| | --- |
| | license: apache-2.0 |
| | task_categories: |
| | - token-classification |
| | language: |
| | - en |
| | tags: |
| | - biology |
| | --- |
| | