGCAGAGGGUCGAGGGUUCGAAUCCCUUCUCC---- +>URS00031ACB91 58 0.761 1.000E+00 2 68 72 0 66 67 +--CGCGGUGGCCAAGUGGUAAGGCGUCGGUCUCGUAAAGUGAAGAUCGCGGGUUCGAACCCCAUCCGUG--- +>URS0003403C57 61 0.707 1.000E+00 3 67 72 1 65 67 +---GGAAUAGCUCAGCGGUAGAGCGUCAGCCUUCCAAGCUGAGGGUCGCGGGUUCAAAUCCCGUUUCC---- +>URS0002F0E098 59 0.692 1.000E+00 3 67 72 1 65 67 +---AAAGUGGCCUAGUGGUAUGGCAUCAGCUUCCCAAGCUGAGGAUCGCGGGUUCGAAUCCCGUCUUU---- +>URS0003414D53 61 0.707 1.000E+00 3 67 72 1 65 67 +---GGCGUAGCUCAGCGGUAGAGCACCUGCCUUCCAAGCAGGCUGUCGUGGGUUCGAUUCCCAUCGCC---- +>URS0002F0DA23 61 0.707 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGCGGUAGAGCAUCGGCUUCCCAAGCCGAGGGUCGCGGGUUCGAGCCCCGUUUCC---- +>URS00034198E1 60 0.701 1.000E+00 2 68 72 0 66 67 +--CGGCGUAGCUCAGCGGUAGAGCGCGGGCUUCGUAAGCCUGAGGUCGUGGGUUCGAAUCCCACCGCCG--- +>URS00026977AA 64 0.738 1.000E+00 3 67 72 1 65 67 +---GGCGUAGCUCAGUGGUAGAGCGCCUGCCUUCCAAGCAGGCUGUCGUGGGUUCGAUUCCCAUCGCC---- +>URS00026A06AF 57 0.723 1.000E+00 3 67 72 1 65 67 +---GGAGUAGUUCAGCGGUAGAGCGUCGGCUUCCCAAGCCGAAAGUCGCGGGUUCGAAUCCCGUUUCC---- +>URS0002691D06 52 0.707 1.000E+00 3 67 72 1 65 67 +---GGAAUAGCUCAGUGGUAGAGCGUCACCUUGCCAAGGUGAAUGUCGCGGGUUCGAGUCUCGUUUCC---- +>URS0002696A71 52 0.731 1.000E+00 2 67 72 0 66 67 +--UGGGAUAGCUCAGuUGGUAGAGCAUCUCCAUGGUAAGGAGAAGGUCGUGGGUUCGACUCCCAUUCCC---- +>URS000341DE11 61 0.707 1.000E+00 3 67 72 1 65 67 +---GGAGUGGCGCAACGGUAGCGCAUCUGCUUCCCAAGCAGAGGGCUGCGGGUUCGAAUCCCGUCUCC---- +>URS000340C09E 70 0.784 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGUGGUAGAGCAUCUGCUUCCCAAGCAGAGGGUCGCGGGUUCGAUCCCCGUCUCC---- +>URS0002683D24 60 0.692 1.000E+00 3 67 72 1 65 67 +---AACGUAGUUCAGUGGUAGAACAUCACCUUGCCAAGGUGGGGGUCGCGGGUUCGAAUCCCGUCGUU---- +>URS000341CAA9 61 0.738 1.000E+00 3 67 72 1 65 67 +---GGGGUAACUCAGCGGUAGAGUUCCUGCCUUCCAAGCAGGAUGUCGCGGGUUCGAAUCCCGUCCCC---- +>URS000341619B 70 0.784 1.000E+00 3 67 72 1 65 67 +---GGGGUAGCUCAGUGGUAGAGCUCCUGCCUUCCAAGCAGGAGGCCGCGGGUUCGAAUCCCGUCCCC---- +>URS0003419678 70 0.791 1.000E+00 1 67 72 0 66 67 +-CGGAAGUGGCUCAGUGGUAGAGCAUCUCCUUGCCAAGGAGAAGGUCGCGGGUUCGAAUCCCGUCUUC---- +>URS00026A0C12 54 0.661 1.000E+00 3 67 72 1 65 67 +---GGAAUAACUCAACGGUAGAGUGUCAGCUUCCCAAGCUGAAGGUUGCGGGUUCAAAUCCCGUUUCC---- +>URS000341E08B 65 0.723 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUUAGUGGUAAAGCAUCUGCCUUCCAAGCAGAGGAGCGCGGGUUCGAAUCCCGUCUCC---- +>URS0002688789 61 0.723 1.000E+00 3 67 72 1 65 67 +---AAAGUGGCUCAGUGGUAGAGCAUCGCCUUGCCAAGGCGAGGGUCGCGAGUUCGAAUCUCGUCUUU---- +>URS00026855D2 55 0.791 1.000E+00 2 67 72 0 66 67 +--UGGGGUAGCUCAGUcGGUAGAGCAUCUCCAUGGUAAGGAGAAGGUCGUGGGUUCGAUUCCCGCUCCC---- +>URS00034037EB 66 0.723 1.000E+00 3 67 72 1 65 67 +---GGCGUAACUCAGUGGAAGAGUAUCUGCCUUCCAAGCAGAAGGUCGUGGGUUCGAAUCCCAUCGCC---- +>URS0003406170 59 0.723 1.000E+00 3 67 72 1 65 67 +---GGCGUAGCUCAGUGGUAGAGCGUCAGCUUCCCAAGCUGAAUGUCGAGGGUUCGAGCCCCUUCGCC---- +>URS00004DF152 49 0.651 1.000E+00 5 70 72 0 65 67 +-----UGUAGUGUAGCGGUUACACGUCGCCCUGUCACGGCGAAGAUCGCGGGUUCGAUUCCCGUCAGAACC- +>URS0003412E30 64 0.738 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGCGGUAGAGCGUCGGCCUUCCAAGCCGAGGGUCGCGGGUUCGAAUCCCGUUUCC---- +>URS0002F0CA1C 57 0.723 1.000E+00 3 67 72 1 65 67 +---GGAGUAGUUCAGUGGUAGAGCGUCAGCUUCCCAAGCUGAAAGUCGCGGGUUCGAACCCCGUUUCC---- +>URS0003416AE3 64 0.701 1.000E+00 2 68 72 0 66 67 +--CGGCGUAGCUCAGUGGUAGAGCAGCUGAUUCGUAAUCAGCAGGUCAUGGGUUCGAAUCCCAUCGCCG--- +>URS00034064FC 64 0.738 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGUGGUAGAGCAUCAGCUUCCCAAGCUGAGGGUCGCGGGUUCGAUCCCCGUUUCC---- +>URS000305D5DC 60 0.731 1.000E+00 2 68 72 0 66 67 +--CCCUGUAGCUCAGUGGUAGAGCGGCCGACUGGUGUGCAGAAGGUCAUAGGUUUGAUUCCUGUUGGGG--- +>URS000340E355 61 0.738 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAAUGGUAGAGCGUCAGCCUUCCAAGCUGAAUGUUGCGGGUUCGAGUCCCGUCUCC---- +>URS000340BD35 65 0.723 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGUGGUAGAGCAUCUGCUUCCCAAGCAGAGGGCCGUGGGUUCGAACCCCAUCUCC---- +>URS0003402872 61 0.723 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGGGGUAGAGUAUUUGCUUCCCAAGCAAAGGGUCGCGGGUUCGAAUCCCGUCUCC---- +>URS000341D567 63 0.769 1.000E+00 3 67 72 1 65 67 +---GGCGUAGCUCAGAGGUAGAGCGUCUGCUUCCCAAGCAGAAUGUCGUGGGUUCGAUUCCCAUCGCC---- +>URS000268338F 56 0.707 1.000E+00 3 67 72 1 65 67 +---AAAGUGGCUCAAUGGUAGAGCGCCACCUUGCCAAGGUGGAUGUUGCGGGUUCGAGUCCCGUCUUU---- +>URS00031EBF50 55 0.742 1.000E+00 6 70 72 0 65 67 +------GUGGCGCAAUGGaUAACGCGUCUGCCUACGGAGCAGAAGAUUGCAGGUUCGAAUCCUGCCGUGGUC- +>URS0003404B2D 65 0.731 1.000E+00 2 68 72 0 66 67 +--CGGAGUAGCUCAGUGGUAGAGCAUCUGUUUCGUAAACAGAGGGUCACGGGUUCAAUCCCCGUCUCCG--- +>URS000268A4F2 53 0.723 1.000E+00 3 67 72 1 65 67 +---GGAAUGGCUCAGUGGUAGAGCGUCACCUUGCCAAGGUGAAUGUCGCGGGUUCGAAUCUCGUUUCC---- +>URS000341BF65 65 0.753 1.000E+00 3 67 72 1 65 67 +---GGAGUAACUCAGUGGUAGAGUGUCUGCUUCCCAAGCAGGAAGUCGCGGGUUCGAAUCCCGUCUCC---- +>URS0003412D69 58 0.738 1.000E+00 3 67 72 1 65 67 +---GGCGUAGCUCAGCGGUGGAGCGUCUCCUUGCCGAGGAGAAGGUCGCGGGUUCAAAUCCCGUCGCC---- +>URS00034182D5 61 0.746 1.000E+00 2 68 72 0 66 67 +--CGACGUAGCUCAGGGGUAGAGCGCUUCCUUGGUAAGGACGAGGUCGCGGGUUCAAUUCCCGCCGUUG--- +>URS000341981F 56 0.692 1.000E+00 3 67 72 1 65 67 +---GGCGUAGCUCAGUGGUAGAGCGUCUCGUUGCCAACGAGAAGGUCAUGGGUUCAAUCCCCAUUGCC---- +>URS0003192438 55 0.641 1.000E+00 2 68 72 0 66 67 +--CCCAGUGGCCUAAUGAUAAGGCACUGGCCUCCUAAGCCAGGGAUUGUGGGUUCAAGUCCCAUCUGGG--- +>URS000342128F 58 0.723 1.000E+00 3 67 72 1 65 67 +---GGCGUAGCUCAGUGGUAGAGCGUCUCGUUGCCAACGAGAAGGUCGUGGGUUCGAGUCCCAUUGCC---- +>URS00026A6BAF 44 0.591 1.000E+00 70 0 72 1 66 67 +UCCAGGGUAGCCUAG-----UGGCAUCUGCCCGGUAAGCGGAACACUGUGGGUUCGAUUCCCAUCCCUGGA- +>URS0002F0D544 59 0.723 1.000E+00 3 67 72 1 65 67 +---GGAGUAACUCAGUGGUAGAGUGUCAGCUUCCCAAGCUGAAGGUCGCGGGUUCAAGUCCCGUUUCC---- +>URS000341F955 54 0.692 1.000E+00 3 67 72 1 65 67 +---GGCAUAGCUCAGUGGUAGAGCGUCUCGUUGCCAACGAGAAGGUCGUGGGUUCGAAUCUCAUUGCC---- +>URS0003413E3C 61 0.723 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGGGGCAGAGCAUCUGCUUCCCAAGCAGAGGGUCGUGGGUUCGAAUCCCAUCUCC---- +>URS00026910EE 64 0.723 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGUGGUAGAGCAUCGGCUUCCCAAGCCGAGGGUCGCGGGUUCGAACCCCGUUUCC---- +>URS00026865B7 56 0.738 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGUGGCAGAGCAUCACCCUUCCAAGGUGAACGUCGCGGGUUCGAAUCCCGUCUCC---- +>URS00034213A3 61 0.738 1.000E+00 3 67 72 1 65 67 +---GGAGUAACUCAGUGGUAGAGUGUCAGCUUCCCAAGCUGAAGGUCGCGGGUUCGACCCCCGUCUCC---- +>URS000340986B 67 0.738 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGUGGUAGAGCAUCUGCUUCCCAAGCAGAGGGUCGAGGGUUCGAACCCCUUCUCC---- +>URS00034128DA 57 0.723 1.000E+00 3 67 72 1 65 67 +---GGCGUAGCUCAGUGGUAGAGCGUCUCGUUGCCAACGAGAAGGUCGAGGGUUCGAUCCCCUUUGCC---- +>URS000268E1EB 61 0.701 1.000E+00 2 68 72 0 66 67 +--CGACGUAGCUCAGGGGUAGAGCGUUUCCUUGGUAAGGAAGAGGCCACGGGUUCAAAUCCCGUCGUUG--- +>URS00026A0B02 56 0.696 1.000E+00 2 67 72 0 65 67 +--UGAUGUGGCUCAAUGGUAGAGCAGCUCAUUCGUAAUGAGCAGGUUGGGGGUUCGAUUCCCUUCAUC---- +>URS00030EF78D 51 0.641 1.000E+00 2 68 72 0 66 67 +--CCUCGUGGCCUAACGAUAAGGCAUCGGUCUUCUAAACGGGGGAUGGCGGGUUCGAGUCCCGCUGAGG--- +>URS0000DADB5C 52 0.746 1.000E+00 0 70 72 0 65 67 +GCCUAGUUAGCUU-----UAACGCGCCUGCCUUGUAAGCACUAGGUCGCGGGUUCGAUUCCCGCCCGGGGC- +>URS0003403A91 53 0.692 1.000E+00 3 67 72 1 65 67 +---GGCGUAGCUCAGCGGUAGAGCGUCUCGUUGCCAACGAGAAGGUCGUGGGUUCAAGUCCCAUUGCC---- +>URS000340A9DE 59 0.707 1.000E+00 3 67 72 1 65 67 +---UUGGUAGCUCAAUGGUAGAGCAGCGCCCUUUUAAGGCGUUGGUUGUGGGUUCGAGUCCCACCCAA---- +>URS00034187C4 65 0.727 1.000E+00 2 67 72 0 65 67 +--UGAUGUAGCUCAGUGGUAGAGCACCUCCUUGGUAAGGAGGAGGCCGCGGGUUCAAUCCCCGCCAUC---- +>URS00034034E4 64 0.753 1.000E+00 3 67 72 1 65 67 +---GGCGUAGCUCAGUGGUAGAGCGGCUGCUUGCCAUGCAGCAGGUCGCGGGUUCGAGUCCCGUCGCC---- +>URS000340A1FC 57 0.723 1.000E+00 3 67 72 1 65 67 +---GGCGUAGCUCAGUGGUAGAGCGUCUCGUUGCCAACGAGAAGGUCGUGGGUUCAAGUCCCAUCGCC---- +>URS0000E551CF 46 0.791 1.000E+00 0 64 72 0 66 67 +GCCCCCGUAGCUCAUCugGAUAGAGCGUCCCCCUCCUAAGGGGAAGGUAGCAGGUUCGAGUCCUGCC------- +>URS00031FA82A 58 0.704 1.000E+00 0 70 72 0 65 67 +GACUCUGUGGC-----GGUAGCGCGUCUGACUCCAGAUCAGAAGGUUGCGUGUUCGAGUCACGUCAGGGUC- +>URS0002F0D0FA 55 0.738 1.000E+00 3 67 72 1 65 67 +---GGAGUGGCUCAGUGGUAGAGCGUCACCUUGCCAAGGUGAACGUCGCGAGUUCGAAUCUCGUCUCC---- +>URS0003416216 68 0.769 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGUGGUAGAGCAUCUGCUUCCCAAGCAGAGGGUCGCGGGUUCGACCCCCGUCUCC---- +>URS000269484C 60 0.723 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGGGGUAGAGCAUCAGCUUCCCAAGCUGAGGGUCGCGGGUUCGAAUCCCGUUUCC---- +>URS000340CD73 61 0.769 1.000E+00 3 67 72 1 65 67 +---CGGGUAGCUCAGUGGUAGAGCGCCUCCCUUACAAGGAGGACGUCGCAGGUUCGACCCCUGUCCCG---- +>URS000341FDA2 67 0.723 1.000E+00 3 67 72 1 65 67 +---GGCGUAACUCAGUGGUAGAGUGUUUGCUUCCCAAGCAAAAGGUCGUGGGUUCGAAUCCCAUCGCC---- +>URS000268F8AE 68 0.753 1.000E+00 3 67 72 1 65 67 +---GGAAUAGCUCAGUGGUAGAGCGUCUGCUUGCCAAGCAGAAGGUCGCGGGUUCAAUCCCCGUUUCC---- +>URS000268BD86 59 0.676 1.000E+00 3 67 72 1 65 67 +---GGAAUAGCUCAGUGGUAGAGCGCUAGCCUUCCAAGCUGGAUGUCGCGGGUUCAAAUCCCGUUUCC---- +>URS0003412551 64 0.707 1.000E+00 3 67 72 1 65 67 +---GGAAUAGCUCAGUGGUAGAGCAUCGGCUUCCCAAGCCGAGGGUCGCGGGUUCGAACCCCGUUUCC---- +>URS0003404D33 61 0.723 1.000E+00 3 67 72 1 65 67 +---UGGGUAGCUCAGUGGUAGAGCGUCGCCCUUACAAGGCGGUGGUCGCAGGUUCGACCCCUGUCCCA---- +>URS00030D999D 51 0.641 1.000E+00 2 68 72 0 66 67 +--CCCAGUGGCCUAUGGAUAAGGCACUGGCCUCCUAAGCCAGGGAUUGUGGGUUCGAGUCCCAUCUGGG--- +>URS0003421255 58 0.757 1.000E+00 3 67 72 1 66 67 +---GGAGUAGCUCAGuUGGUAGAGCGUCUGCUUCCCAAGCAGAAGGUCGCGAGUUCGAGCCUCGUCUCC---- +>URS00026845C5 52 0.707 1.000E+00 3 67 72 1 65 67 +---GGAAUAGCUCAGUGGUAGAGCGUCACCUUGCCAAGGUGAAUGUCGCGGGUUCGAAUCUCGUUUCC---- +>URS00026977DE 63 0.753 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGUGGCAGAGCAUCAGCUUCCCAAGCUGAAGGUCGCGGGUUCGAUCCCCGUCUCC---- +>URS000341DE04 75 0.846 1.000E+00 3 67 72 1 65 67 +---GCGGUAGUUCAGUGGUAGAACGUCUGCCUUCCAAGCAGAAGGUCGCGGGUUCGAAUCCCGUCCGC---- +>URS000341DFFD 66 0.738 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGUGGUAGAGCAUCUGCCUUCCAAGCAGAUGGCCGUGGGUUCGAAUCCCAUCUCC---- +>URS000268D46A 66 0.753 1.000E+00 3 67 72 1 65 67 +---GCAGUAGCUCAGUGGUAGAGCAUCGGCUUCCCAAGCCGAGGGUCGCGGGUUCGAACCCCGUUUGC---- +>URS000341B158 62 0.738 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGUGGUAGAGCGCCACCCUGCCGAGGUGGAGGUCGCGGGUUCAAUUCCCGUCUCC---- +>URS000340FE95 66 0.800 1.000E+00 3 67 72 1 65 67 +---GCGGUAGUUCAGUGGUAGAACGUCUCCUUGCCAUGGAGAAGGCCGCGGGUUCGAAUCCCGUCCGC---- +>URS00034067E8 64 0.784 1.000E+00 3 67 72 1 65 67 +---GGGGUAACUCAGUGGUAGAGUCUCUGCCUUCCAAGCAGAUGGUCGCGGGUUCGAGUCCCGUCCCC---- +>URS0003403725 69 0.753 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGUGGUAGAGCAUCUGCCUUCCAAGCAGAUGGUCGUGGGUUCGAUCCCCAUCUCC---- +>URS0003409CAC 61 0.738 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGUGGCAGAGCAUCUGCUUCCCAAGCAGAUGGCCGUGGGUUCGAUUCCCAUCUCC---- +>URS0002690F66 58 0.753 1.000E+00 3 67 72 1 65 67 +---GGGGUAGCUCAGUGGUAGAGCGUCAGUUUUCCAAACUGAAUGUCGCGGGUUCGAUCCCCGUCCCC---- +>URS000340D5E8 64 0.746 1.000E+00 2 68 72 0 66 67 +--CGCUGUAGCUCAGUGGUAGAGCGCACCCUUGGUAAGGGUGAGGUCGCGGGUUCAAAUCCCGCCAGCG--- +>URS0003418804 60 0.707 1.000E+00 3 67 72 1 65 67 +---AAAGUAGCUCAGUGGUAGAGCAUCACCUUGCCAAGGUGGGGGUCGCGGGUUCAAAUCCCGUCUUU---- +>URS000340615E 65 0.723 1.000E+00 3 67 72 1 65 67 +---GGCGUAGUUCAGUGGCAGAACGUCUGCUUGCCAAGCAGAAGGUCGUGGGUUCGAAUCCCAUCGCC---- +>URS00026892D1 61 0.738 1.000E+00 3 67 72 1 65 67 +---GGAGUAACUCAGUGGUAGAGUGUCAGCUUCCCAAGCUGAAGGUCGCGGGUUCAAAUCCCGUCUCC---- +>URS000269B8FF 65 0.707 1.000E+00 2 66 72 0 64 67 +--CUAUGUAGCUCAGUGGUAGAGCACUUCCUUGGUAAGGAAGAGGUCCCGGGUUCAAAUCCCGGCAU----- +>URS00026925B3 57 0.656 1.000E+00 2 68 72 0 66 67 +--CGACUUAGCUCAGCGGUAGAGUGCUUCCUUGGUAAGGAAGAGGUCACGGGUUCAAGUCCCGUAGUUG--- +>URS00026A0A78 57 0.723 1.000E+00 3 67 72 1 65 67 +---GGAAUAGUUCAGUGGUAGAGCGUCAGCUUCCCAAGCUGAAUGUCGCGGGUUCGAAUCCCGUUUCC---- +>URS000268E4EE 68 0.753 1.000E+00 3 67 72 1 65 67 +---GGCAUAGCUCAGUGGUAGAGCGUCUGCUUGCCAUGCAGAAGGUCGCGGGUUCGAAUCCCGUUGCC---- +>URS0003415F64 62 0.738 1.000E+00 3 67 72 1 65 67 +---GGAGUGGCUCAGCGGUAGAGCGUCUCCUUGCCAAGGAGAAGGUCACGAGUUCAAAUCUCGUCUCC---- +>URS000268C069 61 0.707 1.000E+00 3 67 72 1 65 67 +---GGAAUAGCUCAGUGGUAGAGCAUCAGCUUCCCAAGCUGAGGGUCGCGGGUUCGACCCCCGUUUCC---- +>URS000269A32E 53 0.727 1.000E+00 3 67 72 0 65 67 +---AGAGUAGCUCAGuUGGUAGAGCAUCAGCUUCCCAAGCUGAGGGUCGCGGGUUCGAGUCCCGUUUCU---- +>URS000268C6D3 60 0.738 1.000E+00 3 67 72 1 65 67 +---GGCGUAGCUCAGGGGUAGAGCAGCAGCUUCCCAAGCUGAAGGUCGCGGGUUCGAAUCCCGUCGCC---- +>URS000268E631 68 0.815 1.000E+00 3 67 72 1 65 67 +---GGGGUAGCUCAGUGGUAGAGCGUCUGCUUCCCAAGCAGGAUGUCGCGGGUUCGAUCCCCGUCCUC---- +>URS0003405490 56 0.676 1.000E+00 3 67 72 1 65 67 +---GGCGUGGCGCAACGGUAGCGCAUCAGCUUCCCAAGCUGAGUGUUGCGGGUUCGAAUCCCGUCGCC---- +>URS0003419986 59 0.707 1.000E+00 3 67 72 1 65 67 +---GGCGUAGCUCAAUGGUAGAGCGUCAGCCUUCCAAGCUGAAUGUUGAGGGUUCGAGUCCCUUCGCC---- +>URS00026898C0 61 0.723 1.000E+00 3 67 72 1 65 67 +---GGCGUAACUCAGUGGUAGAGUGUCAGCUUCCCAAGCUGAAGGCCGCGGGUUCAAAUCCCGUCGCC---- +>URS000268B8CC 42 0.681 1.000E+00 3 67 72 0 65 67 +---AUAGUAGCUUAGcUGGUAAAGCGCCACCUUGCCAAGGUGGAGACCACGGGUUCGAAUCCCGCCUGU---- +>URS000340C897 56 0.707 1.000E+00 3 67 72 1 65 67 +---GGCGUAGCUCAGUGGUAGAGCGUCUCGUUGCCAACGAGAAGGUCGAGGGUUCGAGCCCCUUUGCC---- +>URS000269DB13 58 0.723 1.000E+00 3 67 72 1 65 67 +---GGCAUAGCUCAGUGGUAGAGCGACUCCUUGCCAAGGAGUAGGUCGCGGGUUCGAAUCUCGUUGCC---- +>URS000268CE2E 63 0.738 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGUGGUAGAGCAUCAGCUUCCCAAGUUGAGGGUCGCGGGUUCGAGCCCCGUCUCC---- +>URS000269E33C 55 0.676 1.000E+00 3 67 72 1 65 67 +---GGCGUAGCUCAGUGGUAGAGCACAACCUUGCCAAGGUUGGGGUCGCGGGUUCGAAUCCCGUCGCC---- +>URS0002696759 56 0.738 1.000E+00 3 67 72 1 65 67 +---GGAGUAGUUCAGUGGUAGAGCGUCAGCUUCCCAAGCUGAAUGUCGCGGGUUCGAGUCCCGUUUCC---- +>URS0002683C53 54 0.738 1.000E+00 3 67 72 1 65 67 +---GGAGUGGCUCAGUGGUAGAGCGUCACCUUCCCAAGGUGAACGUCGCGAGUUCGAAUCUCGUUUCC---- +>URS000310786D 57 0.671 1.000E+00 2 68 72 0 66 67 +--CCUCGUGGCCUAACGAUAAGGCAUCGGUCUUCUAAACCGGGGGUUGCGGGUUCGAGUCCCGCCGAGG--- +>URS00034134B8 67 0.769 1.000E+00 3 67 72 0 64 67 +---UCAGUAGCUCAGUGGUAGAGCAACAGCCUUUUAAGCUGAUGGUCGCGGGUUCAAUCCCCGCCUGA---- +>URS000341CA0E 59 0.646 1.000E+00 3 67 72 1 65 67 +---GGAAUAGCUCAGUGGUAGAGCACUGCCUUGCCAAGGCAGGGGUCGCGGGUUCGAAUCCCGUUUCC---- +>URS00026A0B7E 56 0.757 1.000E+00 3 67 72 0 65 67 +---ACAAUAGCUCAGuUGGUAGAGCGUUGGCUUCCCAAGCCGAAGGUCGCGGGUUCGAGUCCCGUUUGU---- +>URS000268D1E2 64 0.707 1.000E+00 3 67 72 1 65 67 +---GGCAUAGCUCAGUGGUAGAGCAUCAGCUUCCCAAGCUGGGGGUCGCGGGUUCGAAUCCCGUUGCC---- +>URS0003419B92 66 0.738 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGUGGUAGAGCGCCUGCCUUCCAAGCAGGAUGUCGAGGGUUCGAUCCCCUUCUCC---- +>URS0003405A44 60 0.692 1.000E+00 3 67 72 1 65 67 +---GGAAUAGCUCAGUGGUAGAGCGUUGCCUUGCCAAGGCAAAGGUCGCGGGUUCGACCCCCGUUUCC---- +>URS0003408354 60 0.707 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGUGGUAGAGCUCCUGCCUUCCAAGCAGGUUGUCGUGGGUUCGAAUCCCAUCUCC---- +>URS0002691BB7 62 0.723 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGUGGUAGAGCAUCAGCUUCCCAAGCUGAGGGUCGCGGGUUCGAGCCCCGUUUCC---- +>URS0002685913 61 0.742 1.000E+00 2 67 72 0 65 67 +--UAAUGUGGCUCAGGGGUAGAGCAGCUCACUCGUAAUGAGCAGGUCGUGGGUUCGAUUCCCACCAUU---- +>URS000341718F 58 0.738 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGUGGUAGAGCAUCUCGUUGCCAACGAGAGGGUCGCGGGUUCGAGUCCCGUCUCC---- +>URS00034055D9 65 0.753 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGUGGUAGAGCAUCAGCUUCCCAAGCUGAGGGUCGCGGGUUCGAGUCCCGUCUCC---- +>URS000268C38B 59 0.692 1.000E+00 3 67 72 1 65 67 +---GGCAUAGUUCAGUGGUAGAAUGUCUGCUUCCCAAGCAGAAGAUCGCCGGUUCGAGUCCGGUUGCC---- +>URS00027366CB 44 0.591 1.000E+00 70 0 72 1 66 67 +UCCAGGGUAGCCUAG-----UGGCAUCUGCCCGGUAAGCGGAACACUGUGGGUUCGAUUCCCAUCCCUGGA- +>URS000269ED26 64 0.753 1.000E+00 3 67 72 1 65 67 +---GCAGUAGCUCAGGGGUAGAGCAUCUGCUUCCCAAGCAGAGGGCCGUGGGUUCGAAUCCCAUCUGC---- +>URS000341F4C2 63 0.707 1.000E+00 3 67 72 1 65 67 +---GGCAUGGUGCAGGGGUAGCACGUCUGCCUUCCAAGCAGAAGGUCGCGGGUUCAAAUCCCGUUGCC---- +>URS0003408DAA 64 0.800 1.000E+00 3 67 72 0 64 67 +---CCGGUAGCUCAGUGGAAGAGCACCUGACUUUUAAUCAGGGUGUCGCGGGUUCGAUACCCGCCCGG---- +>URS0000BAD6E9 33 0.611 1.000E+00 67 3 72 0 66 67 +---GGAGUAGAGUAGUGGUAGCUCGCAAGGCUCAUAACCUUGAGGUCGCGGGUUCAagAAUCCCGCCUCC---- +>URS0003402CE1 57 0.707 1.000E+00 3 67 72 1 65 67 +---AAAGUAGCUCAGGGGUAGAGCAUCACCUUGCCAAGGUGGAGGUCGCGGGUUCGAAUCCCGUUUUU---- +>URS00034041B3 60 0.731 1.000E+00 2 68 72 0 66 67 +--CGACGUAGCUCAGGGGUAGAGCGCUUCCUUGGUAAGGACGAGGUCGCGGGUUCAAAUCCCGCCGUUG--- +>URS0003402277 62 0.738 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGGGGUAGAGCAUCGGCCUCCCAUGCCGAGGGUCGCGGGUUCGAAUCCCGUCUCC---- +>URS0002696BB5 51 0.716 1.000E+00 2 67 72 0 66 67 +--UGGGAUAGCUCAGuUGGUAGAGCAUCUCCAUGGUAAGGAGAAGGUCGUGGGUUCAAGUCCCAUUCCC---- +>URS000340C3B7 62 0.738 1.000E+00 3 67 72 1 65 67 +---GGCGUAGCUCAGUGGUAGAGCUUCUGCUUCCCAAGCAGACGGCCGUGGGUUCGAUCCCCAUCGCC---- +>URS0002696BB6 60 0.692 1.000E+00 3 67 72 1 65 67 +---GGCAUAGUUCAGUGGUAGAAUGUCUGCUUCCCAAGCAGAAGAUCGCGAGUUCGAGUCUCGUUGCC---- +>URS00034BC069 61 0.776 1.000E+00 2 68 72 0 66 67 +--CCCGGUAGCUCAGUGGUAGAGCAACCGGCUGUUAACCGGUUGGUCGCUGGUUCGAAUCCGGCCCGGG--- +>URS00034183E1 55 0.692 1.000E+00 3 67 72 1 65 67 +---GGCAUAGCUCAGUGGUAGAGCGUCUCGUUGCCAACGAGAAGGUCGUGGGUUCAAGUCCCAUUGCC---- +>URS000269311B 57 0.707 1.000E+00 3 67 72 1 65 67 +---AAAGUAGCUCAGGGGUAGAGCAUCACCUUGCCAAGGUGGAGGUCGCGGGUUCAAAUCCCGUCUUU---- +>URS0003403ACC 61 0.692 1.000E+00 3 67 72 1 65 67 +---GGAAUAGCUCAGUGGUAGAGCACCUCCUUGCCAAGGAGGGGGUCGCGGGUUCAAAUCCCGUUUCC---- +>URS0003413217 63 0.738 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGGGGUAGAGCGUCUGCCUUCCAAGCAGAAUGCCGUGGGUUCGAAUCCCAUCUCC---- +>URS0002697FD9 59 0.723 1.000E+00 3 67 72 1 65 67 +---GGAGUAGUUCAGUGGUAGAGCAUCGGCUUCCCAAGCCGAGUGUCGCGGGUUCGAAUCCCGUCUCC---- +>URS0002F0CAB1 55 0.753 1.000E+00 3 67 72 1 65 67 +---GGAGUGGCUCAGUGGUAGAGCGUCACCUUGCCAAGGUGAAUGUCGCGGGUUCGAAUCUCGUCUCC---- +>URS0003413381 69 0.753 1.000E+00 3 67 72 1 65 67 +---GGCGUAGUUCAGUGGUAGAACGCCUGCUUCCCAAGCAGGAGGUCGCGGGUUCGAGUCCCGUCGCC---- +>URS000268F0F8 55 0.776 1.000E+00 2 67 72 0 66 67 +--UGGGGUAGCUCAGUcGGUAGAGCAUCUCCAUGGUAAGGAGAAGGUCGUGGGUUCGAUUCCCACUCCC---- +>URS0003418319 59 0.707 1.000E+00 3 67 72 1 65 67 +---GGAAUAGCUCAGCGGUAGAGCAUCUCCUUGCCAAGGAGAGGGUCGCGGGUUCAAGUCCCGUUUCC---- +>URS00034183F5 62 0.738 1.000E+00 3 67 72 1 65 67 +---GGCGUAGUUCAGUGGUAGAACGUCUCCUUGCCAAGGAGAAGGUCAUGGGUUCGAAUCCCACCGCC---- +>URS000340400C 57 0.723 1.000E+00 3 67 72 1 65 67 +---GGCGUAGCUCAGGGGUAGAGCGUCAGCUUCCCAAGCUGAAUGUCGUGGGUUCGAAUCCCAUCGCC---- +>URS000340FB3D 57 0.707 1.000E+00 3 67 72 1 65 67 +---GGCAUAGCUCAGUGGUAGAGCGUCUCGUUGCCAACGAGAAGGUCGAGGGUUCGAAUCCCUUUGCC---- +>URS000269FC31 48 0.651 1.000E+00 3 67 72 1 66 67 +---GGCUUAGCGCAGuUGGUAGCGCGCCACCUUGCCAAGGUGGAGGCCGCGGGUUCGAAUCCCGUAGCC---- +>URS00006FC429 51 0.712 1.000E+00 5 70 72 0 65 67 +-----CGUGGCGCAAUGGUACCGCGUCUGACUCCAGAUCAGAAGGUUGCGUUUUCAAAUCACGUCGGGGUC- +>URS000268B2AB 62 0.707 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGUGGUAGAGCAUCGGCUUCCCAAGCCGAGGGUCGCGGGUUCAAACCCCGUUUCC---- +>URS00030A2538 56 0.671 1.000E+00 2 68 72 0 66 67 +--CCCAGUGGCCUAAUGGUAAGGCACUGGCCUCCUAAGCCAGGGAUGGUGGGUUCGAGUCCCAUCUGGG--- +>URS0003408770 59 0.707 1.000E+00 3 67 72 1 65 67 +---GGAAUAACUCAGUGGUAGAGUGUCAGCUUCCCAAGCUGAAGGUCGCGGGUUCAAAUCCCGUUUCC---- +>URS000341CF2B 68 0.753 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUUAGUGGUAGAGCGUUUGCUUCCCAAGCAAAAAGUCGCGGGUUCGAAUCCCGUCUCC---- +>URS0002699987 53 0.686 1.000E+00 2 67 72 0 66 67 +--UGUUAUAGCUCAgGUGGUAGAGCGUCGCCUUGGUAAGGCGGAGGUCACGGGUUCGAGUCCCGUUAAC---- +>URS0003420A53 58 0.707 1.000E+00 3 67 72 1 65 67 +---GGCAUAGCUCAGAGGUAGAGCAUCAGCUUCCCAAGCUGAGGGUCGCGGGUUCGACUCCCGUUGCC---- +>URS000340E9FD 57 0.692 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGCGGUAGAGCAUCACCUUGCCAAGGUGGGGGUCGCGGGUUCAAAUCCCGUCUCC---- +>URS000269AE35 53 0.707 1.000E+00 3 67 72 1 65 67 +---GGAAUGGCUCAGUGGUAGAGCGUCACCUUGCCAAGGUGAACGUCGCGAGUUCGAAUCUCGUUUCC---- +>URS000269A858 57 0.676 1.000E+00 3 67 72 1 65 67 +---GGAAUAGCUCAGUGGUAGAGCAUCACCUUGCCAAGGUGGGGGUCGCGGGUUCAAGUCCCGUUUCC---- +>URS0003408A41 58 0.676 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGUGGUAGAGCACUACCUUGCCAAGGUAGAUGUCGCGGGUUCGAAUCCCGUCUCC---- +>URS00030FEA77 67 0.776 1.000E+00 2 68 72 0 66 67 +--CCCGGUGGCGCAACGGUAGCGCGUCUGACUCCAGAUCAGAAGGUUGUGUGUUCGAAUCACACCCGGG--- +>URS000269A0EC 54 0.723 1.000E+00 3 67 72 1 65 67 +---AAAGUGGCUCAAUGGUAGAGCGUCACCUUACCAAGGUGGAUGUUGCGGGUUCGAGUCCCGUCUUU---- +>URS000340C3C4 58 0.661 1.000E+00 3 67 72 1 65 67 +---GGUGUGGCGCAACGGUAGCGCAACGGCCUUCCAAGCCGUUGGUUGCGGGUUCGAAUCCCGUCACC---- +>URS00026F3DF5 39 0.591 1.000E+00 70 0 72 1 66 67 +UCCAGGGUAGCCUAG-----UGGACAUGGCCCGGUAAGCGGAAGGUUGUGGGUUCGAAUCCCGCCCCUGGA- +>URS00026850DF 61 0.723 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGUGGUAGAGCAUCAGCUUCCCAAGCUGAGGGUCGCGGGUUCGACCCCCGUUUCC---- +>URS000341A9AE 63 0.716 1.000E+00 2 68 72 0 66 67 +--CGACGUAGCUCAGGGGUAGAGCGCUUCCUUGGUAAGGAAGAGGUCACGGGUUCAAUUCCCGUCGUUG--- +>URS00026982B1 53 0.757 1.000E+00 3 67 72 0 65 67 +---GCAAUAGCUCAGUcGGUAGAGCAUUUCAUUCGUAACGAAAAGGUCGCGGGUUCGAGUCCCGCUCGC---- +>URS0003407606 61 0.738 1.000E+00 3 67 72 1 65 67 +---GGCGUAGCUCAGGGGUAGAGCGUCUGCCUUCCAAGCAGAAUGUCGCGAGUUCAAAUCUCGUCGCC---- +>URS00034131C8 65 0.753 1.000E+00 3 67 72 1 65 67 +---GGCGUAGCUCAGUGGUAGAGCAGCUGCCUUCCAAGCAGCAGGCCGCGGGUUCGAGUCCCGUCGCC---- +>URS0003413A69 58 0.692 1.000E+00 3 67 72 1 65 67 +---GGAAUAGCUCAGUGGUAGAGCAUCAGCUUCCCAAGCUGAGGGCCGCGGGUUCAAGUCCCGUUUCC---- +>URS000340A5F5 59 0.753 1.000E+00 3 67 72 1 65 67 +---GGAGUAGUUCAGUGGUAGAGCGUCAGCUUCCCAAGCUGAAUGUCGCGGGUUCGAAUCCCGUCUCC---- +>URS000341786F 62 0.692 1.000E+00 3 67 72 1 65 67 +---GGAAUAGCUCAGCGGUAGAGCAUCGGCCUUCCAAGCCGUUGGUCGCGGGUUCGAAUCCCGUUUCC---- +>URS000268C6FF 55 0.661 1.000E+00 3 67 72 1 65 67 +---GGCAUAACUCAACGGUAGAGUGUCAGCUUCCCAAGCUGAAGGUUGCGGGUUCAAAUCCCGUUGCC---- +>URS0002698198 56 0.753 1.000E+00 3 67 72 1 65 67 +---GGAGUGGCUCAGUGGUAGAGCGUCGCCUUGCCAAGGCGAAUGUCGCGGGUCCGAAUCCCGUCUCC---- +>URS0002F0B7D5 57 0.707 1.000E+00 3 67 72 1 65 67 +---GGAAUAGUUCAGCGGUAGAGCGUCGGCUUCCCAAGCCGAAAGUCGCGGGUUCGAAUCCCGUUUCC---- +>URS0000BF689D 56 0.731 1.000E+00 2 68 72 0 66 67 +--CACGGUGGCCAAGUGGUAAGGCGUCGGUCUCGUAAACCGAAGAUCGCGGGUCCGAACCCCAUCCGUG--- +>URS000341BA9D 65 0.769 1.000E+00 3 67 72 1 65 67 +---GCAGUAGCUCAGUGGCAGAGCAUCAGCUUCCCAAGCUGGAGGUCGCGGGUUCGAAUCCCGUCUGC---- +>URS00034028AD 67 0.753 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGUGGCAGAGCUCCUGCCUUCCAAGCAGGAGGUCGCGGGUUCGAUCCCCGUCUCC---- +>URS00026824CD 60 0.676 1.000E+00 3 67 72 1 65 67 +---AAAGUGGCUCAAUGGUAGAGCAUUGCCUUCCCAAGGCAACGGUUGCGAGUUCGAAUCUCGUCUUU---- +>URS00033BC74D 68 0.753 1.000E+00 3 67 72 1 65 67 +---CCUGUAGCUCAGUGGUAGAGCUCUUGCCUCCGAAGCAGAAAGUUCCAGGUUCAAUCCCUGGCAGG---- +>URS000279E004 55 0.661 1.000E+00 70 0 72 1 66 67 +UCCAGGGUAGCCUAG-----UGGCAUCUGCCCGGUAAGCGGAAGGUUGUGGGUUCGAUUCCCGCCCCUGGA- +>URS00031C2DAD 45 0.661 1.000E+00 3 67 72 1 65 67 +---UUUGUAGCGCAAUGGUAGCGCGUCUAACUCCACGUGAGAAGGCUGUGUGUUCGAUUCACUCUAAG---- +>URS0003412B10 66 0.753 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGUGGUAGAGCGCCUCCCUCCCAAGGAGGAGGUCGCGAGUUCAAGUCUCGUCUCC---- +>URS000341E30F 51 0.712 1.000E+00 3 67 72 1 66 67 +---GGAAUAGCUCAGuUGGUAGAGCGUCAGCCUUCCAAGCUGAAGGUCGCGGGUUCGAGCCUCGUUUCC---- +>URS0003416F7C 50 0.707 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGUGGUAGAGCAUCACGUUGCCAACGUGAAUGUCGCGGGUUCGAGUCCCGUCUCC---- +>URS0003416558 55 0.676 1.000E+00 3 67 72 1 65 67 +---GGAAUAGCUCAGUGGUAGAGCGCCACCUUGCCAAGGUGGAAGUCGCGGGUUCAAAUCCCGUUUCC---- +>URS00034177A2 65 0.761 1.000E+00 2 68 72 0 66 67 +--CGGUGUAGCUCAGUGGUAGAGCAGCUGCCUUGUAAGCAGCAGGUCGCGGGUUCGAAUCCCAUCGCCG--- +>URS00026A0A9C 58 0.738 1.000E+00 3 67 72 1 65 67 +---GGAGUAGUUCAGUGGUAGAGCGUCAGCUUCCCAAGCUGAAAGUCGCGGGUUCGAAUCCCGUUUCC---- +>URS0002698017 52 0.727 1.000E+00 3 67 72 1 66 67 +---GUAGUAGCUCAGuUGGUAGAGCAUCAGCUUCCCAAGCUGAGGGCCGCGGGUUCGAGUCCCGUCUAC---- +>URS0003407F96 64 0.784 1.000E+00 3 67 72 1 65 67 +---GGCGUAGCUCAGGGGUGGAGCGUCUGCUUCCCAAGCAGAAGGUCGCGGGUUCGAAUCCCGUCGCC---- +>URS00033BFAB8 66 0.757 1.000E+00 5 70 72 0 65 67 +-----CGUGGCGCAAUGGUAGCGCGUCUGACUCCAGAUCAGAAGGUUGCGUGUUCGAUUCACGUCGGGUUC- +>URS0003419119 57 0.723 1.000E+00 3 67 72 1 65 67 +---AAAGUAGCUCAGUGGUAGAGCAUCACCUUGCCAAGGUGAGGGUCGCGGGUCCGAAUCCCGUCUUU---- +>URS0000AE19F7 42 0.696 1.000E+00 6 70 72 0 65 67 +------GUGGUGUAGCGGuUAUCACGUCGCCCUGUCACGGCGAAGAUCGCGGGUUCGAUUCCCGUCGGGACC- +>URS00034057F0 63 0.731 1.000E+00 2 68 72 0 66 67 +--CGAAGUAGCUCAGGGGUAGAGCGUUUCCUUGGUAAGGAAGAGGUCACGGGUUCAAUUCCCGUCUUUG--- +>URS0002695E67 57 0.712 1.000E+00 3 67 72 0 65 67 +---AAUGUGGCUCAgGUGGUAGAGCACUUCCUUGGUAAGGAAGAGGUCACGGGUUCGAGUCCCGUCAUU---- +>URS0000C6283F 67 0.757 1.000E+00 5 70 72 0 65 67 +-----CGUAGCAUAGUGGUAGUGCGUUCGCUUCGUAAGCGAAAGGUCGCGAGUUCGAUCCUCGCCGGGGGC- +>URS0003405638 67 0.784 1.000E+00 3 67 72 1 65 67 +---GGGGUAACUCAGUGGUAGAGUAUCUGCUUCCCAAGCAGAAGGCCGCGGGUUCGAAUCCCGUCCCC---- +>URS0003404934 64 0.707 1.000E+00 3 67 72 1 65 67 +---GGAGUGGCGCAAUGGUAGCGCAUCUGCUUCCCAAGCAGAGGGUUGCGAGUUCGAAUCUCGUCUCC---- +>URS0002F0ABCE 58 0.738 1.000E+00 3 67 72 1 65 67 +---GGAGUGGCUCAGUGGUAGAGCGUCGCCUUGCCAAGGCGAAUGUCGCGAGUUCGAAUCUCGUCUCC---- +>URS0002683B89 65 0.738 1.000E+00 3 67 72 1 65 67 +---GGCGUAGCUCAGUGGUAGAGCGUUUGCUUCCCAAGCAAAAUGUCGUGGGUUCGACUCCCAUCGCC---- +>URS0002F0D0A0 64 0.712 1.000E+00 2 67 72 0 65 67 +--UGAAGUGGCUCAAUGGUAGAGCAUCUGAUUUGUAAUCAGACGGUUGUGGGUUCGAUUCCCACCUUC---- +>URS0002F0B602 57 0.676 1.000E+00 3 67 72 1 65 67 +---AAAGUGGCCUAGUGGUAUGGCAUCAGCUUCCCAAGCUGAGGAACGCGGGUUCGAAUCCCGUCUUU---- +>URS000341ABF8 63 0.707 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGUGGUAGAGCACCUCCUUGCCAAGGAGGGGGUCGAGGGUUCGAAUCCCUUCUCC---- +>URS00026987E1 65 0.696 1.000E+00 2 67 72 0 65 67 +--UGAUGUGGCUCAGUGGUAGAGCACUUCCUUGGUAAGGAAGGGGUCACGGGUUCAAUUCCCGUCAUC---- +>URS0002F0BB35 59 0.707 1.000E+00 3 67 72 1 65 67 +---GGAAUGGCUCAAUGGUAGAGCAGUAGCUUCCCAAGCUGAAGGUUGCGGGUUCGAUUCCCGUUUCC---- +>URS0003403554 62 0.707 1.000E+00 3 67 72 1 65 67 +---GGAAUAGCUCAGUGGCAGAGCAUCAGCCUUCCAAGCUGAGGGUCGCGGGUUCGAAUCCCGUUUCC---- +>URS000269C224 63 0.696 1.000E+00 2 67 72 0 65 67 +--UGAUGUGGCUCAGGGGUAGAGCGUUCCCUUGGUAAGGGAGAGGUCACGGGUUCAAUUCCCGUCAUC---- +>URS00026876AE 57 0.738 1.000E+00 3 67 72 1 65 67 +---GGAGUAGUUCAGUGGUAGAGCGUCAGCUUCCCAAGCUGAAUGUCGCGGGUUCGAAUCCCGUUUCC---- +>URS000341DC2B 66 0.723 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGUGGUAGAGCGCUUCCUUGCCAAGGAAGAGGUCGCGGGUUCGAACCCCGUCUCC---- +>URS00030D8677 57 0.716 1.000E+00 2 68 72 0 66 67 +--CACGGUGGCCAAGUGGUAAGGCGUCGGUUUCGCAAACCGAAGAUCGCGGGUCCGAACCCCGUCCGUG--- +>URS0000918531 51 0.661 1.000E+00 0 70 72 0 65 67 +GGAUCCGUGGCGCAAUGGUAGCGCGUCUGACUCCUGAUCAGAAGGUUGGGUGUUCGAUU-----CUAAUCC- +>URS00007E9277 49 0.651 1.000E+00 66 1 72 0 65 67 +-CCUCAGUAGCUCAGUGGUAGAGCGGUCGGCUGUUAACUGACUGGUCGUAGGUUCAAAUCCUACUUG----- +>URS0003404F33 58 0.707 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGGGGUAGAGCAUCAGCCUUCCAAGCUGAGGGCCGCGGGUUCAAAUCCCGUCUCC---- +>URS00026874C5 62 0.712 1.000E+00 2 67 72 0 65 67 +--UGAUGUGGCUCAAUGGCAGAGCAUCUCACUCGUAAUGAGAAGGUUGUGAGUUCGAUUCUCACCAUC---- +>URS0003419FE3 59 0.692 1.000E+00 3 67 72 1 65 67 +---GGAGUAACUCAAUGGUAGAGUCCCUGCCUUCCAAGCAGGUGGUUGCGGGUUCGAGUCCCGUCUCC---- +>URS0003419637 63 0.716 1.000E+00 2 68 72 0 66 67 +--CGACGUAGCUCAGGGGUAGAGCGUUUCCUUGGUAAGGAAGAGGUCACGGGUUCAAAUCCCGUCGUUG--- +>URS000340EAE2 57 0.707 1.000E+00 3 67 72 0 64 67 +---CCUUUAGCUCAGGGGUAGAGCAACGGACUUUUAAUCCGCAGGUCGUGGGUUCGAUCCCCACAGGG---- +>URS0002681850 56 0.681 1.000E+00 2 67 72 0 65 67 +--UGAUGUAGCUUAAUGGCAGAGCAUCUCAUUCGUAACGAGAAGGUUGGGGGUUCGACUCCCCCCAUC---- +>URS000340E6D8 57 0.769 1.000E+00 3 67 72 1 65 67 +---GUCGUAGUUCAGAGGUAGAACGUCAGCUUCCCAAGCUGAAUGUCGCGGGUUCGAUUCCCGCCGGC---- +>URS000340810E 68 0.800 1.000E+00 3 67 72 0 64 67 +---CCGGUAGCUCAGGGGUAGAGCAUCCGCCUUUUAAGCGGAUGGCCGUGGGUUCGAUCCCCACCCGG---- +>URS000268F378 60 0.723 1.000E+00 3 67 72 1 65 67 +---GGCGUAACUCAGCGGUAGAGUGUUAGCUUCCCAAGCUGAAGGUCGCGGGUUCGAAUCCCGUCGCC---- +>URS000340ABF8 58 0.707 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAAUGGUAGAGCGUCAGCCUUCCAAGCUGAAUGUUGCGAGUUCGAGUCUCGUCUCC---- +>URS0002687BFA 56 0.742 1.000E+00 3 67 72 0 65 67 +---ACAAUAGCUCAGuUGGUAGAGCGUUGGCUUCCCAAGCCGAAGGUCGCGGGUUCGAGCCCCGUUUGU---- +>URS0003498CDE 62 0.731 1.000E+00 2 68 72 0 66 67 +--CCCGGUAGCUCAAUGGUAGAGCGUUCGGCUGUUAACCGAAAUGUUGGAGGUUCGAAUCCUCCCCGGG--- +>URS000269CCD2 54 0.746 1.000E+00 2 67 72 0 66 67 +--UGGGAUAGCUCAGcUGGUAGAGCAUCUCCAUGGUAAGGAGAAGGUCGUGGGUUCGAUUCCCAUUCCC---- +>URS0000B1DEC8 47 0.569 1.000E+00 3 67 72 1 65 67 +---GACGUGGCGUAAUGGUAGCGCAUUGGAUUUCGGCUCCAAAGGCUGCGGGUUCGAGUCCUGUCGUC---- +>URS0002698936 59 0.753 1.000E+00 3 67 72 0 64 67 +---AAAGUGGCUCAGGGGUAGAGCAAUUCACUCGUAAUGAAUAGGUCGCGGGUUCGAUUCCCGCCUUU---- +>URS000269A6A5 59 0.723 1.000E+00 3 67 72 0 64 67 +---GAGGUAGAGCAGUGGUAGCUCGUCGGGCUCAUAACCCGAAGGUCGCAGGUUCGAGUCCUGCCCUC---- +>URS0002686B0D 54 0.761 1.000E+00 2 67 72 0 66 67 +--CGCAAUAGCUCAGUcGGUAGAGCAUUUCAUUCGUAACGAAAAGGUCGCGGGUUCGAGUCCCGCUCGC---- +>URS0003417AF4 60 0.784 1.000E+00 3 67 72 1 65 67 +---GCCGUAGUUCAGAGGUAGAACGUCAGCUUCCCAAGCUGAAUGUCGCGGGUUCGAUUCCCGCCGGC---- +>URS000325CA00 65 0.742 1.000E+00 2 67 72 0 65 67 +--UCCUGUAGCUCAGUGGUAGAGCAUCCGACCGGUGUGCGGAAGGUCAUGGGUUCGACUCCCGCCAGG---- +>URS0002692FEB 62 0.723 1.000E+00 3 67 72 1 65 67 +---GGCGUAACUCAGUGGUAGAGUGUUAGCUUCCCAAGCUGAAAGUCGCGGGUUCGAAUCCCGUCGCC---- +>URS000269FB7C 60 0.707 1.000E+00 3 67 72 1 65 67 +---GGCGUAGCUCAAUGGUAGAGCGUCAGCCUUCCAAGCUGAAUGUUGAGGGUUCGAAUCCCUUCGCC---- +>URS000269501C 58 0.647 1.000E+00 1 68 72 0 66 67 +-CGGAAAUAGCUCAGUGGUAGAGCACCACCUUGCCAAGGUGGGGGUCGCGGGUUCGAACCCCG-UUUCC--- +>URS00034042FD 60 0.723 1.000E+00 3 67 72 1 65 67 +---GGAGUAACUCAGUGGUAGAGUGUCAGCUUCCCAAGCUGAAGGCCGCGGGUUCGACCCCCGUCUCC---- +>URS000341EECF 63 0.738 1.000E+00 3 67 72 1 65 67 +---GGAAUAGCUCAGUGGUAGAGCGUCUCCUUGCCAAGGAGAAGGCCGCGGGUUCAAGUCCCGUUUCC---- +>URS00026957F1 56 0.776 1.000E+00 2 67 72 0 66 67 +--UGGGGUAGCUCAGcUGGUAGAGCAUCUCCAUGGUAAGGAGAAGGUCGUGGGUUCGAUUCCCAUCCCC---- +>URS0003414EAB 63 0.712 1.000E+00 2 67 72 0 65 67 +--UGAUGUAGCUCAGUGGUAGAGCACCUCCUUGGUAAGGAGGAGGCCGUGGGUUCAAUCCCCGCCAUC---- +>URS000273959B 44 0.563 1.000E+00 70 0 72 1 66 67 +GGCUCAGCA-----GUGGUAAGGCGUUGGUCUUGUAAACCAAAGAUCCAGGGUUCAAACCCCUGCUGGGCC- +>URS000340A28D 59 0.723 1.000E+00 3 67 72 0 64 67 +---CCCGUAGCUCAGUGGUAGCGCGCCCGGCUCAUAACCGGGUGGUCGUAGGUUCGAACCCUGCCGGG---- +>URS0000C753DB 55 0.661 1.000E+00 0 70 72 0 65 67 +GGAUCCGUGGCGCAAUGGUAGCGCGUCUGACUCCAGAUUAGAAGGUUGCGUGUU-----CACGUCGGGUUC- +>URS0003416389 57 0.692 1.000E+00 3 67 72 1 65 67 +---GGAAUAGCUCAGUGGUAGAGCGUUGCCUUGCCAAGGCAAAGGUCGCGGGUUCGACACCCGUUUCC---- +>URS00034079B1 69 0.753 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGUGGUAGAGCAUUUGCUUCCCAAGCAAAGGGUCGCGGGUUCGAUCCCCGUCUCC---- +>URS000269CF98 56 0.723 1.000E+00 3 67 72 1 65 67 +---GGAGUGGCUCAGUGGUAGAGCGUCGCCUUGCCAAGGCGAAUGUCGCGAGUUCGAAUCUCGUUUCC---- +>URS0003410B6B 66 0.753 1.000E+00 3 67 72 1 65 67 +---GGCGUAGCUCAGCGGUAGAGCGUCUGCUUGCCAUGCAGAAGGUCGCGGGUUCAAAUCCCGUCGCC---- +>URS0003409567 64 0.707 1.000E+00 3 67 72 1 65 67 +---GGAAUAGCUCAGUGGUAGAGCACCUCCUUGCCAAGGAGGGGGUCGCGGGUUCGAAUCCCGUUUCC---- +>URS0002684B0B 51 0.590 1.000E+00 2 67 72 0 65 67 +--UGAUGUGGCUUAGAGGUAAAGCACUGCCUUGGUAAGGCAGAGACCACGGGUUCGAGCCCCGUCAUC---- +>URS0002F0D4B2 54 0.707 1.000E+00 3 67 72 1 65 67 +---GGAAUGGCUCAGUGGUAGAGCGUCACCUUGCCAAGGUGAAUGUCGCGAGUUCGAAUCUCGUUUCC---- +>URS0003411AE8 64 0.738 1.000E+00 3 67 72 0 64 67 +---CCCGUAGCUCAGUGGUAGAGCGCCCGGCUCAUAACCGGGUGGUCGUAGGUUCGAACCCUGCCGGG---- +>URS000268381D 62 0.769 1.000E+00 3 67 72 1 65 67 +---GCAGUGGCUCAGAGGUAGAGCAUCGCCUUGCCAAGGCGAGGGUCGCGGGUUCGAAUCCCGUCUGC---- +>URS0003402B01 53 0.712 1.000E+00 3 67 72 1 66 67 +---GGAAUAGCUCAGuUGGUAGAGCAUCAGCUUCCCAAGCUGAGGGUCGCGGGUUCGAGUCCCGUUUCC---- +>URS00032BFE76 47 0.626 1.000E+00 2 68 72 0 66 67 +--CCCAGUGGCCUAAGGAUAAUGCACUGGCCUCCUAAGCCAGGGAUUGUGGGUUCAAGUCCCAUCUGGG--- +>URS000268FA83 60 0.707 1.000E+00 3 67 72 1 65 67 +---GGAAUAGCUCAGUGGUAGAGCAUCAGCUUCCCAAGCUGAGGGCCGCGGGUUCGAGUCCCGUUUCC---- +>URS000269DADB 55 0.738 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGUGGUAGAGCUUCUCGUUGCCAACGAGAAGGUCGCGGGUUCAAAUCCCGUCUCC---- +>URS0003408B70 43 0.666 1.000E+00 3 67 72 1 66 67 +---GGCGUAGCUCAAuUGGUAGAGCGUCACCCUUCCAAGGUGAAUGUUGUCGGUUCGAGUCCGAUCGCC---- +>URS000269BC64 58 0.676 1.000E+00 3 67 72 1 65 67 +---GGAAUAGCUCAAUGGUAGAGCAUCAGCCUUCCAAGCUGAGGGUUGCGGGUUCGAGUCUCGUUUCC---- +>URS000340E2EC 68 0.742 1.000E+00 2 67 72 0 65 67 +--UGAUGUAGCUCAGUGGUAGAGCACCUCCUUGGUAAGGAGGAGGUCGCGGGUUCAAUCCCCGCCAUC---- +>URS0003415F9C 63 0.707 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGUGGUAGAGCACUUCCUUGCCAAGGAAGAUGUCGCGGGUUCGAAUCCCGUCUCC---- +>URS0003417B1B 64 0.738 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGUGGAAGAGCAUUUGCUUCCCAAGUAAAGGGUCGCGGGUUCGAAUCCCGUCUCC---- +>URS0002F0F696 68 0.757 1.000E+00 2 67 72 0 65 67 +--UGAUGUGGCUCAAUGGUAGAGCAUCUCACUCGUAAUGAGAAGGUUGUGGGUUCGAUUCCCACCAUC---- +>URS0003A0692D 37 0.621 1.000E+00 3 67 72 0 65 67 +---AUAGUGGCGUAGuUGGUAACGCGCGACCUUGCCAAGGUCGAGACCGCGGGUUCGAGUCCCGUCUAU---- +>URS000329904C 47 0.661 1.000E+00 6 70 72 1 65 67 +------CUGGCGCAAUGGUAGCGCGUAGUCCUCCUAAGACUAAGGCUCAGGGUUCGAGUCCCUGGUGUAUC- +>URS000340C8D1 55 0.676 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGUGGUAGAGCUCUACCUUGCCAAGGUAGAGGUCGCGGGUUCAAGUCCCGUCUCC---- +>URS0003406FFE 58 0.707 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGCGGUAGAGCAUCAGCCUUCCAAGCUGAGGGUCGCGGGUUCGAAUCUCGUUUCC---- +>URS0000822106 63 0.753 1.000E+00 3 67 72 0 64 67 +---GACGUGGCUCAGCGGUAGAGCAUCGCCUUGCCAAGGCGAGGGUCGCGGGUUCGAUUCCCGUCGUC---- +>URS000318FC72 63 0.791 1.000E+00 0 66 72 0 66 67 +GCCUCAUUGGCUCAGGGGCAGAGCGUCUGUCUAGUAAACAGAAGGUCGCUGGUUCGAUUCCAGCAUG----- +>URS000268494E 66 0.753 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGUGGUAGAGCGUCAGCCUUCCAAGCUGAGGGUCGCGGGUUCGAAUCCCGUUUCC---- +>URS000268E6E1 56 0.707 1.000E+00 3 67 72 1 65 67 +---GGCGUAGUUCAAUGGUAGAGCGUCAGCUUCCCAAGCUGAAUGUUGCGGGUUCGAGCCCCGUCGCC---- +>URS0002687949 61 0.753 1.000E+00 3 67 72 1 65 67 +---GGAGUGGCUCAGGGGUAGAGCGUCUCCUUGCCAAGGAGAAGGUCGAGGGUUCAAAUCCCUUCUCC---- +>URS000255BAF8 55 0.707 1.000E+00 6 70 72 1 65 67 +------GUGGCGCAACGGUAGCGCCUCUGACUUCAGAUCAGAAGAUCGCGUGUUCAAAUCACGUCAGGGUC- +>URS00026A09FA 55 0.651 1.000E+00 2 67 72 0 65 67 +--UGAUAUAGCUUAAUGGCAGAGCAUCUCAUUCGUAACGAGAAGGUUGUGGGUUCGACUCCCACUAUC---- +>URS0003403DF0 60 0.723 1.000E+00 3 67 72 1 65 67 +---AAAGUAGCUCAGUGGUAGAGCAUCACCUUGCCAAGGUGAGGGUCGCGGGUUCAAAUCCCGUCUUU---- +>URS0002F0DDC5 50 0.712 1.000E+00 3 67 72 0 65 67 +---ACUAUAGCUUAGuUGGUAGAGUAUCUCAUUCGUAACGAGAAGGUCGUGGGUUCGAGUCCCACUAGU---- +>URS000341BAD8 56 0.692 1.000E+00 3 67 72 1 65 67 +---GGCAUAGCUCAGUGGUAGAGCGUCUCGUUGCCAACGAGAAGGUCGAGGGUUCGAGCCCCUUUGCC---- +>URS000340359C 67 0.784 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGUGGUAGAGCAUCUGCUUCCCAAGCAGAGGGUCGCGGGUUCGAUACCCGUCUCC---- +>URS000269338D 62 0.666 1.000E+00 2 67 72 0 65 67 +--UGAUGUAGCUCAGCGGUAGAGCACUUCCUUGGUAAGGAAGAGGUCACGGGUUCAAGUCCCGUCAUC---- +>URS0003413F07 68 0.800 1.000E+00 3 67 72 1 65 67 +---GGCAUGGCUCAGUGGUAGAGCGUCUGCUUCCCAAGCAGAAUGUCGCGGGUUCGAAUCCCGCUGCC---- +>URS000269BCA2 69 0.815 1.000E+00 3 67 72 1 65 67 +---GGGGUAGCUCAGUGGUAGAGCGUCUGCUUCCCAAGCAGGAUGUCGCGGGUUCGAUCCCCGUCCCC---- +>URS00026A1825 58 0.753 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGUGGUAGAGUUUCUGCCUUCCAAGCAGAAUGUCGCGGGUUCGAAUCUCGUCUCC---- +>URS0003410AA3 62 0.753 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGGGGUAGAGCGUCUCCUUGCCAAGGAGAAGGCCGCGGGUUCAAAUCCCGUCUCC---- +>URS00032A1D5A 59 0.723 1.000E+00 2 66 72 2 66 67 +--CCUCGUGGCGCAACGGUAGCGCGUCUGACUCCAGAUCAGAAGGUUGCGUGUUCAAAUCACGUCGG----- +>URS000268AF8D 67 0.712 1.000E+00 2 67 72 0 65 67 +--UGCUGUAGCUCAGUGGUAGAGCACUUCCUUGGUAAGGAAGAGGUCACGGGUUCAAGUCCCGUCAGC---- +>URS0002682712 52 0.723 1.000E+00 3 67 72 1 65 67 +---GGAAUGGCUCAGUGGUAGAGCGUCACCUUGCCAAGGUGAAUGUCGCGGGUCCGAAUCCCGUUUCC---- +>URS000268D671 58 0.805 1.000E+00 2 67 72 0 66 67 +--CGCUUUAGCUCAGuUGGCAGAGCGUCUCACUCGUAAUGAGAAGGUCGCGGGUUCGAUUCCCGCAGGC---- +>URS0002699779 58 0.753 1.000E+00 3 67 72 1 65 67 +---GCGGUAGUUCAAUGGUAGAGCCUCAGCCUUCCAAGCUGAUUGUUGCGGGUUCGAGUCCCGUCCGC---- +>URS0003317FD6 58 0.676 1.000E+00 0 70 72 0 65 67 +GGCUCUGUGGCGCAAUGGUAGCGCAUCUGACUCCAGAUCAGAAGGUUGCGUGUU-----CACGUCAGGGUC- +>URS0000DAF9A7 59 0.695 1.000E+00 0 68 72 0 66 67 +GCUGGGGUGGCUCAGUGGUAGAGCGCUUGCCUUGCAAGCCUGAGGCCCUGGGUUCGAUUCCU--CAGCA--- +>URS000268CDCD 56 0.681 1.000E+00 2 67 72 0 65 67 +--UGAUGUAGCUUAAUGGCAGAGCAUCUCAUUCGUAACGAGAAGGUUGAGGGUUCGACUCCCUCCAUC---- +>URS0000B20816 68 0.716 1.000E+00 2 68 72 0 66 67 +--CCCUGUAGCUCAAUGGUAGAGCGCUCGCCUGUUAAGCGAAGGGUUGUAGGUUCGAAUCCUACCGGGG--- +>URS000270C287 43 0.641 1.000E+00 2 67 72 0 66 67 +--CGACGUGGCCUAAUGGaUAAGGCGCACCCCUCCUAAGGGUGAGAUUGUGGGUUCGAGUCCCACCGUU---- +>URS000269B82C 60 0.753 1.000E+00 3 67 72 0 64 67 +---GCCGUAGCUCAGUGGUAGAGCGCAUCCUUGGUAAGGCUGAGGUCGUGGGUUCGAUUCCCCCCGGC---- +>URS0003413D39 67 0.723 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGUGGUAGAGCGCCUGCCUUCCAAGCAGGAUGUCGUGGGUUCGAACCCCAUCUCC---- +>URS00034191FA 61 0.738 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGUGGUAGAGCUCCUGCCUUCCAAGCAGGCUGUCGCGGGUUCGAAUCCCGUCUCC---- +>URS000340BB80 54 0.686 1.000E+00 2 68 72 0 66 67 +--CGGUGUAGCUCAACGGUAGAGCGGCUCAUUCGUAAUGAGUAGGUUGGGGGUUCGAGUCCCUUCACCG--- +>URS0002F103F2 57 0.646 1.000E+00 3 67 72 1 65 67 +---AAAGUGGCCUAAUGGUAUGGCAUCAGCUUCCCAAGCUGGGGGUUGCGGGUUCGAGCCCCGUCUUU---- +>URS000340B7EE 60 0.769 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGUGGUAGAGCGUCUCGUUGCCAACGAGAAGGUCGCGGGUUCAAUUCCCGUCUCC---- +>URS000269940C 58 0.676 1.000E+00 3 67 72 1 65 67 +---GGAAUAGCUCAGUGGUAGAGCGCCGCCUUGCCGAGGCGGAGGUCGCGGGUUCAAGUCCCGUUUCC---- +>URS000341D714 63 0.723 1.000E+00 3 67 72 1 65 67 +---GGCGUAGCUCAAUGGUAGAGCAUCAGCUUCCCAAGCUGAGGGUCGCGGGUUCGAGCCCCGUCGCC---- +>URS000341BF40 65 0.712 1.000E+00 2 67 72 0 65 67 +--UGCUAUAGCUCAGUGGUAGAGCACCUCCUUGGUAAGGAGGAGGUCGUGGGUUCAAGUCCCACUAGC---- +>URS000341774F 59 0.707 1.000E+00 3 67 72 1 65 67 +---GGCGUAGCUCAGUGGUAGAGCGUGUCCUUGCCAAGGACGAGGCCGUGGGUUCGAAUCCCAUCGCC---- +>URS00022CDC9C 46 0.696 1.000E+00 3 67 72 0 65 67 +---CCCGUAGUGUAGCGGuUAUCACGUCGCCCUGUCACGGCGAAGAUCGCGGGUUCGAUUCCCGUCGGG---- +>URS000341338D 54 0.692 1.000E+00 3 67 72 1 65 67 +---GGCAUAGCUCAGUGGUAGAGCGUCUCGUUGCCAACGAGAAGGUCGAGGGUUCAAGUCCCUUUGCC---- +>URS000341A417 66 0.716 1.000E+00 2 68 72 0 66 67 +--CGACGUAGCUCAGUGGUAGAGCGCUUCCUUGGUAAGGAAGAGGUCACGGGUUCAAAUCCCGUCGUUG--- +>URS0002698003 55 0.671 1.000E+00 2 67 72 0 66 67 +--CGGAGUGGCGCAAuUGGUAGCGCGCCCGCCUUGUAAGCGGAUGGUUGUGGGUUCGAGUCCCAUCUCC---- +>URS0003406021 63 0.723 1.000E+00 3 67 72 1 65 67 +---GGCGUAGUUCAGCGGUAGAACGUCUCCUUGCCAAGGAGAAGGUCGUGGGUUCGAAUCCCAUCGCC---- +>URS0003402F71 60 0.676 1.000E+00 3 67 72 1 65 67 +---GGAAUAGCUCAGUGGUAGAGCACCUCCUUGCCAAGGAGGGGGUCGCGGGUUCAAGCCCCGUUUCC---- +>URS0002689399 65 0.742 1.000E+00 2 67 72 0 65 67 +--UGAUGUGGCUCAAUGGCAGAGCAUCUCACUCGUAAUGAGAAGGUUGUGGGUUCGAUUCCCACCAUC---- +>URS000335D36A 49 0.701 1.000E+00 2 68 72 0 66 67 +--CCUUGUGGCGCAACGGUAGCGCGUCUUACUCCAGAUCAGAAGGUUGCGUUUUCAAAUCACGUCGGGG--- +>URS0003416A47 66 0.707 1.000E+00 3 67 72 1 65 67 +---GGCGUAGUUCAGUGGUAGAACGUCUGCUUGCCAAGCAGAAGGUCGUGGGUUCAAACCCCAUCGCC---- +>URS000269AD59 67 0.753 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGUGGUAGAGCGUCGGCCUUCCAAGCCGAGGGUCGCGGGUUCGAAUCCCGUUUCC---- +>URS0003181BB9 65 0.716 1.000E+00 2 68 72 0 66 67 +--CUCCGUAGCUCAGUGGUAGAGCAUCCGACCGGUGUACGGAAGGUCAUGGGUUCGACUCCCGUCGGGG--- +>URS000340FF70 66 0.769 1.000E+00 3 67 72 1 65 67 +---GGUGUGGCUCAGUGGUAGAGCGUCUCCUUGCCAAGGAGAAGGUCGUGGGUUCAAAUCCCAUCACC---- +>URS0003413DAA 65 0.723 1.000E+00 3 67 72 1 65 67 +---GGCGUAGCUCAAUGGUAGAGCAUCAGCCUUCCAAGCUGAGGGUUGCGGGUUCGAGUCCCGUCGCC---- +>URS00033F602E 59 0.746 1.000E+00 2 68 72 0 66 67 +--CACGGUGGCCAAGUGGUAAGGCGUCGGUCUCAUAAACCGAAGAUCGCGGGUCCGAACCCCGUCCGUG--- +>URS000277FFD5 47 0.647 1.000E+00 70 0 72 1 66 67 +UCCAGGGUAGCCGAG-----UGGCACCUGCCCGGUAAGCAGAAGGGUGUGGGUUCGAUUCCCGCCCCUGGA- +>URS00034029CE 65 0.753 1.000E+00 3 67 72 1 65 67 +---GGAGUAACUCAGUGGUAGAGUAUCUGCUUCCCAAGCAGAAAGUCGCGGGUUCGAUCCCCGUCUCC---- +>URS0003405CCD 55 0.707 1.000E+00 3 67 72 1 65 67 +---GGCGUAGCUCAGUGGUAGAGCGUCUCGUUGCCAACGAGAAGGUCGUGGGUUCAAGUCCCAUUGCC---- +>URS00034205E7 62 0.707 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGUGGUAGAGCAUCAGCUUCCCAAGCUGAGGGUCGUGGGUUCGAUCCCCAUUUCC---- +>URS0003289CD3 57 0.661 1.000E+00 0 70 72 0 65 67 +GGCCUUGUGGCGC-----UAACGCGUCUGCCUACGGAGCAGAAGAUUGCUGGUUCGAAUCCAGUCAAGGUC- +>URS0002684D82 65 0.757 1.000E+00 2 67 72 0 65 67 +--UGAUGUGGCUCAGGGGUAGAGCGUUUCCUUGGUAAGGAAGAGGUCGCGGGUUCAAUUCCCGCCAUC---- +>URS0003402D5D 60 0.707 1.000E+00 3 67 72 1 65 67 +---GGAAUAGCUCAGUGGUAGAGCAUCACCUUGCCAAGGUGAGGGUCGCGGGUUCGAAUCCCGUUUCC---- +>URS000340A46F 63 0.738 1.000E+00 3 67 72 1 65 67 +---GGUGUAACUCAGUGGUAGAGUGUCUGCUUCCCAAGCAGAAUGUCGUGGGUUCGAAUCCCAUCACC---- +>URS000269CD2B 66 0.738 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGUGGUAGAGCGUCAGCUUCCCAUGCUGAUGGUCACGGGUUCGAUCCCCGUCUCC---- +>URS000321D164 41 0.656 1.000E+00 2 68 72 0 66 67 +--CCCGGUGCCUAAUGGAUAAGGCACUGGCCUCCUAAGCCAGGGAUUGUGGGUUCGAGUCCCAUCUGGG--- +>URS000341152C 64 0.769 1.000E+00 3 67 72 1 65 67 +---GGGGUAGCUCAGUGGUAGAGCUCCUGCCUUCCAAGCAGGCGGUCGCGGGUUCAAGUCCCGUCCCC---- +>URS000340D58D 70 0.784 1.000E+00 3 67 72 1 65 67 +---GGCGUAGCUCAGUGGUAGAGCGUCUGCUUGCCAAGCAGAAGGUCGCGGGUUCGAAACCCGUCGCC---- +>URS000268E70B 59 0.753 1.000E+00 3 67 72 1 65 67 +---GGAGUGGCUCAGUGGUAGAGCGUCGCCUUGCCAAGGCGAAUGUCGCGGGUUCGAAUCCCGUUUCC---- +>URS0002F0E03A 64 0.769 1.000E+00 3 67 72 1 65 67 +---GCAGUAGCUCAGUGGUAGAGCAUCGGCUUCCCAAGCCGAGGGUCACGGGUUCGAAUCCCGCUUGC---- +>URS000341368F 63 0.753 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGGGGUAGAGCGUCUGCCUUCCAAGCAGAAUGUCGAGGGUUCGAAUCCCUUCUCC---- +>URS0003419816 58 0.738 1.000E+00 3 67 72 1 65 67 +---GGAGUAGCUCAGGGGUAGAGCAUCACCCUUCCAAGGUGAAAGUCGCGGGUUCGAAUCCCGUCUCC---- +>URS0002F0B647 59 0.676 1.000E+00 3 67 72 1 65 67 +---AAAGUGGCCUAGUGGUAUGGCAUCAGCUUCCCAAGCUGGGGAUCGCGGGUUCGAAUCCCGUCUUU---- +>URS000268747A 61 0.764 1.000E+00 2 68 72 0 67 68 +--CGCGAUAGCUCAGcCGGUAGAGCAUCCGCCUCGUAAGCGGAAGGCCACGGGUUCGAAUCCCGUUCGCG--- +>URS000340C735 54 0.727 1.000E+00 3 67 72 1 66 68 +---GGCGUAGCUCAGaCGGUAGAGCGCCUCCCUGCCAUGGAGGAGGCCGCGGGUUCGAGUCCCGUCGCC---- +>URS000268AF03 51 0.727 1.000E+00 3 67 72 1 66 68 +---GUAAUAGCUCAGuUGGUAGAGCGUCGGCUUCCCAAGCCGAAUGUCGCGGGUUCGAGUCCCGUUUAC---- +>URS000268CABF 53 0.757 1.000E+00 3 67 72 1 66 68 +---GCGGUAGUUCAAuUGGUAGAGCGUCAGCCUUCCAAGCUGAUUGUUGCGGGUUCGAGUCCCGUCCGC---- +>URS0003407C37 72 0.735 1.000E+00 1 68 72 0 67 68 +-CGGGUGUAGCUCAGUGGUAGAGCGCCUGCUUGCCAAGCAGGAGGUCGCGGGUUCAAAUCCCGUCGCCC--- +>URS0002F0C5DD 53 0.750 1.000E+00 2 68 72 0 67 68 +--CGCAAUAGCUUAGuUGGUAGAGCAUCUCAUUCGUAACGAGAAGGUCGUGGGUUCGAGUCCCACUCGCG--- +>URS0000DAB6BE 64 0.753 1.000E+00 3 67 72 3 67 68 +---UUCGUGGCGCAAUGGUAGCGCGUCUGACUCCAGAUCAGAAGGUUGCGUGUUCGAUUCACGUCGGG---- +>URS0002684B10 52 0.696 1.000E+00 3 67 72 0 65 68 +---AACUUAGCUCAGcUGGUAGAGCAUCUCCCUCGUAAAGAGAAGGUCGUAGGUUCGACUCCUAUAGUU---- +>URS0002699567 53 0.727 1.000E+00 3 67 72 1 66 68 +---GUAGUAGCUCAGuUGGUAGAGCAUCAGCUUCCCAAGCUGAGGGUCGCGGGUUCGAGUCCCGUUUAC---- +>URS0003420040 57 0.757 1.000E+00 3 67 72 1 66 68 +---GGCGUGGUUCAGuUGGUAGAACGUCUCCUUGCCAAGGAGAAGGUCACGGGUUCGAGUCCCGUCGCC---- +>URS0003411ABD 58 0.676 1.000E+00 1 68 72 0 67 68 +-CGGGUAUAGCUCAGUGGUAGAGCGUCUCGUUGCCAACGAGAAGGUCGUGGGUUCAAAUCCCAUUGCCC--- +>URS0002697CFD 56 0.691 1.000E+00 1 68 72 0 67 68 +-CGGAAGUGGCUCAAUGGUAGAGCGUCACCUUGCCAAGGUGAAUGUUGCGAGUUCGAGUCUCGUCUUUC--- +>URS000269992D 54 0.757 1.000E+00 3 67 72 0 65 68 +---CCCGUAGCUCAAUcGGUAGAGCACCGACCUCAUAAGUCGAUGGUUGCGGGUUCGAGCCCCGCCGGG---- +>URS000269AD1F 55 0.735 1.000E+00 4 70 72 0 67 68 +----CCAUGGCGCAGuUGGUAGCGCGUCUCGUUCGCAAUGAGAAGGUCGGGGGUUCGAAUCCCCCUGGGUCC- +>URS0003409C32 64 0.705 1.000E+00 1 68 72 0 67 68 +-CGGGUGUAGCUCAGAGGUAGAGCAUCGGCUUCCCAAGCCGAGGGUCGCGGGUUCAAAUCCCGUCACCC--- +>URS0003405F3C 67 0.735 1.000E+00 1 68 72 0 67 68 +-CGGGUGUAGCUCAGUGGUAGAGCAGCUGCCUUCCAAGCAGCAGGCCGCGGGUUCGAGUCCCGUCGCCC--- +>URS000341D7B8 60 0.705 1.000E+00 1 68 72 0 67 68 +-CGGGUGUAGCUCAGUGGUAGAGCGUCACCUUGCCAAGGUGAAUGUCGAGGGUUCGAAUCCCUUCACCC--- +>URS000268A8FC 56 0.707 1.000E+00 3 67 72 1 65 68 +---GAAGUGGCUCAAUGGUAGAGCGCCACCUUGCCAAGGUGGAUGUUGCGGGUUCGAGUCCCGUCUUC---- +>URS000340BED6 47 0.686 1.000E+00 3 67 72 0 66 68 +---AGAAUAGCUCAGUugGCUAGAGCGUUUCCUUGCCAAGGAAGAGGUCGCGGGUUCGAGUCCCGUUUCU---- +>URS0003406007 58 0.779 1.000E+00 4 70 72 0 67 68 +----CCGUGGCGCAGuUGGUAGCGCGUCUCGUUCGCAAUGAGAAGGUCGGGGGUUCGAUUCCCCCCGGGUCC- +>URS00034137FF 51 0.727 1.000E+00 3 67 72 1 66 68 +---GGAAUAGCUCAGuUGGUAGAGCGUCAGCUUCCCAAGCUGAAGGUCGCGGGUUCGAGACCCGUUUCC---- +>URS000341AEE2 56 0.742 1.000E+00 3 67 72 1 66 68 +---GGCGUGGUUCAGuUGGUAGAACGUCUCCUUGCCAAGGAGAAGGCCACGGGUUCGAGUCCCGUCGCC---- +>URS000340AECB 58 0.723 1.000E+00 3 67 72 1 65 68 +---GGAGUAGUUCAGUGGUAGAACAUCUCGUUGCCAACGAGAAGGUCGCGGGUUCGAUCCCCGUCUCC---- +>URS0002F09FBD 62 0.716 1.000E+00 2 68 72 0 66 68 +--CUAUGUAGCUCAGGGGUAGAGCACUUCCUUGGUAAGGAAGAGGUCGCGGGUUCAAAUCCCGCCAUUG--- +>URS00034078F0 50 0.757 1.000E+00 3 67 72 1 66 68 +---GGAGUAGCUCAGcUGGUAGAGCGUCUCGUUGCCAACGAGAAGGUCGCGGGUUCGAGUCCCGUCUCC---- +>URS000340D7B7 61 0.738 1.000E+00 3 67 72 1 65 68 +---GACGUGGCUCAGCGGUAGAGCAUCACCUUGCCAAGGUGAGGGUCGCGGGUUCGAAUCCCGUCGUC---- +>URS00039AFBAA 62 0.692 1.000E+00 3 67 72 1 65 68 +---GGAAUAGCUCAGCGGUAGAGCGCCAGCCUUCCAAGCUGGGGGUCGCGGGUUCGAAUCCCGUUUCC---- +>URS0003407F09 60 0.705 1.000E+00 1 68 72 0 67 68 +-CGGGUGUAGCUCAGUGGUAGAGCGUCAGCUUCCCAAGCUGAAUGUCGCGAGUUCAAGUCUCGUCACCC--- +>URS000269B03B 46 0.750 1.000E+00 2 68 72 0 67 68 +--CGCUUUAGCUCAGUcGGUAGAGCAGCUCAUUCGUAAUGAGCAGGUCGCGGGUUCGAGUCCCGUUCGCG--- +>URS000269E103 64 0.705 1.000E+00 1 68 72 0 67 68 +-CGGAAGUGGCUCAGUGGUAGAGCAUCACCUUGCCAAGGUGAGGGUCGGGGGUUCGAAUCCCCUCUUCC--- +>URS000269E13E 55 0.764 1.000E+00 2 68 72 0 67 68 +--CGCAAUAGCUCAGuUGGUAGAGCAUUUCAUUCGUAACGAAAAGGUCGCGGGUUCGAGUCCCGCUCGCG--- +>URS00031D8C5C 58 0.716 1.000E+00 4 70 72 0 66 68 +----CGGUGGCCAAGUGGUAAGGCGUCGGUCUCGUAAACCGAAGAUCGUGGGUUCAAACCCCACCUGUGCC- +>URS0002687719 55 0.727 1.000E+00 3 67 72 1 66 68 +---GGAGUAGCUCAAuUGGUAGAGCAUCAGCCUUCCAAGCUGAGGGUCGCGGGUUCGAGUCCCGUCUCC---- +>URS00019D0251 52 0.735 1.000E+00 2 68 72 0 67 68 +--CUCGUUAGCUCAGcCGGUAGAGCAUCUCCCUUUUAAGGAGGGGGCCGUUGGUUCGAAUCCAACACGGG--- +>URS0000EEA2E6 64 0.803 1.000E+00 5 70 72 1 66 68 +-----AUUGGCUCAGUGGCAGAGCGUCUGUCUAGUAAACAGAAGGUCGCUGGUUCGAUUCCAGCAUGAGGC- +>URS0002F0FA7C 49 0.712 1.000E+00 3 67 72 1 66 68 +---GAAUUAGCUCAGcUGGUAGAGCGUUGGCUUCCCAAGCCGAAGGUCGCGGGUCCGAGUCCCGUAUUC---- +>URS000268B607 54 0.712 1.000E+00 3 67 72 1 66 68 +---GUAAUAGCUCAGuUGGUAGAGCGUUGGCUUCCCAAGCCGAAGGUCGCGGGUUCGAGCCCCGUUUAC---- +>URS0002F0DC14 55 0.727 1.000E+00 3 67 72 1 66 68 +---GCUGUAGCUCAAuUGGUAGAGCAUUGGCCUUCCAAGCCGAGGGUUGCGGGUUCGAGUCCCGUCGGC---- +>URS0002691F71 49 0.742 1.000E+00 3 67 72 1 66 68 +---GGGUUAGCUCAUcCGGUAGAGCGUCUGCUUCCCAAGCAGAAGGCGGCGGGUUCGAGUCCCGUACCC---- +>URS0003410B78 46 0.727 1.000E+00 3 67 72 1 66 68 +---GGGUUAACUCAGuUGGUAGAGUGUCAGCUUCCCAAGCUGAAUGUCGCGGGUUCGAGUCCCGUACUC---- +>URS000268F405 55 0.727 1.000E+00 3 67 72 1 66 68 +---GGAAUAGCUCAGuUGGUAGAGCGUCAGCCUUCCAAGCUGAGGGUCGCGGGUUCGAGUCCCGUUUCC---- +>URS000341DD9E 48 0.731 1.000E+00 3 67 72 0 66 68 +---CGCGUAGCACAGCugGAUAAUGCGUCGGCCUCCUAAGCCGAAGAUCGCGGGUUCGAAUCCUGCCGCG---- +>URS000341EE01 57 0.692 1.000E+00 3 67 72 1 65 68 +---AGGUUAGCUCAGCGGUAGAGCACCUGCCUUACACGCAGGGGGUCGUGGGUUCGAAUCCCUCACCU---- +>URS00033473D0 61 0.690 1.000E+00 0 70 72 0 66 68 +GAGGCUGUGGCUCAGUGGUAGAGCGGUUGCCUGCCAAUCAGAAGGUUGGUGGUUCGAUCC----CUGCCUC- +>URS00027C7BC1 45 0.661 1.000E+00 70 0 72 1 67 68 +UCCAGGGUAGCCC--GGGUA--GCAUCUGCCUGGUAAGCGGAACACCGUGGGUUCGAUUCCCAUCCCUGGA- +>URS000340D78B 47 0.681 1.000E+00 3 67 72 1 66 68 +---GGAAUAGCUCAAcUGGUAGAGCGACAGCCUUCCAAGCUGUAGGUUGCGGGUUCGAGUCCCGUUUCC---- +>URS0002694D8A 56 0.757 1.000E+00 3 67 72 1 66 68 +---GGCAUAGCUCAGuUGGUAGAGCGUCAGCUUCCCAAGCUGAAGGUCGCGGGUUCGAUUCCCGUUGCC---- +>URS0003413E64 68 0.779 1.000E+00 1 68 72 0 67 68 +-CCGAUGUAGCUCAGGGGUAGAGCGUUUCCUUGGUAAGGAAAAGGUCGCGGGUUCAAUUCCCGCCGUUG--- +>URS0003417B6A 60 0.761 1.000E+00 2 68 72 0 66 68 +--CGGAGUAGCUCAGGGGUAGAGCAACGCCCUCGUAAUGCGUAGGUCGCGGGUUCGAUUCCCGCCUUCG--- +>URS0003414D2A 61 0.738 1.000E+00 3 67 72 1 65 68 +---CCUGUAGCUCAGCGGUAGAGCAUUCGUCUUAUAAGCGAAAGGUCGUGGGUUCAAACCCCGCCGGG---- +>URS0002695C1F 54 0.707 1.000E+00 3 67 72 1 65 68 +---GAAGUGACUCAAUGGUAGAGUGUCACCUUGCCAAGGUGAAAGUUGCGGGUUCGAGUCCCGUCUUC---- +>URS00026A05FB 59 0.794 1.000E+00 2 68 72 0 67 68 +--CGCUUUAGCUCAGUcGGUAGAGCGUCUCACUCGUAAUGAGAAGGUCGCGAGUUCGAUUCUCGCAAGCG--- +>URS00030AB394 53 0.676 1.000E+00 2 68 72 0 67 68 +--CUCUGUGGCCUAAUGGaUAAGGCAUCGGCCUCCUAAGUCGGGGAUUGUGGGUUCGAGUCCCAUCAGAG--- +>URS0002F0F331 54 0.761 1.000E+00 2 67 72 0 66 68 +--UAAUGUGGCUCAGaUGGUAGAGCAGCUCACUCGUAAUGAGCAGGUCGCGGGUUCGAGUCCCGCCAUU---- +>URS000268FC0A 54 0.707 1.000E+00 3 67 72 1 65 68 +---GAAGUGACUCAAUGGUAGAGUGUCACCUUCCCAAGGUGAAAGCUGCGGGUUCGAGUCCCGUCUUC---- +>URS000268213D 58 0.707 1.000E+00 3 67 72 1 65 68 +---UUGGUAGCUCAAUGGUAGAGCAGCGGCCUUUUAAGCCGUUGGCUGUGGGUUCGAGUCCCACCCGA---- +>URS000269D942 53 0.712 1.000E+00 3 67 72 1 66 68 +---GGAAUAGCUCAGuUGGUAGAGCAUCAGCUUCCCAAGCUGAGGGUCGCGGGUUCGAGUCCCGUUUCC---- +>URS000340AD0B 69 0.735 1.000E+00 3 70 72 0 67 68 +---CCGGUAGUUCAGUGGUAGGACACCCGCUUUGCAGGCGGGAGGUCGCGGGUUCGAAUCCCGCCCGGUCC- +>URS0002691758 53 0.727 1.000E+00 3 67 72 1 66 68 +---GGAGUAGCUCAGuUGGUAGAGCAUCAGCUUCCCAAGCUGAGGGUCGCGGGUUCGAGUCCCGUUUCC---- +>URS0000AE1F58 47 0.681 1.000E+00 3 67 72 1 66 68 +---AUAGUAGUACAGuUGGUAGUACGCCACCUUGCCAAGGUGGAGGUCGCGGGUUCGAGUCCCGUCUGU---- +>URS0000C2753C 54 0.720 1.000E+00 0 66 72 0 67 68 +GCCUCUGUGGCCUAAUGGaUAAGGCACCGGCCUCCUAAGCCGGGGAUUGCGGGUUCGAGUCCCGCCAG----- +>URS000340AB2A 62 0.731 1.000E+00 2 68 72 0 66 68 +--CGGGGUAGCUCAGUGGCAGAGCAACCGCCUUGUAAGCGGUAGGUCGUGGGUUCAAUUCCCACUCUCG--- +>URS000269AD52 60 0.738 1.000E+00 3 67 72 1 65 68 +---GAAGUGGCUCAGGGGUAGAGCAUCGCCUUGCCAAGGCGAGGGUCGCGGGUUCGAAUCCCGUCUUC---- +>URS0003413DBD 57 0.742 1.000E+00 3 67 72 1 66 68 +---GGAGUAGCUCAGuUGGUAGAGCAUCAGCCUUCCAAGCUGAGGGUCGCGGGUUCGAGUCCCGUCUCC---- +>URS000269ACE2 61 0.692 1.000E+00 2 66 72 0 64 68 +--CUAUGUAGCUCAGCGGUAGAGCACUUCCUUGGUAAGGAAGAGGUCACGGGUUCAAGUCCCGUCAU----- +>URS000311AF71 65 0.779 1.000E+00 0 67 72 0 67 68 +GCCUCAUUGGCUCAGGGGCAGAGCGUCUGUCUAGUAAACAGAAGGUCGCUGGUUCGAUUCCAGCAUGA---- +>URS0003498982 49 0.705 1.000E+00 2 68 72 0 67 68 +--CCGCGUGGCCUAAUGGaUAAGGCGUCGGUCUCCUAAACCGAAGAUUGGGGGUUCGAGUCCCCCCGUGG--- +>URS0003410D80 63 0.691 1.000E+00 3 70 72 0 67 68 +---CCGGUAGUUCAGCGGUAGAAUACCCGACUUGCACUCGGGAGGUCGCGGGUUCAAAUCCCGCCCGGUCC- +>URS0002696941 55 0.656 1.000E+00 2 68 72 0 66 68 +--CGGCGUGGCGCAACGGUAGCGCAACUGAUUUGUAAUCAGUGGGUUGCGGGUUCGAUUCCUGUCGCCG--- +>URS00033DC9F9 59 0.701 1.000E+00 4 70 72 0 66 68 +----CUGUGGCGCAACGGUAGCGCAUCUGACUCCAGAUCAGAAGGUUGUCCGUUCGAAUCGGGCCGGGGUC- +>URS000268DF23 57 0.705 1.000E+00 1 68 72 0 67 68 +-CGGAAGUGGCUCAAUGGUAGAGCGUCACCUUGCCAAGGUGAAUGUUGUGGGUUCGAGUCCCGUCUUUC--- +>URS0002F0AC00 64 0.676 1.000E+00 1 68 72 0 67 68 +-CGGGUGUAGCUCAAUGGUAGAGCAUCAGCUUCCCAAGCUGAGGGUUGCGAGUUCGAUCCUCGUCACCC--- +>URS000341BAE7 52 0.742 1.000E+00 3 67 72 1 66 68 +---GGAAUAGCUCAGUGGcUAGAGUGUCUCCUUGCCAAGGAGAAGGUCGCGGGUUCGAAUCCCGUUUCC---- +>URS00034099C6 54 0.696 1.000E+00 3 67 72 1 66 68 +---AGUGUAGCUCAGuUGGUAGAGCACUUCCUUGCCAAGGAAGGGGUCGCGGGUUCGAGUCCCGUCACU---- +>URS000340905D 56 0.676 1.000E+00 1 68 72 0 67 68 +-CGGGUAUAGCUCAGUGGUAGAGCGUCUCGUUGCCAACGAGAAGGUCGAGGGUUCAAGUCCCUUUGCCC--- +>URS0002693A40 50 0.742 1.000E+00 3 67 72 1 66 68 +---GCGGUAGUUCAAuUGGUAGAGCAUCAGCCUUCCAAGCUGACUGUUGCGGGUUCGAGUCCCGUCCGC---- +>URS0003B00770 58 0.671 1.000E+00 2 68 72 0 66 68 +--CUCCUUGGCGCAACGGUAGCGCGUUCGUCUCCAAAGCGAAAGGUUGCGUGUUCAAAUCACGUAGGGG--- +>URS000303C910 47 0.705 1.000E+00 1 68 72 1 67 68 +-ACAUACUAGCUCA-UGGUAGAGCAAUUCCCUCUUAAGGAAAAGGUCGCGGGUUCAACUCCUGCGUAUG--- +>URS000341F757 55 0.757 1.000E+00 3 67 72 1 66 68 +---GGCAUAGCUCAGUcGGUAGAGCGUCAGCUUCCCAAGCUGAAGGUCGCGGGUUCGAUUCCCGUUGCC---- +>URS0003413E43 45 0.742 1.000E+00 3 67 72 1 66 68 +---GGAGUAGCUCAGcUGGUAGAGCGUCACCCUUCCAAGGUGAAUGUCGCGGGUUCGAAUCUCGUCUCC---- +>URS000341DE0A 53 0.727 1.000E+00 3 67 72 1 66 68 +---GGCGUAGUUCAGuUGGUAGAACGCCUCCUUGCCAAGGAGAAGGUCGCGGGUUCAAGUCCCGUCGCC---- +>URS0002685229 62 0.723 1.000E+00 3 67 72 1 65 68 +---GACGUGGCUCAGUGGUAGAGCAUCGCCUUGCCAAGGCGAGGGUCGAGGGUUCGAAUCCCUUCGUC---- +>URS0002692365 51 0.727 1.000E+00 3 67 72 1 66 68 +---GGAGUAGCUCAGuUGGUAGAGCGUCAGCCUUCCAAGCUGAAUGUCGCGGGUUCGAGCCCCGUUUCC---- +>URS000340C38F 61 0.632 1.000E+00 1 68 72 0 67 68 +-CGGGUGUAGCUCAAUGGUAGAGCACCAGCCUUCCAAGCUGGGUGUUGUGGGUUCGAGUCCCAUCACCC--- +>URS0002694309 64 0.676 1.000E+00 1 68 72 0 67 68 +-CGGGUGUAGCUCAACGGUAGAGCACCAGCUUCCCAAGCUGGGGGUUGCGGGUUCGAUUCCCGUCACCC--- +>URS000341B632 50 0.761 1.000E+00 3 67 72 0 66 68 +---CCUGUAGCUCAGCugGAUAGAGCAUCUCCCUUCUAAGGAGGUGGCCGCAGGUUCGAAUCCUGCCAGG---- +>URS000341A9D8 66 0.691 1.000E+00 1 68 72 0 67 68 +-CGGGUGUAGCUCAAUGGUAGAGCAUCGGCCUUCCAAGCCGAGGGCUGCGGGUUCGAAUCCCGUCGCCC--- +>URS0002683899 52 0.681 1.000E+00 3 67 72 1 66 68 +---GGAAUAGCUCAAuUGGUAGAGCAUCAGCCUUCCAAGCUGAGGGUUGCGGGUUCGAGUCCCGUUUCC---- +>URS0003421274 57 0.691 1.000E+00 1 68 72 0 67 68 +-CGGGUGUAGUUCAGUGGUAGAACGUCACCUUCCCAAGGUGAAUGUCGUGGGUUCGAGUCCCAUCUCCC--- +>URS00026910DF 56 0.769 1.000E+00 3 67 72 1 65 68 +---GGGGUGGCUCAGCGGUAGAGCGUCGCCUUGCCAAGGCGAAUGUCGCGGGUUCAAAUCCCGUCCUC---- +>URS0003418709 61 0.707 1.000E+00 3 67 72 1 65 68 +---UGCGUAGCUCAGUGGGAGAGCGCCUGCCUUACAAGCAGGAGGUCGUAGGUUCAAGUCCUGCCGCA---- +>URS000340F45D 59 0.720 1.000E+00 1 68 72 0 67 68 +-CGGGUGUAGCUCAGGGGUAGAGCGUCAGCUUCCCAAGCUGAAUGUCGCGGGUUCAAAUCCCGUCGCCC--- +>URS00034159F4 66 0.735 1.000E+00 1 68 72 0 67 68 +-CGGGUGUAGCUCAGUGGUAGAGCGGCUGCUUGCCAUGCAGCAGGUCGCGGGUUCGAGUCCCGUCGCCC--- +>URS0003414F85 50 0.757 1.000E+00 3 67 72 1 66 68 +---GCGAUAGCUCAGuUGGUAGAGCGCAACCCUCCCAAGGGUGAGGUCGCGGGUUCGACUCCCGUUCGC---- +>URS0001D7CA29 58 0.731 1.000E+00 2 68 72 0 66 68 +--CCUCGUGGCGCAAUGGAAGCGCGUCGGUUUCCGGAGCCGAAGGUUGUGGGUUCAAGUCCCGCCGGGG--- +>URS0000CB93DA 44 0.661 1.000E+00 2 68 72 0 67 68 +--CCCCAUAGCUUAAuUGGCAAAGCAGCUCCCUCUUAAGGAGAUGACUCCAGGUUCGAGUCCUGGUGGGG--- +>URS00027511C4 44 0.605 1.000E+00 70 0 72 1 67 68 +UCCAGGGUAGCCUAGU----GGACAUCUGCCCGGUAAGCGGAAUACUGUGGGUUCGAUUCCCAUCCCUGGA- +>URS0002689859 59 0.661 1.000E+00 1 68 72 0 67 68 +-CGGGUGUAGCUCAAUGGUAGAGCGUCAGCCUUCCAAGCUGAAUGUUGACGGUUCGAGUCCGUUCACCC--- +>URS0002F0CA56 66 0.784 1.000E+00 3 67 72 1 65 68 +---CAAGUGGCUCAGUGGUAGAGCAUCGCCUUGCCAAGGCGAGGGUCGCGGGUUCGAAUCCCGUCUUG---- +>URS000341BA9E 61 0.731 1.000E+00 2 68 72 0 66 68 +--CGGAGUAGCUCAGUGGUAGAGCAUCGCAUUCGUAAUGCGAGGGUCGCGGGUUCAAAUCCCGCCUUCG--- +>URS0002F0AFF2 55 0.712 1.000E+00 3 67 72 1 66 68 +---GGAAUAGCUCAGuUGGUAGAGCAUCUGCUUCCCAAGCAGAGGGUCGUGGGUUCGAGUCCCAUUUCC---- +>URS0002F0B130 53 0.712 1.000E+00 3 67 72 1 66 68 +---GUAAUAGCUCAGuUGGUAGAGCAUCAGCUUCCCAAGCUGAGGGUCGCGGGUUCGAGUCCCGUUUAC---- +>URS0002697AB8 57 0.753 1.000E+00 3 67 72 1 65 68 +---GGGGUGGCUCAGUGGUAGAGCGUUGCCUUGCCAAGGCAAAUGUCGCGGGUCCGAAUCCCGUCCUC---- +>URS000340F088 64 0.731 1.000E+00 2 68 72 0 66 68 +--CGACGUGGCUCAGUGGUAGAGCAAUUGAUUCGUAAUCAAUAGGUCGCGGGUUCAAAUCCCGCCGUCG--- +>URS000341C43C 57 0.772 1.000E+00 3 67 72 1 66 68 +---GUGAUAGCUCAGuUGGUAGAGCAUCUGCUUCCCAAGUAGAAGGUCGCGGGUUCAAACCCCGUUCGC---- +>URS00039D24EB 59 0.731 1.000E+00 4 70 72 0 66 68 +----GCUUAGCUCAGUGGUAGAGCGCCUGCCUUACAAGCAGGAUGUCGCUGGUUCGAAACCGGCAGCGGGC- +>URS000274B02D 46 0.619 1.000E+00 70 0 72 1 67 68 +UCCAGGGUAGCCUAGU----GGACAUCUGCCCGGUAAGCGGAACAUUGUGGGUUCGAUUCCCAUCCCUGGA- +>URS000340FE9D 59 0.818 1.000E+00 3 67 72 1 66 68 +---GCGGUAGUUCAGcUGGUAGAACGUCUCCCUUCCAAGGAGAAGGUCGCGGGUUCAAAUCCCGUCCGC---- +>URS0002683142 55 0.764 1.000E+00 2 68 72 0 67 68 +--CGCAAUAGCUCAGUcGGUAGAGCAUUUCAUUCGUAACGAAAAGGUCGCGGGUUCGAGUCCCGCUCGCG--- +>URS0002687E8C 63 0.753 1.000E+00 3 67 72 1 65 68 +---GACGUGGCUCAGCGGUAGAGCAUCGCCUUGCCAAGGCGAGGGUCGCGGGUUCGAUUCCCGUCGUC---- +>URS0003412116 60 0.632 1.000E+00 1 68 72 0 67 68 +-CGGGUGUAGCUCAAUGGUAGAGCACCAGCCUUCCAAGCUGGGUGUUGUGGGUUCGAGUCCCACUACCC--- +>URS000341CD33 62 0.707 1.000E+00 3 67 72 1 65 68 +---CGUAUAGCUCAGCGGUAGAGCACCUGCCUUACAAGCAGGUGGUCGCAGGUUCGAUCCCUGCUGCG---- +>URS0003412161 47 0.716 1.000E+00 3 67 72 0 66 68 +---CGCGUAGCACAGCugGAUAAUGCGUUGGCCUCCUAAGCCGAAGAUCGCGGGUUCGAAUCCUGCCGCG---- +>URS000340576A 56 0.803 1.000E+00 3 67 72 1 66 68 +---GCGGUAGUUCAGcUGGUAGAACGUCUCCUUGCCAUGGAGAAGGUCGCGGGUUCGAAUCCCGUCCGC---- +>URS000269FB76 56 0.742 1.000E+00 3 67 72 1 66 68 +---GGCGUAGCUCAgGUGGUAGAGCAUCAGCUUCCCAAGCUGAGGGUCGCGGGUUCGAGUCCCGUCGCC---- +>URS000269DD5A 54 0.784 1.000E+00 3 67 72 1 65 68 +---GGGGUGGCUCAGUGGUAGAGCGUCACCUUGCCAAGGUGAACGUCGCGGGUCCGAAUCCCGUCCUC---- +>URS0000D7961F 57 0.760 1.000E+00 0 70 72 0 66 68 +GCUUAGGUAGU----UGGUAGUGCGUCUGCCUUGUAAGCGCUAGGCCGCGGGUUCGAUUCCCGCCCGGGGC- +>URS0003417B42 49 0.716 1.000E+00 3 67 72 0 66 68 +---CGGUUAGCUCAGUugGUUAGAGCGCCUCCCUUACAAGGAGGAGGUCACAGGUUCGAGUCCUGUACCG---- +>URS00034058E5 65 0.686 1.000E+00 2 68 72 0 66 68 +--CGGCGUAGCUCAAUGGUAGAGCAACCGCCUUGUAAGCGGUAGGUUAUGGGUUCGAUUCCCAUCGUCG--- +>URS0002695E96 69 0.720 1.000E+00 1 68 72 0 67 68 +-CGGGUGUAGCUCAGCGGUAGAGCGCCUGCCUUCCAAGCAGGUGGUCGUGGGUUCGAUUCCCAUCGCCC--- +>URS0003407089 54 0.772 1.000E+00 3 67 72 1 66 68 +---GGCGUAGCUCAGuUGGUGGAGCGUCUCCUUGCCAAGGAGAAGGUCGCGGGUUCGAGUCCCGUCGCC---- +>URS0002F0BDE8 64 0.716 1.000E+00 2 68 72 0 66 68 +--CAAUGUAGCUCAGGGGUAGAGCACUUCCUUGGUAAGGAAGAGGUCGCGGGUUCAAAUCCCGCCAUUG--- +>URS00030F1F09 67 0.769 1.000E+00 3 67 72 3 67 68 +---UCCGUGGCGCAAUGGUAGCGCGUCUGACUCCAGAUCAGAAGGUUGCGUGUUCGAUUCACGUCGGG---- +>URS0002695732 56 0.757 1.000E+00 3 67 72 1 66 68 +---GCAAUAGCUCAGuUGGUAGAGCGUUGGCUUCCCAAGCCGAAGGUCGCGGGUUCGAGUCCCGUUUGC---- +>URS0003406C82 58 0.723 1.000E+00 3 67 72 1 65 68 +---CUUGUAGCUCAGUGGUAGAGCAGUCGCCUCUUAAGCGAUUGGCCGUGGGUUCGAAUCCCACCGGG---- +>URS00033DD3B1 61 0.712 1.000E+00 2 67 72 2 67 68 +--CUCCGUGGCGCAACGGUAGCGCGUCUGACUCCAGAUCAGAAGGCUGCGUGUUCAAAUCACGUCGGG---- +>URS0003404BAC 60 0.772 1.000E+00 3 67 72 1 66 68 +---GGAGUAGCUCAGuUGGUAGAGCAUCUGCCUUCCAAGCAGAUGGUCGCGGGUUCGAGUCCCGUCUCC---- +>URS00034206C2 64 0.705 1.000E+00 1 68 72 0 67 68 +-CGGGUGUAGCUCAGCGGUAGAGCGUCGGCUUGCCAUGCCGAAGGUCGCGGGUUCAAAUCCCGUCGCCC--- +>URS0003418F6F 46 0.716 1.000E+00 3 67 72 0 66 68 +---CGCGUAGCACAGauGGAUAAUGCGUCGGCCUCCUAAGCCGAAGACCGCGGGUUCGAAUCCUGCCGCG---- +>URS000341C476 65 0.720 1.000E+00 1 68 72 0 67 68 +-CGGGUGUAGCUCAGUGGUAGAGCUUCUGCUUCCCAAGCAGACGGCCGUGGGUUCGAUCCCCAUCGCCC--- +>URS000268F7FF 46 0.712 1.000E+00 3 67 72 1 66 68 +---GCGAUAGUUCAAuUGGUAGAGCCUCAGCCUUCCAAGCUGAUUGUUGCGGGUUCGAGUCCCGUUCGC---- +>URS000341C78A 56 0.661 1.000E+00 1 68 72 0 67 68 +-CGGGUAUAGUUCAGUGGUAGAACGUCACCUUCCCAAGGUGAAUGUCGUGGGUUCGAGUCCCAUUACCC--- +>URS00026837A6 70 0.764 1.000E+00 3 70 72 0 67 68 +---CAUGUGGCUCAGUGGUAGAGCACUCCCUUGGUAAGGGAGAGGUCGCGGGUCCGAUUCCCGCCAUGGGC- +>URS000269AC18 55 0.757 1.000E+00 3 67 72 1 66 68 +---GGCAUAGCUCAGuUGGUAGAGCGUCGGCGUCCCAAGCCGAAGGUCGCGGGUUCGAUUCCCGUUGCC---- +>URS000268BAE7 63 0.753 1.000E+00 2 66 72 0 64 68 +--CUAUGUAGCUCAGGGGUAGAGCGCUUCCUUGGUAAGGAAGAGGUCGCGGGUUCAAUUCCCGCCAU----- +>URS00032BBC9C 65 0.776 1.000E+00 4 70 72 0 66 68 +----CCUUAGCUUAGUGGUAGAGCGUCUGCUUCGUAAGCAGAAGGUCGUCGGUUCAAUCCCGGCAGGUGGC- +>URS0003408FE5 71 0.720 1.000E+00 1 68 72 0 67 68 +-CGGGUGUAGUUCAGUGGUAGAACGUCUGCUUGCCAAGCAGAAGGUCGUGGGUUCGAAUCCCAUCGCCC--- +>URS000268171F 53 0.742 1.000E+00 3 67 72 1 66 68 +---GGCGUAGCUCAGuUGGUAGAGCGUCAGCUUCCCAAGCUGAAAGUCGCGGGUUCGAGCCCCGUCGCC---- +>URS00033D40ED 28 0.626 1.000E+00 2 67 72 0 66 68 +--UUCCGUGGCCGAAUGGaUAAGGCACCGGUCUCCUAAGCCGUGAAUUGCGGGUUCGAGUGCCGCAGGA---- +>URS000340F41E 66 0.735 1.000E+00 1 68 72 0 67 68 +-CGGGUGUAGCUCAGCGGUAGAGCGUCAGCUUCCCAAGCUGAGGGUCGCGGGUUCAAUUCCCGUCACCC--- diff --git a/tests/fixtures/fitness/README.md b/tests/fixtures/fitness/README.md index 9cbbd1c..6059d27 100644 --- a/tests/fixtures/fitness/README.md +++ b/tests/fixtures/fitness/README.md @@ -1,9 +1,4 @@ -These fixtures contain the complete Andreasson 2020 ribozyme, Domingo 2018 tRNA -and Tome 2014 GFP aptamer assays: 11,934 measured variants spanning all three -ncRNA leaderboard categories. The RNA-FM prediction files are copied byte for -byte from the v0.2 prediction release and include all four fill strategies. - -The workflow test merges those released predictions and reproduces the metric -and fill-strategy tables. A separate checkpoint-free adapter exercises context -construction, batching, scoring, validation and manifest output on the complete -Domingo assay. No checkpoint or network access is required. +These fixtures contain 345 real assay rows from all 31 ncRNA assays, with +released predictions for all 21 models. They cover the three RNA categories, +single and multiple mutations, repeated measurements, and EVmutation coverage. +The Domingo alignment contains 1,000 records from the released Riboseek MSA. diff --git a/tests/fixtures/fitness/assays.parquet b/tests/fixtures/fitness/assays.parquet new file mode 100644 index 0000000..c63b397 Binary files /dev/null and b/tests/fixtures/fitness/assays.parquet differ diff --git a/tests/fixtures/fitness/assays/Andreasson_2020_ribozyme.csv b/tests/fixtures/fitness/assays/Andreasson_2020_ribozyme.csv deleted file mode 100644 index 2343a52..0000000 --- a/tests/fixtures/fitness/assays/Andreasson_2020_ribozyme.csv +++ /dev/null @@ -1,7344 +0,0 @@ -mutant,DMS_score,sequence -"C10A,U56C",3.79e-06,AUAAAAGCGACAGAACUACAGAAAUGUAGUUGACGAGGAGGAGGUUUAUCGAAUUCUCGGCGGAUGCCUCCAAA -C58G,0.000194333,AUAAAAGCGCCAGAACUACAGAAAUGUAGUUGACGAGGAGGAGGUUUAUCGAAUUUUGGGCGGAUGCCUCCAAA -"C16A,G29U",0.030542046,AUAAAAGCGCCAGAAAUACAGAAAUGUAUUUGACGAGGAGGAGGUUUAUCGAAUUUUCGGCGGAUGCCUCCAAA -,0.071215256,AUAAAAGCGCCAGAACUACAGAAAUGUAGUUGACGAGGAGGAGGUUUAUCGAAUUUUCGGCGGAUGCCUCCAAA -C10A,8.26e-06,AUAAAAGCGACAGAACUACAGAAAUGUAGUUGACGAGGAGGAGGUUUAUCGAAUUUUCGGCGGAUGCCUCCAAA -C11G,8.07e-06,AUAAAAGCGCGAGAACUACAGAAAUGUAGUUGACGAGGAGGAGGUUUAUCGAAUUUUCGGCGGAUGCCUCCAAA -"G29U,C70A",6.65e-05,AUAAAAGCGCCAGAACUACAGAAAUGUAUUUGACGAGGAGGAGGUUUAUCGAAUUUUCGGCGGAUGCCUACAAA -"G59U,C71U",5.45e-05,AUAAAAGCGCCAGAACUACAGAAAUGUAGUUGACGAGGAGGAGGUUUAUCGAAUUUUCUGCGGAUGCCUCUAAA -"G13A,U55C",0.000581911,AUAAAAGCGCCAAAACUACAGAAAUGUAGUUGACGAGGAGGAGGUUUAUCGAAUCUUCGGCGGAUGCCUCCAAA -A64G,0.047530653,AUAAAAGCGCCAGAACUACAGAAAUGUAGUUGACGAGGAGGAGGUUUAUCGAAUUUUCGGCGGGUGCCUCCAAA -C11A,0.000290861,AUAAAAGCGCAAGAACUACAGAAAUGUAGUUGACGAGGAGGAGGUUUAUCGAAUUUUCGGCGGAUGCCUCCAAA -"A33C,U47A",0.000255834,AUAAAAGCGCCAGAACUACAGAAAUGUAGUUGCCGAGGAGGAGGUUAAUCGAAUUUUCGGCGGAUGCCUCCAAA -"A15U,G32U",0.001043055,AUAAAAGCGCCAGAUCUACAGAAAUGUAGUUUACGAGGAGGAGGUUUAUCGAAUUUUCGGCGGAUGCCUCCAAA -A53U,0.048001386,AUAAAAGCGCCAGAACUACAGAAAUGUAGUUGACGAGGAGGAGGUUUAUCGAUUUUUCGGCGGAUGCCUCCAAA -A15U,0.010350527,AUAAAAGCGCCAGAUCUACAGAAAUGUAGUUGACGAGGAGGAGGUUUAUCGAAUUUUCGGCGGAUGCCUCCAAA -"A15G,C68U",0.003219127,AUAAAAGCGCCAGAGCUACAGAAAUGUAGUUGACGAGGAGGAGGUUUAUCGAAUUUUCGGCGGAUGCUUCCAAA -G60A,2.13e-05,AUAAAAGCGCCAGAACUACAGAAAUGUAGUUGACGAGGAGGAGGUUUAUCGAAUUUUCGACGGAUGCCUCCAAA -G9U,1.44e-06,AUAAAAGCUCCAGAACUACAGAAAUGUAGUUGACGAGGAGGAGGUUUAUCGAAUUUUCGGCGGAUGCCUCCAAA -G38A,3.85e-06,AUAAAAGCGCCAGAACUACAGAAAUGUAGUUGACGAGAAGGAGGUUUAUCGAAUUUUCGGCGGAUGCCUCCAAA -"G29U,G59C",5.85e-06,AUAAAAGCGCCAGAACUACAGAAAUGUAUUUGACGAGGAGGAGGUUUAUCGAAUUUUCCGCGGAUGCCUCCAAA -A48C,0.00045575,AUAAAAGCGCCAGAACUACAGAAAUGUAGUUGACGAGGAGGAGGUUUCUCGAAUUUUCGGCGGAUGCCUCCAAA -C70U,0.019408766,AUAAAAGCGCCAGAACUACAGAAAUGUAGUUGACGAGGAGGAGGUUUAUCGAAUUUUCGGCGGAUGCCUUCAAA -"A33U,C61U",3.34e-06,AUAAAAGCGCCAGAACUACAGAAAUGUAGUUGUCGAGGAGGAGGUUUAUCGAAUUUUCGGUGGAUGCCUCCAAA -C58A,0.000863406,AUAAAAGCGCCAGAACUACAGAAAUGUAGUUGACGAGGAGGAGGUUUAUCGAAUUUUAGGCGGAUGCCUCCAAA -"U56A,C58A",0.001064881,AUAAAAGCGCCAGAACUACAGAAAUGUAGUUGACGAGGAGGAGGUUUAUCGAAUUAUAGGCGGAUGCCUCCAAA -"G7A,A15C",2.43e-06,AUAAAAACGCCAGACCUACAGAAAUGUAGUUGACGAGGAGGAGGUUUAUCGAAUUUUCGGCGGAUGCCUCCAAA -G40U,0.016554984,AUAAAAGCGCCAGAACUACAGAAAUGUAGUUGACGAGGAUGAGGUUUAUCGAAUUUUCGGCGGAUGCCUCCAAA -C70A,0.000609841,AUAAAAGCGCCAGAACUACAGAAAUGUAGUUGACGAGGAGGAGGUUUAUCGAAUUUUCGGCGGAUGCCUACAAA -U55A,0.053303252,AUAAAAGCGCCAGAACUACAGAAAUGUAGUUGACGAGGAGGAGGUUUAUCGAAUAUUCGGCGGAUGCCUCCAAA -"A12G,G41U",0.000762876,AUAAAAGCGCCGGAACUACAGAAAUGUAGUUGACGAGGAGUAGGUUUAUCGAAUUUUCGGCGGAUGCCUCCAAA -A12G,0.001431065,AUAAAAGCGCCGGAACUACAGAAAUGUAGUUGACGAGGAGGAGGUUUAUCGAAUUUUCGGCGGAUGCCUCCAAA -"G41U,A52G",0.000128275,AUAAAAGCGCCAGAACUACAGAAAUGUAGUUGACGAGGAGUAGGUUUAUCGGAUUUUCGGCGGAUGCCUCCAAA -G51U,2.15e-06,AUAAAAGCGCCAGAACUACAGAAAUGUAGUUGACGAGGAGGAGGUUUAUCUAAUUUUCGGCGGAUGCCUCCAAA -G32C,0.000138726,AUAAAAGCGCCAGAACUACAGAAAUGUAGUUCACGAGGAGGAGGUUUAUCGAAUUUUCGGCGGAUGCCUCCAAA -C71U,0.041200776,AUAAAAGCGCCAGAACUACAGAAAUGUAGUUGACGAGGAGGAGGUUUAUCGAAUUUUCGGCGGAUGCCUCUAAA -G44C,0.000200181,AUAAAAGCGCCAGAACUACAGAAAUGUAGUUGACGAGGAGGAGCUUUAUCGAAUUUUCGGCGGAUGCCUCCAAA -"C34G,U69A",3.79e-05,AUAAAAGCGCCAGAACUACAGAAAUGUAGUUGAGGAGGAGGAGGUUUAUCGAAUUUUCGGCGGAUGCCACCAAA -C68G,0.000102358,AUAAAAGCGCCAGAACUACAGAAAUGUAGUUGACGAGGAGGAGGUUUAUCGAAUUUUCGGCGGAUGCGUCCAAA -G32U,0.001576664,AUAAAAGCGCCAGAACUACAGAAAUGUAGUUUACGAGGAGGAGGUUUAUCGAAUUUUCGGCGGAUGCCUCCAAA -G51A,1.01e-05,AUAAAAGCGCCAGAACUACAGAAAUGUAGUUGACGAGGAGGAGGUUUAUCAAAUUUUCGGCGGAUGCCUCCAAA -G37A,0.005642401,AUAAAAGCGCCAGAACUACAGAAAUGUAGUUGACGAAGAGGAGGUUUAUCGAAUUUUCGGCGGAUGCCUCCAAA -"C58A,G66A",0.000321848,AUAAAAGCGCCAGAACUACAGAAAUGUAGUUGACGAGGAGGAGGUUUAUCGAAUUUUAGGCGGAUACCUCCAAA -"C67A,C68A",3.56e-05,AUAAAAGCGCCAGAACUACAGAAAUGUAGUUGACGAGGAGGAGGUUUAUCGAAUUUUCGGCGGAUGAAUCCAAA 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-U3G,-0.339394447,GCGUCUGGACUGCGAUGGGAGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -U4G,-0.108698221,GCUGCUGGACUGCGAUGGGAGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -U4C,-0.904644925,GCUCCUGGACUGCGAUGGGAGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -U4A,0.034393768,GCUACUGGACUGCGAUGGGAGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -C5U,-0.012223188,GCUUUUGGACUGCGAUGGGAGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -C5A,-0.921515823,GCUUAUGGACUGCGAUGGGAGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -C5G,-0.300770921,GCUUGUGGACUGCGAUGGGAGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -U6C,-0.977442795,GCUUCCGGACUGCGAUGGGAGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -U6G,-0.804190413,GCUUCGGGACUGCGAUGGGAGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -U6A,-1.045729938,GCUUCAGGACUGCGAUGGGAGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -G7A,-3.359558925,GCUUCUAGACUGCGAUGGGAGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -G7C,-4.367829406,GCUUCUCGACUGCGAUGGGAGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -G7U,-2.457784717,GCUUCUUGACUGCGAUGGGAGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -G8U,-0.381539929,GCUUCUGUACUGCGAUGGGAGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -G8C,-3.849687277,GCUUCUGCACUGCGAUGGGAGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -G8A,-2.645127091,GCUUCUGAACUGCGAUGGGAGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -A9U,-0.683998099,GCUUCUGGUCUGCGAUGGGAGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -A9G,-1.796673482,GCUUCUGGGCUGCGAUGGGAGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -A9C,-0.905387483,GCUUCUGGCCUGCGAUGGGAGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -C10U,-0.00971028,GCUUCUGGAUUGCGAUGGGAGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -C10G,0.069848799,GCUUCUGGAGUGCGAUGGGAGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -C10A,0.241513745,GCUUCUGGAAUGCGAUGGGAGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -U11C,-2.501475467,GCUUCUGGACCGCGAUGGGAGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -U11A,0.049258555,GCUUCUGGACAGCGAUGGGAGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -U11G,0.163609038,GCUUCUGGACGGCGAUGGGAGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -G12C,-2.839803149,GCUUCUGGACUCCGAUGGGAGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -G12U,-2.147432606,GCUUCUGGACUUCGAUGGGAGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -G12A,-2.389362399,GCUUCUGGACUACGAUGGGAGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -C13U,-0.08626636,GCUUCUGGACUGUGAUGGGAGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -C13A,-1.778026099,GCUUCUGGACUGAGAUGGGAGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -C13G,-0.576837182,GCUUCUGGACUGGGAUGGGAGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -G14C,-2.558059073,GCUUCUGGACUGCCAUGGGAGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -G14A,-0.781239658,GCUUCUGGACUGCAAUGGGAGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -G14U,-0.205354829,GCUUCUGGACUGCUAUGGGAGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -A15U,-1.277632993,GCUUCUGGACUGCGUUGGGAGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -A15G,-0.156866479,GCUUCUGGACUGCGGUGGGAGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -A15C,-3.95577174,GCUUCUGGACUGCGCUGGGAGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -U16G,0.665297418,GCUUCUGGACUGCGAGGGGAGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -U16C,-0.67321046,GCUUCUGGACUGCGACGGGAGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -U16A,-0.33325763,GCUUCUGGACUGCGAAGGGAGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -G17C,-4.871929545,GCUUCUGGACUGCGAUCGGAGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -G17U,-0.442460732,GCUUCUGGACUGCGAUUGGAGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -G17A,-4.871929545,GCUUCUGGACUGCGAUAGGAGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -G18U,-4.871929545,GCUUCUGGACUGCGAUGUGAGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -G18C,-4.871929545,GCUUCUGGACUGCGAUGCGAGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -G18A,-4.871929545,GCUUCUGGACUGCGAUGAGAGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -G19U,-4.871929545,GCUUCUGGACUGCGAUGGUAGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -G19A,-3.49963973,GCUUCUGGACUGCGAUGGAAGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -G19C,-4.871929545,GCUUCUGGACUGCGAUGGCAGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -A20G,-0.956461242,GCUUCUGGACUGCGAUGGGGGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -A20C,-4.871929545,GCUUCUGGACUGCGAUGGGCGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -A20U,-4.871929545,GCUUCUGGACUGCGAUGGGUGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -G21A,-4.871929545,GCUUCUGGACUGCGAUGGGAACACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -G21U,-0.490889531,GCUUCUGGACUGCGAUGGGAUCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -G21C,-0.264127371,GCUUCUGGACUGCGAUGGGACCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -C22U,-1.165720067,GCUUCUGGACUGCGAUGGGAGUACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -C22A,-1.689478611,GCUUCUGGACUGCGAUGGGAGAACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -C22G,-2.767407123,GCUUCUGGACUGCGAUGGGAGGACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -A23C,-1.633108168,GCUUCUGGACUGCGAUGGGAGCCCGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -A23G,-0.460176297,GCUUCUGGACUGCGAUGGGAGCGCGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -A23U,-1.395564801,GCUUCUGGACUGCGAUGGGAGCUCGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -C24A,-1.312771454,GCUUCUGGACUGCGAUGGGAGCAAGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -C24G,-1.623644528,GCUUCUGGACUGCGAUGGGAGCAGGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -C24U,-0.175957802,GCUUCUGGACUGCGAUGGGAGCAUGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -G25C,-0.713945984,GCUUCUGGACUGCGAUGGGAGCACCAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -G25U,0.142240816,GCUUCUGGACUGCGAUGGGAGCACUAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -G25A,-0.226636439,GCUUCUGGACUGCGAUGGGAGCACAAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -A26C,-0.086835493,GCUUCUGGACUGCGAUGGGAGCACGCAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -A26G,0.01580469,GCUUCUGGACUGCGAUGGGAGCACGGAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -A26U,-0.021077146,GCUUCUGGACUGCGAUGGGAGCACGUAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -A27U,0.101985682,GCUUCUGGACUGCGAUGGGAGCACGAUACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -A27C,-0.066698689,GCUUCUGGACUGCGAUGGGAGCACGACACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -A27G,-0.003447248,GCUUCUGGACUGCGAUGGGAGCACGAGACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -A28U,-0.063726609,GCUUCUGGACUGCGAUGGGAGCACGAAUCGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -A28G,-0.091041642,GCUUCUGGACUGCGAUGGGAGCACGAAGCGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -A28C,-0.696003023,GCUUCUGGACUGCGAUGGGAGCACGAACCGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -C29U,-0.073380353,GCUUCUGGACUGCGAUGGGAGCACGAAAUGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -C29G,0.062657764,GCUUCUGGACUGCGAUGGGAGCACGAAAGGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -C29A,0.360734809,GCUUCUGGACUGCGAUGGGAGCACGAAAAGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -G30A,-0.032790702,GCUUCUGGACUGCGAUGGGAGCACGAAACAUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -G30U,0.113576979,GCUUCUGGACUGCGAUGGGAGCACGAAACUUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -G30C,0.022067693,GCUUCUGGACUGCGAUGGGAGCACGAAACCUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -U31G,0.096637897,GCUUCUGGACUGCGAUGGGAGCACGAAACGGCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -U31A,0.037852772,GCUUCUGGACUGCGAUGGGAGCACGAAACGACGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -U31C,-0.012625714,GCUUCUGGACUGCGAUGGGAGCACGAAACGCCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -C32U,0.001420662,GCUUCUGGACUGCGAUGGGAGCACGAAACGUUGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -C32A,0.395941367,GCUUCUGGACUGCGAUGGGAGCACGAAACGUAGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -C32G,-0.212147101,GCUUCUGGACUGCGAUGGGAGCACGAAACGUGGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -G33U,-3.041310431,GCUUCUGGACUGCGAUGGGAGCACGAAACGUCUUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -G33C,-4.871929545,GCUUCUGGACUGCGAUGGGAGCACGAAACGUCCUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -G33A,-1.447732034,GCUUCUGGACUGCGAUGGGAGCACGAAACGUCAUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -U34G,-0.494125398,GCUUCUGGACUGCGAUGGGAGCACGAAACGUCGGGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -U34A,-2.840511887,GCUUCUGGACUGCGAUGGGAGCACGAAACGUCGAGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -U34C,-2.503419565,GCUUCUGGACUGCGAUGGGAGCACGAAACGUCGCGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -G35A,-4.871929545,GCUUCUGGACUGCGAUGGGAGCACGAAACGUCGUAGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -G35C,-4.871929545,GCUUCUGGACUGCGAUGGGAGCACGAAACGUCGUCGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -G35U,-4.871929545,GCUUCUGGACUGCGAUGGGAGCACGAAACGUCGUUGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -G36C,-4.871929545,GCUUCUGGACUGCGAUGGGAGCACGAAACGUCGUGCCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -G36A,-3.890618074,GCUUCUGGACUGCGAUGGGAGCACGAAACGUCGUGACGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -G36U,-4.871929545,GCUUCUGGACUGCGAUGGGAGCACGAAACGUCGUGUCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -C37A,-0.243493974,GCUUCUGGACUGCGAUGGGAGCACGAAACGUCGUGGAGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -C37G,-1.859456954,GCUUCUGGACUGCGAUGGGAGCACGAAACGUCGUGGGGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -C37U,-2.158672091,GCUUCUGGACUGCGAUGGGAGCACGAAACGUCGUGGUGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -G38U,-1.767881639,GCUUCUGGACUGCGAUGGGAGCACGAAACGUCGUGGCUCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -G38C,-4.289105928,GCUUCUGGACUGCGAUGGGAGCACGAAACGUCGUGGCCCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -G38A,-2.712640384,GCUUCUGGACUGCGAUGGGAGCACGAAACGUCGUGGCACAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -C39U,-1.271995635,GCUUCUGGACUGCGAUGGGAGCACGAAACGUCGUGGCGUAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC -C39G,-1.107515167,GCUUCUGGACUGCGAUGGGAGCACGAAACGUCGUGGCGGAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC 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-"U47C,A79G",-3.862013558,GCUUCUGGACUGCGAUGGGAGCACGAAACGUCGUGGCGCAAUUGGGCGGGGAAAGUCCUUAAAAGAGGGCCACCACAGGAGC diff --git a/tests/fixtures/fitness/comparison.csv b/tests/fixtures/fitness/comparison.csv new file mode 100644 index 0000000..3002dd1 --- /dev/null +++ b/tests/fixtures/fitness/comparison.csv @@ -0,0 +1,22 @@ +model,Ribozyme,tRNA,Aptamer,macro_3ncRNA +EVmutation,0.38803385471789953,0.34682539682539687,0.4285714285714286,0.38781022670490833 +RNAErnie,0.2838887790583834,0.5333333333333333,0.2,0.3390740374639056 +RNA-FM,0.1387348644403504,0.553968253968254,-0.08571428571428573,0.20232961089810622 +rnagenesis,0.2273473632208916,0.5158730158730158,-0.48571428571428577,0.08583536445987389 +evo2,0.18810746366135692,0.5111111111111112,-0.5428571428571429,0.05212047730510839 +evo2_40b,0.1531902652060158,0.5134920634920636,-0.6571428571428573,0.003179823851740704 +aido_rna_650m,0.18428291774193675,0.5349206349206349,-0.7142857142857143,0.0016392794589524529 +evo2_1b_base,0.10105116046836475,0.46031746031746035,-0.6,-0.01287712640472496 +aido_rna_300m,0.04677568628350528,0.4825396825396826,-0.6,-0.023561543725604032 +aido_rna_25m,0.029718757472470644,0.5420634920634921,-0.6571428571428573,-0.028453535868964822 +GenSLM,-0.11068447207409955,0.2563492063492064,-0.2571428571428572,-0.03715937428925011 +evo2_20b,0.17045713190085784,0.5325396825396825,-0.8285714285714287,-0.04185820471029612 +ntv3_8m,0.0777421262248864,0.5420634920634921,-0.7714285714285715,-0.05054098438006431 +rinalmo,0.04906741431241585,0.6111111111111112,-0.8285714285714287,-0.05613096771596724 +aido_rna,0.18678636933336215,0.4714285714285715,-0.8285714285714287,-0.05678549593649837 +evo1,0.028637017087926556,-0.13412698412698412,-0.08571428571428573,-0.0637347509177811 +evo1.5,0.06708342941159834,0.5301587301587302,-0.8285714285714287,-0.07710975633370008 +ntv3_100m,0.04717892204829612,0.5468253968253969,-0.8857142857142858,-0.09723665561353093 +ntv3_650m,0.025526568323441565,0.36428571428571427,-0.7714285714285715,-0.12720542960647188 +aido_rna_1m,0.02171010303937972,0.15873015873015875,-0.6571428571428573,-0.1589008651244396 +orthrus,-0.047247869435770286,-0.2896825396825397,-0.48571428571428577,-0.2742148982775319 diff --git a/tests/fixtures/fitness/coverage.csv b/tests/fixtures/fitness/coverage.csv new file mode 100644 index 0000000..19d9d7e --- /dev/null +++ b/tests/fixtures/fitness/coverage.csv @@ -0,0 +1,32 @@ +DMS_ID,RNA_TYPE,variants,scored_variants,evaluated_variants +Andreasson_2020_ribozyme,Ribozyme,12,6,6 +Beck_2022_ribozyme,Ribozyme,12,6,6 +Domingo_2018_tRNA,tRNA,9,9,9 +Guy_2014_tRNA,tRNA,12,6,6 +Janzen_2022_fam1a1_ribozyme,Ribozyme,6,6,6 +Janzen_2022_fam1b1_ribozyme,Ribozyme,6,6,6 +Janzen_2022_fam21_ribozyme,Ribozyme,12,6,6 +Janzen_2022_fam22_ribozyme,Ribozyme,6,6,6 +Janzen_2022_fam31_ribozyme,Ribozyme,6,6,6 +Kobori_2015_ribozyme_j12,Ribozyme,6,6,6 +Kobori_2015_ribozyme_p4,Ribozyme,6,6,6 +Kobori_2015_ribozyme_tw,Ribozyme,6,6,6 +Kobori_2018_ribozyme,Ribozyme,12,6,6 +Li_2016_tRNA,tRNA,6,6,6 +Peri_2022_ribozyme,Ribozyme,12,6,6 +Pitt_2010_ribozyme,Ribozyme,53,50,50 +Roberts_2023_cepeb3_ribozyme,Ribozyme,12,6,6 +Roberts_2023_HDV_ribozyme,Ribozyme,12,6,6 +Roberts_2023_hh_ribozyme,Ribozyme,6,6,6 +Roberts_2023_hp_ribozyme,Ribozyme,12,6,6 +Roberts_2023_tw_ribozyme,Ribozyme,12,6,6 +Soo_2021_ribozyme,Ribozyme,6,0,0 +Tome_2014_GFP_aptamer,Aptamer,6,0,0 +Tome_2014_NELFE_aptamer,Aptamer,12,6,6 +Zhang_2020_cpeb3_ribozyme,Ribozyme,12,6,6 +Zhang_2024_line1_mini_ribozyme,Ribozyme,12,6,6 +Zhang_2024_line1_full_ribozyme,Ribozyme,12,6,6 +Zhang_2024_OR4K15_ribozyme,Ribozyme,12,6,6 +McRae_2024_5tu_ribozyme,Ribozyme,12,6,6 +McRae_2024_t1_ribozyme,Ribozyme,12,6,6 +Kobori_2016_osa_ribozyme,Ribozyme,12,6,6 diff --git a/tests/fixtures/fitness/expected/assay_level_results.csv b/tests/fixtures/fitness/expected/assay_level_results.csv deleted file mode 100644 index 8fef831..0000000 --- a/tests/fixtures/fitness/expected/assay_level_results.csv +++ /dev/null @@ -1,13 +0,0 @@ -DMS_ID,RNA_TYPE,Model,Spearman,AUC,MCC -Andreasson_2020_ribozyme,Ribozyme,rna_fm_wt_fill_score,-0.010196816450093174,0.48026767998583586,-0.03241623535821302 -Andreasson_2020_ribozyme,Ribozyme,rna_fm_mask_fill_score,-0.022613218381173875,0.4729296544934155,-0.03840915281939526 -Andreasson_2020_ribozyme,Ribozyme,rna_fm_mut_fill_score,-0.03267474052834986,0.46639578499672124,-0.055298283846363386 -Andreasson_2020_ribozyme,Ribozyme,rna_fm_match_fill_score,-0.021225465453882437,0.471009089255676,-0.046036502315445385 -Domingo_2018_tRNA,tRNA,rna_fm_wt_fill_score,0.3964526357624622,0.6939042457688263,0.293892175059252 -Domingo_2018_tRNA,tRNA,rna_fm_mask_fill_score,0.33118894887059486,0.6589978649071886,0.2402394773704028 -Domingo_2018_tRNA,tRNA,rna_fm_mut_fill_score,0.22558004342256452,0.5976630555509659,0.11760473979589027 -Domingo_2018_tRNA,tRNA,rna_fm_match_fill_score,0.3887556583873049,0.6735045171073623,0.23832330959580106 -Tome_2014_GFP_aptamer,Aptamer,rna_fm_wt_fill_score,-0.021634275727798857,0.48555391976444606,-0.04556956201693044 -Tome_2014_GFP_aptamer,Aptamer,rna_fm_mask_fill_score,-0.011398747140662652,0.4886593669488406,-0.04556956201693044 -Tome_2014_GFP_aptamer,Aptamer,rna_fm_mut_fill_score,-0.015154787465720219,0.48750920132499076,-0.0263847994111152 -Tome_2014_GFP_aptamer,Aptamer,rna_fm_match_fill_score,-0.012520244789621967,0.48748619801251386,-0.01679241810820758 diff --git a/tests/fixtures/fitness/expected/assay_level_results_transposed.csv b/tests/fixtures/fitness/expected/assay_level_results_transposed.csv deleted file mode 100644 index ef43c42..0000000 --- a/tests/fixtures/fitness/expected/assay_level_results_transposed.csv +++ /dev/null @@ -1,4 +0,0 @@ -DMS_ID,RNA_TYPE,Spearman_rna_fm_wt_fill_score,Spearman_rna_fm_mask_fill_score,Spearman_rna_fm_mut_fill_score,Spearman_rna_fm_match_fill_score,AUC_rna_fm_wt_fill_score,AUC_rna_fm_mask_fill_score,AUC_rna_fm_mut_fill_score,AUC_rna_fm_match_fill_score,MCC_rna_fm_wt_fill_score,MCC_rna_fm_mask_fill_score,MCC_rna_fm_mut_fill_score,MCC_rna_fm_match_fill_score -Andreasson_2020_ribozyme,Ribozyme,-0.010196816450093174,-0.022613218381173875,-0.03267474052834986,-0.021225465453882437,0.48026767998583586,0.4729296544934155,0.46639578499672124,0.471009089255676,-0.03241623535821302,-0.03840915281939526,-0.055298283846363386,-0.046036502315445385 -Domingo_2018_tRNA,tRNA,0.3964526357624622,0.33118894887059486,0.22558004342256452,0.3887556583873049,0.6939042457688263,0.6589978649071886,0.5976630555509659,0.6735045171073623,0.293892175059252,0.2402394773704028,0.11760473979589027,0.23832330959580106 -Tome_2014_GFP_aptamer,Aptamer,-0.021634275727798857,-0.011398747140662652,-0.015154787465720219,-0.012520244789621967,0.48555391976444606,0.4886593669488406,0.48750920132499076,0.48748619801251386,-0.04556956201693044,-0.04556956201693044,-0.0263847994111152,-0.01679241810820758 diff --git a/tests/fixtures/fitness/expected/fill_strategy_macro.csv b/tests/fixtures/fitness/expected/fill_strategy_macro.csv deleted file mode 100644 index 1617241..0000000 --- a/tests/fixtures/fitness/expected/fill_strategy_macro.csv +++ /dev/null @@ -1,5 +0,0 @@ -model,strategy,Ribozyme,tRNA,Aptamer,macro_3ncRNA -rna_fm,mask_fill,-0.022613218381173875,0.33118894887059486,-0.011398747140662652,0.09905899444958612 -rna_fm,match_fill,-0.021225465453882437,0.3887556583873049,-0.012520244789621967,0.11833664938126683 -rna_fm,mut_fill,-0.03267474052834986,0.22558004342256452,-0.015154787465720219,0.059250171809498146 -rna_fm,wt_fill,-0.010196816450093174,0.3964526357624622,-0.021634275727798857,0.12154051452819005 diff --git a/tests/fixtures/fitness/expected/fill_strategy_per_assay.csv b/tests/fixtures/fitness/expected/fill_strategy_per_assay.csv deleted file mode 100644 index b89f130..0000000 --- a/tests/fixtures/fitness/expected/fill_strategy_per_assay.csv +++ /dev/null @@ -1,13 +0,0 @@ -model,strategy,assay,RNA_TYPE,n,spearman -rna_fm,wt_fill,Andreasson_2020_ribozyme,Ribozyme,7342,-0.010196816450093174 -rna_fm,mask_fill,Andreasson_2020_ribozyme,Ribozyme,7342,-0.022613218381173875 -rna_fm,mut_fill,Andreasson_2020_ribozyme,Ribozyme,7342,-0.03267474052834986 -rna_fm,match_fill,Andreasson_2020_ribozyme,Ribozyme,7342,-0.021225465453882437 -rna_fm,wt_fill,Domingo_2018_tRNA,tRNA,4175,0.3964526357624622 -rna_fm,mask_fill,Domingo_2018_tRNA,tRNA,4175,0.33118894887059486 -rna_fm,mut_fill,Domingo_2018_tRNA,tRNA,4175,0.22558004342256452 -rna_fm,match_fill,Domingo_2018_tRNA,tRNA,4175,0.3887556583873049 -rna_fm,wt_fill,Tome_2014_GFP_aptamer,Aptamer,417,-0.021634275727798857 -rna_fm,mask_fill,Tome_2014_GFP_aptamer,Aptamer,417,-0.011398747140662652 -rna_fm,mut_fill,Tome_2014_GFP_aptamer,Aptamer,417,-0.015154787465720219 -rna_fm,match_fill,Tome_2014_GFP_aptamer,Aptamer,417,-0.012520244789621967 diff --git a/tests/fixtures/fitness/expected/results_by_mutation_depth.csv b/tests/fixtures/fitness/expected/results_by_mutation_depth.csv deleted file mode 100644 index 52a6dcd..0000000 --- a/tests/fixtures/fitness/expected/results_by_mutation_depth.csv +++ /dev/null @@ -1,13 +0,0 @@ -Depth,Model,Spearman_Mean,AUC_Mean,MCC_Mean -All,rna_fm_wt_fill_score,0.12422703214420688,0.5708434982276578,0.1312920554507489 -All,rna_fm_mask_fill_score,0.11457448060776584,0.5633084287459073,0.12546392504264875 -All,rna_fm_mut_fill_score,0.09397858839506178,0.5509028880188689,0.09846581343617702 -All,rna_fm_match_fill_score,0.12373880970470338,0.5684302790945269,0.12670242933602513 -multiple,rna_fm_wt_fill_score,0.11591421983697281,0.5545565080758825,0.08158075505425261 -multiple,rna_fm_mask_fill_score,0.09660911676409074,0.5394863691123816,0.06992449423805226 -multiple,rna_fm_mut_fill_score,0.0554173323386826,0.5146752876583048,0.015928271025108826 -multiple,rna_fm_match_fill_score,0.11493777495796582,0.5497300698096209,0.07240150282480502 -single,rna_fm_wt_fill_score,0.13253984445144096,0.5871304883794329,0.18100335584724522 -single,rna_fm_mask_fill_score,0.13253984445144096,0.5871304883794329,0.18100335584724522 -single,rna_fm_mut_fill_score,0.13253984445144096,0.5871304883794329,0.18100335584724522 -single,rna_fm_match_fill_score,0.13253984445144096,0.5871304883794329,0.18100335584724522 diff --git a/tests/fixtures/fitness/expected/results_by_rna_type.csv b/tests/fixtures/fitness/expected/results_by_rna_type.csv deleted file mode 100644 index f79fba1..0000000 --- a/tests/fixtures/fitness/expected/results_by_rna_type.csv +++ /dev/null @@ -1,5 +0,0 @@ -Model,Spearman_tRNA_Mean,Spearman_Aptamer_Mean,Spearman_Ribozyme_Mean,Spearman_All_Mean,AUC_tRNA_Mean,AUC_Aptamer_Mean,AUC_Ribozyme_Mean,AUC_All_Mean,MCC_tRNA_Mean,MCC_Aptamer_Mean,MCC_Ribozyme_Mean,MCC_All_Mean -rna_fm_wt_fill_score,0.3964526357624622,-0.021634275727798857,-0.010196816450093174,0.12154051452819005,0.6939042457688263,0.48555391976444606,0.48026767998583586,0.5532419485063694,0.293892175059252,-0.04556956201693044,-0.03241623535821302,0.07196879256136952 -rna_fm_mask_fill_score,0.33118894887059486,-0.011398747140662652,-0.022613218381173875,0.09905899444958612,0.6589978649071886,0.4886593669488406,0.4729296544934155,0.5401956287831483,0.2402394773704028,-0.04556956201693044,-0.03840915281939526,0.05208692084469237 -rna_fm_mut_fill_score,0.22558004342256452,-0.015154787465720219,-0.03267474052834986,0.05925017180949815,0.5976630555509659,0.48750920132499076,0.46639578499672124,0.5171893472908926,0.11760473979589027,-0.0263847994111152,-0.055298283846363386,0.011973885512803895 -rna_fm_match_fill_score,0.3887556583873049,-0.012520244789621967,-0.021225465453882437,0.11833664938126683,0.6735045171073623,0.48748619801251386,0.471009089255676,0.5439999347918507,0.23832330959580106,-0.01679241810820758,-0.046036502315445385,0.058498129724049365 diff --git a/tests/fixtures/fitness/expected/results_by_rna_type_and_depth.csv b/tests/fixtures/fitness/expected/results_by_rna_type_and_depth.csv deleted file mode 100644 index ab14f43..0000000 --- a/tests/fixtures/fitness/expected/results_by_rna_type_and_depth.csv +++ /dev/null @@ -1,49 +0,0 @@ -Model,Depth,RNA_TYPE,Spearman_Mean,AUC_Mean,MCC_Mean -rna_fm_wt_fill_score,single,tRNA,0.47252747252747257,0.8571428571428571,0.7142857142857143 -rna_fm_wt_fill_score,single,Aptamer,-0.09395878014390537,0.4309106578569377,-0.11864406779661017 -rna_fm_wt_fill_score,single,Ribozyme,0.019050840970755677,0.47333795013850416,-0.05263157894736842 -rna_fm_wt_fill_score,single,All,0.13253984445144096,0.587130488379433,0.18100335584724525 -rna_fm_wt_fill_score,multiple,tRNA,0.3908687544258433,0.6914556611096736,0.29055511403541195 -rna_fm_wt_fill_score,multiple,Aptamer,-0.03195312974318499,0.48913308913308917,-0.016605616605616606 -rna_fm_wt_fill_score,multiple,Ribozyme,-0.011172965171739886,0.48308077398488475,-0.02920723226703755 -rna_fm_wt_fill_score,multiple,All,0.11591421983697281,0.5545565080758825,0.0815807550542526 -rna_fm_wt_fill_score,overall,tRNA,0.3964526357624622,0.6939042457688263,0.293892175059252 -rna_fm_wt_fill_score,overall,Aptamer,-0.021634275727798857,0.48555391976444606,-0.04556956201693044 -rna_fm_wt_fill_score,overall,Ribozyme,-0.010196816450093174,0.48026767998583586,-0.03241623535821302 -rna_fm_wt_fill_score,overall,All,0.12154051452819005,0.5532419485063694,0.07196879256136952 -rna_fm_mask_fill_score,single,tRNA,0.47252747252747257,0.8571428571428571,0.7142857142857143 -rna_fm_mask_fill_score,single,Aptamer,-0.09395878014390537,0.4309106578569377,-0.11864406779661017 -rna_fm_mask_fill_score,single,Ribozyme,0.019050840970755677,0.47333795013850416,-0.05263157894736842 -rna_fm_mask_fill_score,single,All,0.13253984445144096,0.587130488379433,0.18100335584724525 -rna_fm_mask_fill_score,multiple,tRNA,0.32603491074997626,0.6580714246109489,0.2396058200569253 -rna_fm_mask_fill_score,multiple,Aptamer,-0.011887559953858293,0.484981684981685,0.005494505494505495 -rna_fm_mask_fill_score,multiple,Ribozyme,-0.024320000503845682,0.4754059977445107,-0.03532684283727399 -rna_fm_mask_fill_score,multiple,All,0.09660911676409074,0.5394863691123816,0.06992449423805226 -rna_fm_mask_fill_score,overall,tRNA,0.33118894887059486,0.6589978649071886,0.2402394773704028 -rna_fm_mask_fill_score,overall,Aptamer,-0.011398747140662652,0.4886593669488406,-0.04556956201693044 -rna_fm_mask_fill_score,overall,Ribozyme,-0.022613218381173875,0.4729296544934155,-0.03840915281939526 -rna_fm_mask_fill_score,overall,All,0.09905899444958612,0.5401956287831483,0.05208692084469237 -rna_fm_mut_fill_score,single,tRNA,0.47252747252747257,0.8571428571428571,0.7142857142857143 -rna_fm_mut_fill_score,single,Aptamer,-0.09395878014390537,0.4309106578569377,-0.11864406779661017 -rna_fm_mut_fill_score,single,Ribozyme,0.019050840970755677,0.47333795013850416,-0.05263157894736842 -rna_fm_mut_fill_score,single,All,0.13253984445144096,0.587130488379433,0.18100335584724525 -rna_fm_mut_fill_score,multiple,tRNA,0.22308016896177343,0.5975924573984401,0.11751977599526854 -rna_fm_mut_fill_score,multiple,Aptamer,-0.021878857790459998,0.478021978021978,-0.016605616605616606 -rna_fm_mut_fill_score,multiple,Ribozyme,-0.0349493141552656,0.46841142755449633,-0.053129346314325455 -rna_fm_mut_fill_score,multiple,All,0.0554173323386826,0.5146752876583048,0.01592827102510883 -rna_fm_mut_fill_score,overall,tRNA,0.22558004342256452,0.5976630555509659,0.11760473979589027 -rna_fm_mut_fill_score,overall,Aptamer,-0.015154787465720219,0.48750920132499076,-0.0263847994111152 -rna_fm_mut_fill_score,overall,Ribozyme,-0.03267474052834986,0.46639578499672124,-0.055298283846363386 -rna_fm_mut_fill_score,overall,All,0.05925017180949815,0.5171893472908926,0.011973885512803895 -rna_fm_match_fill_score,single,tRNA,0.47252747252747257,0.8571428571428571,0.7142857142857143 -rna_fm_match_fill_score,single,Aptamer,-0.09395878014390537,0.4309106578569377,-0.11864406779661017 -rna_fm_match_fill_score,single,Ribozyme,0.019050840970755677,0.47333795013850416,-0.05263157894736842 -rna_fm_match_fill_score,single,All,0.13253984445144096,0.587130488379433,0.18100335584724525 -rna_fm_match_fill_score,multiple,tRNA,0.38299159469897953,0.6705540975122908,0.23383797545558718 -rna_fm_match_fill_score,multiple,Aptamer,-0.01546151822394916,0.5052503052503052,0.027594627594627594 -rna_fm_match_fill_score,multiple,Ribozyme,-0.022716751601132876,0.47338580666626684,-0.04422809457579972 -rna_fm_match_fill_score,multiple,All,0.11493777495796582,0.5497300698096209,0.07240150282480501 -rna_fm_match_fill_score,overall,tRNA,0.3887556583873049,0.6735045171073623,0.23832330959580106 -rna_fm_match_fill_score,overall,Aptamer,-0.012520244789621967,0.48748619801251386,-0.01679241810820758 -rna_fm_match_fill_score,overall,Ribozyme,-0.021225465453882437,0.471009089255676,-0.046036502315445385 -rna_fm_match_fill_score,overall,All,0.11833664938126683,0.5439999347918507,0.058498129724049365 diff --git a/tests/fixtures/fitness/leaderboard.csv b/tests/fixtures/fitness/leaderboard.csv new file mode 100644 index 0000000..b25d643 --- /dev/null +++ b/tests/fixtures/fitness/leaderboard.csv @@ -0,0 +1,22 @@ +model,Ribozyme,tRNA,Aptamer,macro_3ncRNA +EVmutation,0.38803385471789953,0.34682539682539687,0.4285714285714286,0.38781022670490833 +RNAErnie,0.2929111088525174,0.6780552780552781,-0.24805194805194808,0.2409714796186158 +evo2,0.15432907713725985,0.5742479742479744,-0.18801198801198804,0.18018835445774872 +evo2_20b,0.1801174310549459,0.6935786435786436,-0.524975024975025,0.11624034988618817 +aido_rna,0.2463473013649663,0.5695304695304696,-0.4781840862455453,0.11256456154996353 +evo2_40b,0.15872747696951728,0.6795926295926297,-0.510989010989011,0.10911036519104533 +aido_rna_650m,0.22754912512594863,0.626029526029526,-0.6270746392064516,0.07550133731634102 +evo2_1b_base,0.06669243230896164,0.5257853257853258,-0.37512487512487513,0.0724509609898041 +evo1.5,0.014843153666192977,0.651970251970252,-0.4637362637362638,0.06769238063339374 +RNA-FM,0.06440809102310532,0.6512709512709514,-0.5132867132867133,0.06746410966911447 +rnagenesis,0.20108796028705583,0.6283605283605284,-0.6420579420579422,0.062463515529880664 +rinalmo,0.007468885956044829,0.6586635586635586,-0.5073926073926075,0.05291327907566532 +ntv3_8m,0.011077814108533382,0.49983904983904986,-0.5955044955044956,-0.028195877185637444 +aido_rna_300m,-0.004306273023248884,0.5751803751803752,-0.6584415584415585,-0.029189152094810714 +aido_rna_25m,-0.08831364963009326,0.6222499722499724,-0.7003996003996005,-0.05548775925990713 +GenSLM,-0.04374331727716172,0.06494061494061493,-0.21718281718281723,-0.06532850650645468 +ntv3_100m,-0.00574326370675043,0.45758130758130766,-0.6911088911088912,-0.07975694907811132 +evo1,-0.0714075407682149,-0.11128316128316132,-0.2434565434565435,-0.14204908183597323 +ntv3_650m,-0.0268707327493925,0.42742257742257744,-0.8414585414585416,-0.14696889892845222 +aido_rna_1m,-0.005123115674360549,0.12862692862692865,-0.6234765234765235,-0.1666575701746518 +orthrus,-0.05445873387875957,-0.23420468420468424,-0.41938061938061943,-0.23601467915468774 diff --git a/tests/fixtures/fitness/leaderboard.md b/tests/fixtures/fitness/leaderboard.md new file mode 100644 index 0000000..611c1f2 --- /dev/null +++ b/tests/fixtures/fitness/leaderboard.md @@ -0,0 +1,32 @@ +# Fitness leaderboard + + +| Rank | Model | Ribozyme (n=26) | tRNA (n=3) | Aptamer (n=2) | Macro (3 ncRNA) | +|---:|:--|--:|--:|--:|--:| +| 1 | EVmutation* | 0.3880 | 0.3468 | 0.4286 | 0.3878 | +| 2 | RNA-ERNIE | 0.2929 | 0.6781 | -0.2481 | 0.2410 | +| 3 | Evo 2 (7B) | 0.1543 | 0.5742 | -0.1880 | 0.1802 | +| 4 | Evo 2 (20B) | 0.1801 | 0.6936 | -0.5250 | 0.1162 | +| 5 | AIDO.RNA (1.6B) | 0.2463 | 0.5695 | -0.4782 | 0.1126 | +| 6 | Evo 2 (40B) | 0.1587 | 0.6796 | -0.5110 | 0.1091 | +| 7 | AIDO.RNA (650M) | 0.2275 | 0.6260 | -0.6271 | 0.0755 | +| 8 | Evo 2 (1B base) | 0.0667 | 0.5258 | -0.3751 | 0.0725 | +| 9 | Evo 1.5 | 0.0148 | 0.6520 | -0.4637 | 0.0677 | +| 10 | RNA-FM | 0.0644 | 0.6513 | -0.5133 | 0.0675 | +| 11 | RNAGenesis | 0.2011 | 0.6284 | -0.6421 | 0.0625 | +| 12 | RiNALMo | 0.0075 | 0.6587 | -0.5074 | 0.0529 | +| 13 | Nucleotide Transformer v3 (8M) | 0.0111 | 0.4998 | -0.5955 | -0.0282 | +| 14 | AIDO.RNA (300M) | -0.0043 | 0.5752 | -0.6584 | -0.0292 | +| 15 | AIDO.RNA (25M) | -0.0883 | 0.6222 | -0.7004 | -0.0555 | +| 16 | GenSLM (2.5B) | -0.0437 | 0.0649 | -0.2172 | -0.0653 | +| 17 | Nucleotide Transformer v3 (100M) | -0.0057 | 0.4576 | -0.6911 | -0.0798 | +| 18 | Evo 1 | -0.0714 | -0.1113 | -0.2435 | -0.1420 | +| 19 | Nucleotide Transformer v3 (650M) | -0.0269 | 0.4274 | -0.8415 | -0.1470 | +| 20 | AIDO.RNA (1M) | -0.0051 | 0.1286 | -0.6235 | -0.1667 | +| 21 | Orthrus | -0.0545 | -0.2342 | -0.4194 | -0.2360 | + +\* EVmutation scores 29/31 assays and 221/344 variants (64.2%). Its row uses those variants. [Coverage by assay][5]. [Compare all models on those variants][6]. + + +[5]: evmutation_coverage.csv +[6]: leaderboard_evmutation.csv diff --git a/tests/fixtures/fitness/predictions.parquet b/tests/fixtures/fitness/predictions.parquet new file mode 100644 index 0000000..a7a95b3 Binary files /dev/null and b/tests/fixtures/fitness/predictions.parquet differ diff --git a/tests/fixtures/fitness/predictions/rna_fm_4fill/Andreasson_2020_ribozyme.csv b/tests/fixtures/fitness/predictions/rna_fm_4fill/Andreasson_2020_ribozyme.csv deleted file mode 100644 index 6d8d73c..0000000 --- a/tests/fixtures/fitness/predictions/rna_fm_4fill/Andreasson_2020_ribozyme.csv +++ /dev/null @@ -1,7344 +0,0 @@ -mutant,DMS_score,sequence,RNA_FM_scores_wt_fill,RNA_FM_scores_mask_fill,RNA_FM_scores_mut_fill,RNA_FM_scores_match_fill -"C10A,U56C",3.79e-06,AUAAAAGCGACAGAACUACAGAAAUGUAGUUGACGAGGAGGAGGUUUAUCGAAUUCUCGGCGGAUGCCUCCAAA,-1.2660943269729614,-1.0196086168289185,-1.0062952041625977,-1.2333139181137085 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-"U34C,A77G",-2.075473661,GCUUCUGGACUGCGAUGGGAGCACGAAACGUCGCGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACGGAAGC,-0.31595754623413086,-0.28908228874206543,-0.030874252319335938,-0.2432190179824829 -"C22U,G68A",-2.077865208,GCUUCUGGACUGCGAUGGGAGUACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGAGCCACCACAGAAGC,0.3692190647125244,0.3185877799987793,0.4086918830871582,0.3883460760116577 -"U31C,U42C",-2.188903849,GCUUCUGGACUGCGAUGGGAGCACGAAACGCCGUGGCGCAACUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC,0.9911038875579834,1.0830702781677246,1.1530461311340332,1.0558311939239502 -"U42C,U47C",-2.197704077,GCUUCUGGACUGCGAUGGGAGCACGAAACGUCGUGGCGCAACUGGGCGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC,0.8975667953491211,0.9764890670776367,1.1055439710617065,1.1662545204162598 -"A20G,A41G",-2.198363547,GCUUCUGGACUGCGAUGGGGGCACGAAACGUCGUGGCGCAGUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC,-0.02515733242034912,-0.20715343952178955,-0.24723505973815918,-0.13229572772979736 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-"G30A,G33A",-2.702251015,GCUUCUGGACUGCGAUGGGAGCACGAAACAUCAUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC,0.7131174802780151,0.8746614456176758,0.5135619640350342,0.5583248138427734 -"U31C,G68A",-2.719305819,GCUUCUGGACUGCGAUGGGAGCACGAAACGCCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGAGCCACCACAGAAGC,1.045158863067627,1.2111276388168335,0.9763079881668091,0.9850970506668091 -"C10U,G33A",-2.794591367,GCUUCUGGAUUGCGAUGGGAGCACGAAACGUCAUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC,0.1274200677871704,0.13029873371124268,0.11083900928497314,0.378473162651062 -"A20G,G30A",-2.828308773,GCUUCUGGACUGCGAUGGGGGCACGAAACAUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC,-0.09452450275421143,-0.18437469005584717,-0.08205199241638184,-0.04155075550079346 -"G30A,G68A",-2.895251861,GCUUCUGGACUGCGAUGGGAGCACGAAACAUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGAGCCACCACAGAAGC,1.2774217128753662,1.4983676671981812,1.375758171081543,1.3310859203338623 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-"C24U,G68A",-3.663441459,GCUUCUGGACUGCGAUGGGAGCAUGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGAGCCACCACAGAAGC,0.8951619863510132,0.7372097969055176,0.7942632436752319,0.9970777034759521 -"C32U,G68A",-3.817537467,GCUUCUGGACUGCGAUGGGAGCACGAAACGUUGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGAGCCACCACAGAAGC,0.5226093530654907,0.5254621505737305,0.5376707315444946,0.5951701402664185 -"U47C,A79G",-3.862013558,GCUUCUGGACUGCGAUGGGAGCACGAAACGUCGUGGCGCAAUUGGGCGGGGAAAGUCCUUAAAAGAGGGCCACCACAGGAGC,-0.2983715534210205,-0.3120098114013672,-0.25896215438842773,-0.27698230743408203 diff --git a/tests/fixtures/fitness/reference.csv b/tests/fixtures/fitness/reference.csv index d47f78a..10b3737 100644 --- a/tests/fixtures/fitness/reference.csv +++ b/tests/fixtures/fitness/reference.csv @@ -1,4 +1,32 @@ -DMS_ID,RNA_TYPE,RAW_CONSTRUCT_SEQ -Andreasson_2020_ribozyme,Ribozyme,AUAAAAGCGCCAGAACUACAGAAAUGUAGUUGACGAGGAGGAGGUUUAUCGAAUUUUCGGCGGAUGCCUCCAAA -Domingo_2018_tRNA,tRNA,GUUCCGUUGGCGUAAUGGUAACGCGUCUCCCUCCUAAGGAGAAGACUGCGGGUUCGAGUCCCGUACGGAACG -Tome_2014_GFP_aptamer,Aptamer,GCUUCUGGACUGCGAUGGGAGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC +DMS_ID,TITLE,AUTHOR,YEAR,RNA_TYPE,RNA_TYPE_GRANULAR,RAW_CONSTRUCT_SEQ,ASSAY_DESCRIPTION,PDB_STRUCTURE,RFAM_LINK,WT_DMS_SCORE,Notes +Andreasson_2020_ribozyme,Comprehensive sequence-to-function mapping of cofactor-dependent RNA catalysis in the glmS ribozyme,Andreasson,2020,Ribozyme,Ribozyme riboswitch,AUAAAAGCGCCAGAACUACAGAAAUGUAGUUGACGAGGAGGAGGUUUAUCGAAUUUUCGGCGGAUGCCUCCAAA,Deep mutational scanning assay measuring the self-cleavage rates of ribozyme variants,PDB 2Z75,https://rfam.org/family/glmS,0.071215256, +Beck_2022_ribozyme,Predicting higher-order mutational effects in an RNA enzyme by machine learning of high-throughput experimental data,Beck,2022,Ribozyme,Ribozyme,GGGGGCCACAGCAGAAGCGUUCACGUCGCGGCCCCUGUCAGAUUCUGGUGAAUCUGCGAAUUCUGCUGU,Deep mutational scanning assay measuring the self-cleavage activity of CPEB3 ribozyme variants using in vitro transcription,,https://rfam.org/family/CPEB3_ribozyme,/, +Domingo_2018_tRNA,Pairwise and higher-order genetic interactions during the evolution of a tRNA,Domingo,2018,tRNA,tRNA,GUUCCGUUGGCGUAAUGGUAACGCGUCUCCCUCCUAAGGAGAAGACUGCGGGUUCGAGUCCCGUACGGAACG,Deep mutational scanning assay measuring the fitness effects of tRNA variants in yeast cells using a competitive growth assay and high-throughput sequencing,,https://rfam.org/family/tRNA,0, +Guy_2014_tRNA,Identification of the determinants of tRNA function and susceptibility to rapid tRNA decay by high-throughput in vivo analysis.,Guy,2014,tRNA,tRNA,CUCUCGGUAGCCAAGUUGGUUUAAGGCGCAAGACUUUAAUUUAUCACUACGAAAUCUUGAGAUCGGGCGUUCGACUCGCCCCCGGGAGAUUUUUUCCUCGAGCAA,Deep mutational scanning assay measuring the suppressor activity of tRNA variants in yeast cells using a fluorescent reporter,PDB 1EHZ,https://rfam.org/family/tRNA,1, +Janzen_2022_fam1a1_ribozyme,Emergent properties as by-products of prebiotic evolution of aminoacylation ribozymes,Janzen,2022,Ribozyme,Fam1A.1 aminoacylation ribozyme,GGGAAUGGAUCCACAUCUACGAAUUCCUACUUCAAACAAUCGGUCUGUUCACUGCAGACUUGACGAAGCUG,Deep mutational scanning assay measuring the self-aminoacylation activity of ribozyme variants with multiple amino acid substrates using kinetic sequencing,,,104.650357, +Janzen_2022_fam1b1_ribozyme,Emergent properties as by-products of prebiotic evolution of aminoacylation ribozymes,Janzen,2022,Ribozyme,Fam1B.1 aminoacylation ribozyme,GGGAAUGGAUCCACAUCUACGAAUUCCCACACUUCAAGCAAUCGGUCUUCACUGCAGACUUGACGAAGCUG,Deep mutational scanning assay measuring the self-aminoacylation activity of ribozyme variants with multiple amino acid substrates using kinetic sequencing,,,87.79944642, +Janzen_2022_fam21_ribozyme,Emergent properties as by-products of prebiotic evolution of aminoacylation ribozymes,Janzen,2022,Ribozyme,Fam2.1 aminoacylation ribozyme,GGGAAUGGAUCCACAUCUACGAAUUCAUUACCCUGGUCAUCGAGUGAUUCACUGCAGACUUGACGAAGCUG,Deep mutational scanning assay measuring the self-aminoacylation activity of ribozyme variants with multiple amino acid substrates using kinetic sequencing,,,1006.57529, +Janzen_2022_fam22_ribozyme,Emergent properties as by-products of prebiotic evolution of aminoacylation ribozymes,Janzen,2022,Ribozyme,Fam2.2 aminoacylation ribozyme,GGGAAUGGAUCCACAUCUACGAAUUCAUUCACCUAGGUCAUCGGGUGUUCACUGCAGACUUGACGAAGCUG,Deep mutational scanning assay measuring the self-aminoacylation activity of ribozyme variants with multiple amino acid substrates using kinetic sequencing,,,736.7754285, +Janzen_2022_fam31_ribozyme,Emergent properties as by-products of prebiotic evolution of aminoacylation ribozymes,Janzen,2022,Ribozyme,Fam3.1 aminoacylation ribozyme,GGGAAUGGAUCCACAUCUACGAAUUCAAGUUUGCUAAUAGUCGCAAGUUCACUGCAGACUUGACGAAGCUG,Deep mutational scanning assay measuring the self-aminoacylation activity of ribozyme variants with multiple amino acid substrates using kinetic sequencing,,,44.94817611, +Kobori_2015_ribozyme_j12,High-throughput assay and engineering of self-cleaving ribozymes by sequencing,Kobori,2015,Ribozyme,Lib-J1/2,GGGCCGCGACUCUAGAAGUGAUGCUCUGCAAGUAUAAUCGCGUGGAUAUGGCACGCAAGUUUCUACCGGGCACCGUAAAUGUCCGACUACUCCUAUUCCGGCACGUCCACGUCGUGCAGAGCGGUAACAUGCGUUACUAGGGGUGCAAGAGCUCUUUUUGAGGAGGAGCUCUUUUUGCUGCACUAGUUGCAUCAGAUGGUAACGCAUGGCUAAGCCGGAAAGGGGGAGAC,Deep mutational scanning assay measuring ligand-dependent self-cleavage activity of HDV-like ribozyme variants with a guanine aptamer inserted at the J1/2 position via 4 randomized connecting bases,,https://rfam.org/family/drz-agam-2-2,0.799794661,Used highest activity variant as WT +Guanine (AAGU) +Kobori_2015_ribozyme_p4,High-throughput assay and engineering of self-cleaving ribozymes by sequencing,Kobori,2015,Ribozyme,Lib-P4,GGGCCGCGACUCUAGAAGUGAUGCUCUGCAAAUGGGGUAGGAGGCGAUGCCUCGUCCUCAUACCCAACUCCUAUUCCGGCACGUCCACGUCGUGCAGAGCGGUAAUACUAUAAUCGCGUGGAUAUGGCACGCAAGUUUCUACCGGGCACCGUAAAUGUCCGACUAGUAGCUAAGCCGGAAAGGGGGAGAC,Deep mutational scanning assay measuring ligand-dependent self-cleavage activity of HDV-like ribozyme variants with a guanine aptamer inserted at the P4 position via 4 randomized connecting bases,,https://rfam.org/family/Purine,0.91841127,Used highest activity variant as WT +Guanine (AUAC) +Kobori_2015_ribozyme_tw,High-throughput assay and engineering of self-cleaving ribozymes by sequencing,Kobori,2015,Ribozyme,Lib-Tw,GGGCGCGGCAUUAAUGCAGCUUUAUUGGAAACAAUAAAGCGGUUACAAGCCCGCAAAAAUAGCAGAGUAAUGUCGCG,Deep mutational scanning assay measuring self-cleavage activity of twister ribozyme variants with 5 randomized bases near a pseudoknot interaction,,,0.92053973, +Kobori_2018_ribozyme,Deep sequencing analysis of aptazyme variants based on a pistol ribozyme,Kobori,2018,Ribozyme,Pistol ribozyme,GGGTCGUUGUUCCATAATCGCGTGGATATGGCACGCAAGTTTCTACCGGGCACCGTAAATGTCCGACTGGAGCCGTTCGGGCGGCTATAAACAGACCTCAGGCCCGAAGCGTGGCGGCACCTGCCGCCGGTGGTAAAAAAGATCGGAAGAGCACACGTCT,"Deep mutational scanning assay measuring the self-cleavage activity of ribozyme variants in vitro, with and without the presence of guanine ",,https://rfam.org/family/Pistol,5.204340517,Used highest ON/Off ratio as WT (UUGUUCC) +Li_2016_tRNA,The fitness landscape of a tRNA gene,Li,2016,tRNA,tRNA,GUUCCGUUGGCGUAAUGGUAACGCGUCUCCCUCCUAAGGAGAAGACUGCGGGUUCGAGUCCCGUACGGAACG,Deep mutational scanning assay measuring the fitness of tRNA gene variants in yeast using competitive growth,,https://rfam.org/family/tRNA,1, +Peri_2022_ribozyme,Dynamic RNA Fitness Landscapes of a Group I Ribozyme during Changes to the Experimental Environment,Peri,2022,Ribozyme,Azoarcus group I ribozyme,GUGCCUUGCGCCGGGAAACCACGCAAGGGAUGGUGUCAAAUUCGGCGAAACCUAAGCGCCCGCCCGGGCGUAUGGCAACGCCGAGCCAAGCUUCGGCGCCUGCGCCGAUGAAGGUGUAGAGACUAGACGGCACCCACCUAAGGCAAACGCUAUGGUGAAGGCAUAGUCCAGGGAGUGGCGAAAGUCACACAAACCGG,Deep mutational scanning assay measuring the activity of ribozyme variants across multiple magnesium concentrations using in vitro selection,,,1, +Pitt_2010_ribozyme,Rapid Construction of Empirical RNA Fitness Landscapes,Pitt,2010,Ribozyme,Ligase Ribozyme,GGAACACUAUCCGACUGCGGUCGGUGGAGAUGUAUAGUCUUAGGGUGAGGCUGGAGAUCGGAAGAGCGUCGUGUAGGGAAAGAGUGU,Deep mutational scanning assay measuring self-cleavage activity of hammerhead ribozyme variants,,,0.55, +Roberts_2023_cepeb3_ribozyme,RNA sequence to structure analysis from comprehensive pairwise mutagenesis of multiple self-cleaving ribozymes,Roberts,2023,Ribozyme,CPEB3 Ribozyme,GGGGGCCACAGCAGAAGCGUUCACGUCGCGGCCCCUGUCAGAUUCUGGUGAAUCUGCGAAUUCUGCUGU,Deep mutational scanning assay measuring the self-cleavage activity of ribozyme variants using in vitro co-transcriptional cleavage,,https://rfam.org/family/CPEB3_ribozyme,WT activitiy: 0.9. In fitness score: 1, +Roberts_2023_HDV_ribozyme,RNA sequence to structure analysis from comprehensive pairwise mutagenesis of multiple self-cleaving ribozymes,Roberts,2023,Ribozyme,HDV Ribozyme,GGCCGGCAUGGUCCCAGCCUCCUCGCUGGCGCCGGCUGGGCAACAUUCCGAGGGGACCGUCCCCUCGGUAAUGGCGAAUGGGACCCA,Deep mutational scanning assay measuring the self-cleavage activity of ribozyme variants using in vitro co-transcriptional cleavage,,https://rfam.org/family/HDV_ribozyme,WT activitiy: 0.6. In fitness score: 1, +Roberts_2023_hh_ribozyme,RNA sequence to structure analysis from comprehensive pairwise mutagenesis of multiple self-cleaving ribozymes,Roberts,2023,Ribozyme,Hammerhead Ribozyme,ACCGGAUGUGCUUUCCGGUCUGAUGAGUCCGUGAGGACGAAACAG,Deep mutational scanning assay measuring the self-cleavage activity of ribozyme variants using in vitro co-transcriptional cleavage,,,WT activitiy: 0.34. In fitness score: 1, +Roberts_2023_hp_ribozyme,RNA sequence to structure analysis from comprehensive pairwise mutagenesis of multiple self-cleaving ribozymes,Roberts,2023,Ribozyme,Hairpin Ribozyme,GUCCUCUUUCACGUGCGCGUGAAAGAGAGAAGUGAACCAGAGAAACACACGUUGUGGUAUAUUACCUGGUA,Deep mutational scanning assay measuring the self-cleavage activity of ribozyme variants using in vitro co-transcriptional cleavage,,https://rfam.org/family/Hairpin,WT activitiy: 0.52. In fitness score: 1, +Roberts_2023_tw_ribozyme,RNA sequence to structure analysis from comprehensive pairwise mutagenesis of multiple self-cleaving ribozymes,Roberts,2023,Ribozyme,Twister Ribozyme,AACACUGCCAAUGCCGGUCCCAAGCCCGGAUAAAAGUGGAGGGGGCGG,Deep mutational scanning assay measuring the self-cleavage activity of ribozyme variants using in vitro co-transcriptional cleavage,,,WT activitiy: 0.6. In fitness score: 1, +Soo_2021_ribozyme,Fitness landscape of a dynamic RNA structure,Soo,2021,Ribozyme,Ribozyme,CUCUCUAGGUAGCAAUAUUACGACAUGGAGGGAAAAGUUAUCAGGCAUGCACCUGGUAGCUAGUCUUUAAACCAAUAGAUUGCAUCGGUUUAAAAGGCAAGACCGUCAAAUUGCGGGAAAGGGGUCAACAGCCGUUCAGUACCAAGUCUCAGGGGAAACUUUGAGAUGGCCUUGCAAAGGGUAUGGUAAUAAGCUGACGGACAUGGUCCUAACCACGCAGCCAAGUCCUAAGUCAACAGAUCUUCUGUUGAUAUGGAUGCAGUUCACAGACUAAAUGUCGGUCGGGGAAGAUGUAUUCUUCUCAUAAGAUAUAGUCGGACCUCUCCUUAAUGGGAGCUAGCGGAUGAAGUGAUGCAACACUGGAGCCGCUGGGAACUAAUUUGUAUGCGAAAGUAUAUUGAUUAGUUUUGGAGUACUCGUGGUCG,Deep mutational scanning assay measuring the impact of single and double nucleotide substitutions on ribozyme self-splicing activity in vitro,,https://rfam.org/family/Intron_gpI,0, +Tome_2014_GFP_aptamer,Comprehensive analysis of RNA-protein interactions by high-throughput sequencing-RNA affinity profiling,Tome,2014,Aptamer,GFP aptamer,GCUUCUGGACUGCGAUGGGAGCACGAAACGUCGUGGCGCAAUUGGGUGGGGAAAGUCCUUAAAAGAGGGCCACCACAGAAGC,Deep mutational scanning assay measuring the binding affinity of RNA aptamer variants to their target proteins using in vitro transcription,,,Kd: 4.26887151067555, +Tome_2014_NELFE_aptamer,Comprehensive analysis of RNA-protein interactions by high-throughput sequencing-RNA affinity profiling,Tome,2014,Aptamer,NELF-E aptamer,CCCAACGACUGCCGAGCGAGAUUACGCUUGAGCGCCCCACUGAGGAUGCCCACGGGCGAUUGGGGCACGG,Deep mutational scanning assay measuring the binding affinity of RNA aptamer variants to their target proteins using in vitro transcription,,,Kd: 5.237, +Zhang_2020_cpeb3_ribozyme,Accurate inference of the full base-pairing structure of RNA by deep mutational scanning and covariation-induced deviation of activity.,Zhang,2020,Ribozyme,CPEB3 Ribozyme,UAACAGGGGGCCACAGCAGAAGCGUUCACGUCGCAGCCCCUGUCAGAUUCUGGUGAAUCUGCGAAUUCUGCUGUAUAUCUC,Deep mutational scanning measured the self-cleavage activity of CPEB3 ribozyme variants via in vitro transcription and high_throughput sequencing,,https://rfam.org/family/CPEB3_ribozyme,1, +Zhang_2024_line1_mini_ribozyme,Minimal twister sister-like self-cleaving ribozymes in the human genome revealed by deep mutational scanning.,Zhang,2024,Ribozyme,LINE1-mini,GGCAGAGCUCAACACAGCGUGGCAAAGUGGCUGUGGCUAGACUGCC,Deep mutational scanning measured the self_cleavage activities of minimal TS_like ribozyme variants from LINE_1 and OR4K15 via high_throughput sequencing,,,1, +Zhang_2024_line1_full_ribozyme,Minimal twister sister-like self-cleaving ribozymes in the human genome revealed by deep mutational scanning.,Zhang,2024,Ribozyme,LINE1-full,UACGUAGGUGGUUUUCCCCUGAUAGUGCUAAGGGGGCCUGGAAGUUUGGACUGGGCAGAGCUCAACACAGCGUGGCAAAGUGGCUGUGGCUAGACUGCCUCUCUGGAUUCCUCGUCACUCAGCAGGGCAUCUCUGAGAGAAAUGCC,Deep mutational scanning measured the self_cleavage activities of minimal TS_like ribozyme variants from LINE_1 and OR4K15 via high_throughput sequencing,,,1, +Zhang_2024_OR4K15_ribozyme,Minimal twister sister-like self-cleaving ribozymes in the human genome revealed by deep mutational scanning.,Zhang,2024,Ribozyme,OR4K15,GUUAACGGUCAUAGGCCAUGGAAACUAGGAGCACCAUUUCGCUGCCAGUGAAGAAAUGAACAAAGAAAAUCUGGGCCAGGCAGGCAUCAAAAGAAAUAGUCUUGCGCUCAACCAGAAAGUCUGUAAUCAUUUUAAGGGUA,Deep mutational scanning measured the self_cleavage activities of minimal TS_like ribozyme variants from LINE_1 and OR4K15 via high_throughput sequencing,,,1, +McRae_2024_5tu_ribozyme,Cryo-EM structure and functional landscape of an RNA polymerase ribozyme,McRae,2024,Ribozyme,TPR 5TU,GGAUCUUCUCGAUCUAACAAAAAAGACAAAUCUGCCACAAAGCUUGAGAGCAUCUUCGGAUGCAGAGGCGGCAGCCUUCGGUGGCGCGAUAGCGCCAACGUUCUCAACUAUGACACGCAAAACGCGUGCUCCGUUGAAUGGAGUUUAUCAUG,Deep mutational scanning assay measuring the polymerase activity of an RNA polymerase ribozyme in vitro using high-throughput sequencing.,PDB 8T2P,,0, +McRae_2024_t1_ribozyme,Cryo-EM structure and functional landscape of an RNA polymerase ribozyme,McRae,2024,Ribozyme,TPR t1,GACCAAUCUGCCCUCAGAGCUCGAGAACAUCUUCGGAUGCAGAGGAGGCAGGCUUCGGUGGCGCGAUAGCGCCAACGUCCUCAACCUCCAAUGCAUCCCACCACAUGAUGAUGCCUGAAGAGCCUUGGUUUUUUG,Deep mutational scanning assay measuring the polymerase activity of an RNA polymerase ribozyme in vitro using high-throughput sequencing.,PDB 8T2P,,0, +Kobori_2016_osa_ribozyme,High-Throughput Mutational Analysis of a Twister Ribozyme,Kobori ,2016,Ribozyme,Osa-1-4 twister ribozyme,CCGCCUAACACUGCCAAUGCCGGUCCCAAGCCCGGAUAAAAGUGGAGGGGGCGG,Deep mutational scanning assay measuring the self_cleavage activity of twister ribozyme variants in vitro using high_throughput sequencing.,PDB 4OJI,,0.821, diff --git a/tests/fixtures/s3d/README.md b/tests/fixtures/s3d/README.md index d6abe1d..c18ad46 100644 --- a/tests/fixtures/s3d/README.md +++ b/tests/fixtures/s3d/README.md @@ -1,5 +1,5 @@ These fixtures contain 30 scored RNA3DB 2026-01-05 sequences and ten real exclusion controls with their matching RNAGym registry, USAlign, and model-score -rows. They cover repeated-sequence monomers, multimers, missing predictions, +rows. They cover repeated-sequence monomers, multimers, failed predictions, sequence clusters, and exclusion by release date, resolution, and missing residues. diff --git a/tests/test_fitness.py b/tests/test_fitness.py index 389ee0b..b229083 100644 --- a/tests/test_fitness.py +++ b/tests/test_fitness.py @@ -1,547 +1,174 @@ -"""Reproduce the fitness workflow on complete released assays.""" +"""Reproduce fitness evaluation and model scoring on real assay variants.""" -import json -import runpy +import os import shlex -import shutil import sys from pathlib import Path -from types import SimpleNamespace import numpy as np -import pandas as pd +import polars as pl import pytest -import torch -from fitness import analyze_fill_strategies -from fitness.baselines.Evo import score_evo2_single_dms as evo2 -from fitness.baselines.masked_lm import ( - MASK_CHAR, - MaskedLMAdapter, - accumulate_scores, - build_tasks, - parse_mutations, - runner, - window_contexts, -) -from fitness.baselines.masked_lm.engine import pad_contexts -from fitness.baselines.masked_lm.strategies import validate_table -from fitness.baselines.Nucleotide_Transformer.compute_fitness import NTv3Adapter -from fitness.merge_scoring_files import combine_csv_data -from fitness.model_registry import ALL_MODELS, SCORE_COLS, resolve_source -from fitness.performance_fitness import get_performance_dataset +from polars.testing import assert_frame_equal -REPOSITORY = Path(__file__).parent.parent -FIXTURE_DIR = Path(__file__).parent / "fixtures" / "fitness" -ASSAY_DIR = FIXTURE_DIR / "assays" -PREDICTION_DIR = FIXTURE_DIR / "predictions" / "rna_fm_4fill" -REFERENCE_FILE = FIXTURE_DIR / "reference.csv" -ASSAY_NAMES = ( - "Andreasson_2020_ribozyme.csv", - "Domingo_2018_tRNA.csv", - "Tome_2014_GFP_aptamer.csv", -) -EXPECTED_FILES = ( - "results_by_rna_type.csv", - "results_by_mutation_depth.csv", - "results_by_rna_type_and_depth.csv", - "assay_level_results.csv", - "assay_level_results_transposed.csv", - "fill_strategy_per_assay.csv", - "fill_strategy_macro.csv", -) -MODEL_NAMES = tuple(f"rna_fm_{strategy}" for strategy in runner.STRATEGIES) -PREDICTION_COLUMNS = { - f"rna_fm_{strategy}_score": f"RNA_FM_scores_{strategy}" - for strategy in runner.STRATEGIES -} -VOCAB = { - token: index - for index, token in enumerate( - ("", "", "", "", "A", "C", "G", "U", "N") - ) -} - - -class FixtureAdapter(MaskedLMAdapter): - """Checkpoint-free adapter whose logits depend on the complete context.""" - - name = "fixture masked LM" - bases = "ACGU" - n_special_tokens = 2 - score_column = "RNA_FM_scores" - - def __init__(self): - self.base_ids = {base: VOCAB[base] for base in self.bases} - self.device = "cpu" - self.mask_id = VOCAB[""] - self.pad_id = VOCAB[""] - self.prefix_ids = [VOCAB[""]] - self.suffix_ids = [VOCAB[""]] - self.unk_id = VOCAB["N"] - - @staticmethod - def add_arguments(parser): - """The fixture has no model-specific arguments.""" - - def load(self, args): - """Use the device selected by the shared runner.""" - self.device = args.device - - def logits_at(self, input_ids, attention_mask, rows, cols): - """Return deterministic context- and base-dependent logits.""" - positions = torch.arange(1, input_ids.shape[1] + 1, device=self.device) - context = (input_ids * positions).sum(dim=1)[rows, None] - vocab = torch.arange(len(VOCAB), device=self.device)[None, :] - logits = context * (vocab + 1) + cols[:, None] * (vocab + 2) - return torch.remainder(logits, 23).float() / 4 - - -class NonfiniteAdapter(FixtureAdapter): - """Adapter that simulates a numerically failed model forward pass.""" - - def logits_at(self, input_ids, attention_mask, rows, cols): - logits = super().logits_at(input_ids, attention_mask, rows, cols) - return torch.full_like(logits, torch.nan) - - -class LimitedFixtureAdapter(FixtureAdapter): - """Fixture adapter with a deliberately small position limit.""" - - max_tokens = 20 - - -class PaddedFixtureAdapter(FixtureAdapter): - """Fixture adapter that requires 128-position contexts.""" - - context_pad_char = "N" - - def context_length_for(self, length): - return 128 - - -def run_cli(monkeypatch, command): - """Run a Python command line entry point in the current process.""" - arguments = shlex.split(command) - script = Path(arguments[0]).resolve() - arguments[0] = str(script) - monkeypatch.syspath_prepend(str(script.parent)) - monkeypatch.setattr(sys, "argv", arguments) - runpy.run_path(str(script), run_name="__main__") - - -def fixture_log_probs(adapter, context, position): - """Score one masked position without the shared batching engine.""" - input_ids = torch.tensor([adapter.encode_context(context, MASK_CHAR)]) - logits = adapter.logits_at( - input_ids, - torch.ones_like(input_ids), - torch.tensor([0]), - torch.tensor([adapter.token_position(position)]), - ) - return torch.log_softmax(logits.float(), dim=-1)[0].numpy() - - -def masked(sequence, positions): - """Replace selected sequence positions with the fixture mask.""" - context = list(sequence) - for position in positions: - context[position] = MASK_CHAR - return "".join(context) - - -def runner_command(assay_dir, output_dir): - """Build the shared runner command for the Domingo fixture.""" - return ( - f"fitness-fixture --row_id 1 --ref_sheet {REFERENCE_FILE} " - f"--dms_dir_path {assay_dir} --output_dir_path {output_dir} " - "--device cpu --quiet_skips" - ) +from rnagym.config import ConfigFitness +from rnagym.fitness.tasks.model_registry import SCORE_COLS, resolve_source - -def test_fitness_workflow(tmp_path, monkeypatch): - """Reproduce the benchmark workflow from released RNA-FM predictions.""" - merged_dir = tmp_path / "merged" - performance_dir = tmp_path / "performance" - analysis_dir = tmp_path / "analysis" - merged_dir.mkdir() - (merged_dir / "stale.csv").write_text("stale\n") - - models = " ".join(MODEL_NAMES) - run_cli( - monkeypatch, - f"fitness/merge_scoring_files.py --processed_folder {ASSAY_DIR} " - f"--model_predictions_folder {PREDICTION_DIR.parent} " - f"--output_folder {merged_dir} --reference_file {REFERENCE_FILE} " - f"--models {models}", - ) - assert not (merged_dir / "stale.csv").exists() - run_cli( - monkeypatch, - f"fitness/performance_fitness.py --reference_file {REFERENCE_FILE} " - f"--combined_dir {merged_dir} --performance_dir {performance_dir} " - f"--models {models}", - ) - - for assay_name in ASSAY_NAMES: - assay = ( - pd.read_csv(ASSAY_DIR / assay_name) - .dropna(subset=["mutant"]) - .reset_index(drop=True) - ) - prediction = ( - pd.read_csv(PREDICTION_DIR / assay_name) - .dropna(subset=["mutant"]) - .reset_index(drop=True) - ) - merged = pd.read_csv(merged_dir / assay_name) - pd.testing.assert_frame_equal(merged[assay.columns], assay, check_exact=True) - for merged_column, prediction_column in PREDICTION_COLUMNS.items(): - np.testing.assert_allclose( - merged[merged_column], prediction[prediction_column], rtol=0, atol=1e-14 - ) - score_columns = list(PREDICTION_COLUMNS.values()) - single = ~prediction["mutant"].str.contains(",") - assert np.allclose( - prediction.loc[single, score_columns], - prediction.loc[single, score_columns[0]].to_numpy()[:, None], - ) - if (~single).any(): - assert np.count_nonzero( - np.ptp(prediction.loc[~single, score_columns].to_numpy(), axis=1) - ) - - run_cli( - monkeypatch, - f"{analyze_fill_strategies.__file__} " - f"--predictions_folder {PREDICTION_DIR.parent} " - f"--ref_sheet {REFERENCE_FILE} --output_folder {analysis_dir}", - ) - for result_file in EXPECTED_FILES: - result_dir = ( - analysis_dir if result_file.startswith("fill_strategy") else performance_dir - ) - observed = pd.read_csv(result_dir / result_file) - expected = pd.read_csv(FIXTURE_DIR / "expected" / result_file) - pd.testing.assert_frame_equal( - observed, expected, check_exact=False, rtol=0, atol=1e-10 - ) - - masked_models = [ - model for model in ALL_MODELS if isinstance(SCORE_COLS[model], dict) - ] - for model in masked_models: +FIXTURE_DIR = Path(__file__).parent / "fixtures" / "fitness" +MODEL = os.environ.get("PIXI_ENVIRONMENT_NAME") + + +@pytest.fixture +def fitness_data(tmp_path, monkeypatch): + """Write the fixture tables in the format used by the prediction commands.""" + for name, path in { + "DATA_DIR": tmp_path, + "ASSAY_DIR": tmp_path / "assays", + "COMBINED_DIR": tmp_path / "merged", + "LEADERBOARD_DIR": tmp_path / "leaderboard", + "MSA_DIR": tmp_path / "msa", + "PREDICTION_DIR": tmp_path / "predictions", + "REFERENCE_FILE": FIXTURE_DIR / "reference.csv", + }.items(): + monkeypatch.setattr(ConfigFitness, name, path) + ConfigFitness.ASSAY_DIR.mkdir() + for (name,), assay in pl.read_parquet(FIXTURE_DIR / "assays.parquet").group_by( + "DMS_ID" + ): + assay.drop("DMS_ID").write_csv(ConfigFitness.ASSAY_DIR / f"{name}.csv") + for (model, name), predictions in pl.read_parquet( + FIXTURE_DIR / "predictions.parquet" + ).group_by("model", "DMS_ID"): folder, column = resolve_source(SCORE_COLS, model) - assert folder.endswith("_4fill") - assert column.endswith("_wt_fill") - leaderboard_models = set( - pd.read_csv( - REPOSITORY / "leaderboard" / "fitness" / "leaderboard_signed_3ncRNA.csv" - )["model"] - ) - assert leaderboard_models <= set(SCORE_COLS) - - -def test_masked_lm_scoring_and_guards(tmp_path, monkeypatch): - """Score a complete real assay and reject corrupt scoring states.""" - assay = pd.read_csv(FIXTURE_DIR / "assays" / "Domingo_2018_tRNA.csv", nrows=1) - reference = pd.read_csv(FIXTURE_DIR / "reference.csv").set_index("DMS_ID") - wild_type = reference.loc["Domingo_2018_tRNA", "RAW_CONSTRUCT_SEQ"] - adapter = NonfiniteAdapter() - table = build_tasks( - assay["mutant"], - assay["sequence"], - wild_type, - adapter.bases, - ("wt_fill",), - verbose=False, - ) - position = table.pos[0] - table.pos[0] = len(table.contexts[table.ctx_id[0]]) - with pytest.raises(ValueError, match="falls outside"): - validate_table(table) - table.pos[0] = position - - with pytest.raises(FloatingPointError, match="non-finite log probability"): - accumulate_scores(adapter, table, 1, 1, 256, progress=False) - - with pytest.raises(ValueError, match="cannot hold one encoded context"): - accumulate_scores(FixtureAdapter(), table, 1, 1, 1, progress=False) - - window_contexts(table, budget=20) - assert table.contexts[0][table.pos[0]] == MASK_CHAR - assert np.isfinite( - accumulate_scores(FixtureAdapter(), table, 1, 1, 64, progress=False) - ).all() - - variant = pd.read_csv(FIXTURE_DIR / "assays" / "Domingo_2018_tRNA.csv").iloc[-1] - mutations = parse_mutations(variant["mutant"], FixtureAdapter.bases) - formula_table = build_tasks( - [variant["mutant"]], - [variant["sequence"]], - wild_type, - FixtureAdapter.bases, - runner.STRATEGIES, - verbose=False, - ) - formula_scores = accumulate_scores( - FixtureAdapter(), formula_table, 1, 16, 4096, progress=False - )[:, 0] - expected = np.zeros(len(runner.STRATEGIES)) - positions = [position for position, _, _ in mutations] - joint_context = masked(wild_type, positions) - fixture_adapter = FixtureAdapter() - for position, wild_base, mutant_base in mutations: - wild_context = masked(wild_type, [position]) - mutant_context = masked(variant["sequence"], [position]) - wild_probs = fixture_log_probs(fixture_adapter, wild_context, position) - joint_probs = fixture_log_probs(fixture_adapter, joint_context, position) - mutant_probs = fixture_log_probs(fixture_adapter, mutant_context, position) - wild_id = fixture_adapter.base_ids[wild_base] - mutant_id = fixture_adapter.base_ids[mutant_base] - expected += [ - wild_probs[mutant_id] - wild_probs[wild_id], - joint_probs[mutant_id] - joint_probs[wild_id], - mutant_probs[mutant_id] - mutant_probs[wild_id], - mutant_probs[mutant_id] - wild_probs[wild_id], - ] - assert formula_scores == pytest.approx(expected, abs=1e-6) - - padded_table = build_tasks( - [variant["mutant"]], - [variant["sequence"]], - wild_type, - PaddedFixtureAdapter.bases, - ("wt_fill",), - verbose=False, - ) - positions = padded_table.pos.copy() - pad_contexts(padded_table, PaddedFixtureAdapter()) - assert {len(context) for context in padded_table.contexts} == {128} - assert np.array_equal(padded_table.pos, positions) - assert all( - context.endswith("N" * (128 - len(wild_type))) - for context in padded_table.contexts - ) - - ntv3 = NTv3Adapter() - ntv3.num_downsamples = 7 - assert [ntv3.context_length_for(n) for n in (45, 128, 129, 425)] == [ - 128, - 128, - 256, - 512, - ] + destination = ConfigFitness.PREDICTION_DIR / folder / f"{name}.csv" + destination.parent.mkdir(parents=True, exist_ok=True) + predictions.select("mutant", pl.col("score").alias(column)).write_csv( + destination + ) + return tmp_path - nan_table = build_tasks( - [None, variant["mutant"]], - [wild_type, variant["sequence"]], - wild_type, - FixtureAdapter.bases, - ("wt_fill",), - verbose=False, - ) - nan_scores = accumulate_scores( - FixtureAdapter(), nan_table, 2, 16, 4096, progress=False - ) - assert np.isnan(nan_scores[0, 0]) - assert np.isfinite(nan_scores[0, 1]) - assay_dir = tmp_path / "assays" - output_dir = tmp_path / "predictions" - assay_dir.mkdir() - shutil.copy2( - FIXTURE_DIR / "assays" / "Domingo_2018_tRNA.csv", - assay_dir / "Domingo_2018_tRNA.csv", - ) - monkeypatch.setattr(sys, "argv", shlex.split(runner_command(assay_dir, output_dir))) +def test_evmutation_scoring(fitness_data, monkeypatch) -> None: + """Compare real MSA predictions with the native model's sequence energies.""" + if MODEL != "evmutation": + pytest.skip("Run through the EVmutation environment") + from rnagym.fitness.baselines.EVmutation import compute_fitness as scorer - runner.main(FixtureAdapter()) - scored = pd.read_csv(output_dir / "Domingo_2018_tRNA.csv") - score_columns = [f"RNA_FM_scores_{strategy}" for strategy in runner.STRATEGIES] - assert np.isfinite(scored[score_columns]).all().all() - single = ~scored["mutant"].str.contains(",") - assert np.allclose( - scored.loc[single, score_columns], - scored.loc[single, score_columns[0]].to_numpy()[:, None], - ) - assert ( - np.count_nonzero(np.ptp(scored.loc[~single, score_columns].to_numpy(), axis=1)) - > 3000 + name = "Domingo_2018_tRNA" + alignment = ConfigFitness.MSA_DIR / "by_assay" / f"{name}.a3m" + alignment.parent.mkdir(parents=True) + alignment.symlink_to((FIXTURE_DIR / alignment.name).resolve()) + alignment.with_suffix(".fa").write_text( + "\n".join(alignment.read_text().splitlines()[:2]) + "\n" ) - manifest = json.loads((output_dir / "Domingo_2018_tRNA.manifest.json").read_text()) - assert manifest["scorable_variants"] == len(scored) - assert manifest["contexts"] == 19275 - assert manifest["environment"]["device"] == "cpu" - assert manifest["terms"] == 185632 - assert manifest["strategies"] == [ - "wt-fill", - "mask-fill", - "mut-fill", - "match-fill", - ] + models = [] + infer = scorer.infer_model - invalid_output_dir = tmp_path / "invalid_predictions" - monkeypatch.setattr( - sys, "argv", shlex.split(runner_command(assay_dir, invalid_output_dir)) - ) - with pytest.raises(SystemExit): - runner.main(LimitedFixtureAdapter()) - assert not list(invalid_output_dir.glob("*.csv")) - - def inconsistent_scores(adapter, table, n_rows, **kwargs): - scores = np.tile(np.arange(n_rows), (len(table.strategies), 1)).astype(float) - scores[1] += 1.0 - return scores + def capture_model(config): + model = infer(config) + models.append(model) + return model - single_assay_dir = tmp_path / "single_assay" - single_assay_dir.mkdir() - domingo = pd.read_csv(FIXTURE_DIR / "assays" / "Domingo_2018_tRNA.csv") - domingo[~domingo["mutant"].str.contains(",")].to_csv( - single_assay_dir / "Domingo_2018_tRNA.csv", index=False - ) + monkeypatch.setattr(scorer, "infer_model", capture_model) + output = fitness_data / "scored" monkeypatch.setattr( - sys, - "argv", - shlex.split(runner_command(single_assay_dir, invalid_output_dir)), - ) - monkeypatch.setattr(runner, "accumulate_scores", inconsistent_scores) - with pytest.raises(SystemExit): - runner.main(FixtureAdapter()) - assert not list(invalid_output_dir.glob("*.csv")) - assert not list(invalid_output_dir.glob("*.manifest.json")) - - -def test_metrics_reject_nonfinite_predictions(): - """Reject incomplete predictions instead of changing the evaluation set.""" - frame = pd.DataFrame( - { - "mutant": ["A1C"] * 6, - "DMS_score": np.arange(6, dtype=float), - "model_score": [0.0, 1.0, np.inf, 3.0, np.nan, 5.0], - } - ) - with pytest.raises(ValueError, match="model_score"): - get_performance_dataset(frame, "DMS_score", ["model_score"]) - with pytest.raises(KeyError, match="missing_score"): - get_performance_dataset(frame, "DMS_score", ["missing_score"]) - - -# TODO(MCA): Clean up model tests -def test_evo2_workflow_and_guards(tmp_path): - """Score complete assays in one model load and reject failed inference.""" - - class FixtureEvo2: - calls = [] - fp8 = True - loads = 0 - nonfinite = False - - def __init__(self, model_name, local_path=None): - type(self).loads += 1 - self.model = SimpleNamespace( - config={"use_fp8_input_projections": type(self).fp8} - ) - - def score_sequences(self, sequences, **kwargs): - type(self).calls.append((sequences[0], kwargs)) - scores = np.array( - [sequence.count("T") / len(sequence) for sequence in sequences] - ) - if type(self).nonfinite: - scores[0] = np.nan - return scores - - assay_dir = tmp_path / "assays" - shutil.copytree(ASSAY_DIR, assay_dir) - domingo_file = assay_dir / "Domingo_2018_tRNA.csv" - output_dir = tmp_path / "evo2" - command = ( - f"--row_ids 0-2 --ref_sheet {REFERENCE_FILE} " - f"--dms_dir_path {assay_dir} --output_dir_path {output_dir} " - "--model_name evo2_1b_base --max_tokens_per_batch 4096 --require_fp8" + sys, "argv", shlex.split(f"evmutation --output {output} --cpu 2") + ) + scorer.main() + model = models[0] + assay = pl.read_csv(ConfigFitness.ASSAY_DIR / f"{name}.csv") + result = pl.read_csv(output / f"{name}.csv") + assert_frame_equal(result.select(assay.columns), assay) + reference = pl.read_csv(ConfigFitness.REFERENCE_FILE) + wt = reference.filter(pl.col("DMS_ID") == name)["RAW_CONSTRUCT_SEQ"][0] + positions = model.index_list - 1 + covered = np.array( + [ + all(i in positions for i, (a, b) in enumerate(zip(wt, sequence)) if a != b) + for sequence in assay["sequence"] + ] ) - args = evo2.parse_args(shlex.split(command)) - - evo2.run(args, FixtureEvo2) - assert FixtureEvo2.loads == 1 - assert len(FixtureEvo2.calls) == 3 - for sequence, options in FixtureEvo2.calls: - assert options == { - "batch_size": max( - 1, 4096 // evo2.effective_length(len(sequence), False, True) - ), - "prepend_bos": False, - "reduce_method": "mean", - "average_reverse_complement": True, - } - for assay_file in sorted(assay_dir.glob("*.csv")): - output_file = output_dir / assay_file.name - observed = pd.read_csv(output_file) - source = pd.read_csv(assay_file) - score_column = "evo2_1b_base_score" - expected = source["sequence"].map( - lambda sequence: ( - sequence.upper().replace("U", "T").count("T") / len(sequence) - ) - ) - np.testing.assert_allclose(observed[score_column], expected) - assert output_file.stat().st_mode & 0o777 == 0o644 - assert not list(output_dir.glob("*.tmp")) - - evo2.run(args, FixtureEvo2) - assert FixtureEvo2.loads == 1 - args.row_id, args.row_ids = 0, None - args.output_dir_path = tmp_path / "invalid_evo2" - FixtureEvo2.fp8 = False - with pytest.raises(RuntimeError, match="does not use FP8"): - evo2.run(args, FixtureEvo2) - - FixtureEvo2.fp8 = True - FixtureEvo2.nonfinite = True - with pytest.raises(FloatingPointError, match="nonfinite model scores"): - evo2.run(args, FixtureEvo2) - assert not list(args.output_dir_path.glob("*.csv")) - - FixtureEvo2.nonfinite = False - domingo = pd.read_csv(domingo_file) - domingo.loc[0, "sequence"] = np.nan - domingo.to_csv(domingo_file, index=False) - args.row_id = 1 - with pytest.raises(ValueError, match="missing or empty sequences"): - evo2.run(args, FixtureEvo2) - - -def test_merge_rejects_incomplete_predictions(tmp_path): - """Reject missing assays and partial rows before replacing merged output.""" - assay_name = "Domingo_2018_tRNA.csv" - assay_dir = tmp_path / "assays" - prediction_dir = tmp_path / "predictions" / "rna_fm_4fill" - assay_dir.mkdir() - prediction_dir.mkdir(parents=True) - shutil.copy2(FIXTURE_DIR / "assays" / assay_name, assay_dir / assay_name) - merged_dir = tmp_path / "merged" - - with pytest.raises(FileNotFoundError, match="Prediction coverage is incomplete"): - combine_csv_data( - assay_dir, - prediction_dir.parent, - merged_dir, - ["RNA-FM"], - SCORE_COLS, - ) - assert not merged_dir.exists() - - predictions = pd.read_csv( - FIXTURE_DIR / "predictions" / "rna_fm_4fill" / assay_name - ).iloc[:-1] - predictions.to_csv(prediction_dir / assay_name, index=False) - - with pytest.raises(ValueError, match="Row count mismatch"): - combine_csv_data( - assay_dir, - prediction_dir.parent, - merged_dir, - ["RNA-FM"], - SCORE_COLS, + np.testing.assert_array_equal(result[scorer.SCORE_COLUMN].is_not_null(), covered) + sequences = [wt, *assay.filter(pl.Series(covered))["sequence"]] + energies = model.hamiltonians( + ["".join(s[i] for i in positions).replace("U", "T") for s in sequences] + )[:, 0] + np.testing.assert_allclose( + result.filter(pl.Series(covered))[scorer.SCORE_COLUMN], + energies[1:] - energies[0], + atol=1e-6, + rtol=1e-6, + ) + + +def test_fitness_workflow(fitness_data) -> None: + """Reproduce both leaderboards from released predictions for every model.""" + if MODEL not in {None, "default"}: + pytest.skip("Run the shared workflow in the default environment") + from rnagym.fitness.tasks import leaderboard, merge_scoring_files + + merge_scoring_files.main([]) + leaderboard.main([]) + for actual, expected in ( + ("leaderboard_signed_3ncRNA.csv", "leaderboard.csv"), + ("leaderboard_evmutation.csv", "comparison.csv"), + ("evmutation_coverage.csv", "coverage.csv"), + ): + assert_frame_equal( + pl.read_csv(ConfigFitness.LEADERBOARD_DIR / actual), + pl.read_csv(FIXTURE_DIR / expected), + check_exact=False, + rel_tol=0, + abs_tol=1e-12, ) - assert not merged_dir.exists() + assert (ConfigFitness.LEADERBOARD_DIR / "README.md").read_text() == ( + FIXTURE_DIR / "leaderboard.md" + ).read_text() + for source in ConfigFitness.ASSAY_DIR.glob("*.csv"): + assay = pl.read_csv(source).drop_nulls("mutant") + merged = pl.read_csv(ConfigFitness.COMBINED_DIR / source.name) + assert_frame_equal(merged.select(assay.columns), assay) + + +def test_genslm_scoring(fitness_data) -> None: + """Compare checkpoint predictions and padded batches with native causal losses.""" + if MODEL != "genslm": + pytest.skip("Run through the GenSLM environment") + import genslm + import torch + + from rnagym.fitness.baselines.GenSLM import compute_fitness as scorer + + device = "cuda" if torch.cuda.is_available() else "cpu" + model = genslm.GenSLM( + "genslm_2.5B_patric", + model_cache_dir=str(ConfigFitness.CHECKPOINT_DIR / "genslm/2.5B"), + ) + output = fitness_data / "scored" + scorer.main( + scorer.parse_args(shlex.split(f"--output {output}")), + lambda *args, **kwargs: model, + ) + sequences, expected, observed = [], [], [] + for source in sorted(ConfigFitness.ASSAY_DIR.glob("*.csv")): + assay = pl.read_csv(source).drop_nulls("mutant") + result = pl.read_csv(output / source.name) + assert_frame_equal(result.select(assay.columns), assay) + sequence = assay["sequence"][0].replace("U", "T") + encoded = genslm.SequenceDataset( + [sequence], (len(sequence) + 2) // 3, model.tokenizer, verbose=False + )[0] + with torch.inference_mode(): + loss = model( + encoded["input_ids"][None].to(device), + encoded["attention_mask"].to(device), + ).loss.item() + sequences.append(sequence) + expected.append(-loss) + observed.append(result["logit_scores"][0]) + np.testing.assert_allclose(observed, expected, atol=1e-5, rtol=1e-6) + np.testing.assert_allclose( + scorer.sequence_log_likelihoods(model, sequences, device), + expected, + atol=1e-5, + rtol=1e-6, + ) diff --git a/tests/test_s2d.py b/tests/test_s2d.py index 439c1fd..e8c55aa 100644 --- a/tests/test_s2d.py +++ b/tests/test_s2d.py @@ -8,6 +8,7 @@ import polars as pl import pytest from polars.testing import assert_frame_equal + from rnagym.config import Config2D from rnagym.s2d.models.utils import parse_pairs diff --git a/tests/test_s3d.py b/tests/test_s3d.py index 99ef85a..1894f32 100644 --- a/tests/test_s3d.py +++ b/tests/test_s3d.py @@ -4,6 +4,7 @@ import polars as pl from polars.testing import assert_frame_equal + from rnagym.config import Config2D, Config3D from rnagym.s3d.cmd import split from rnagym.s3d.tasks import score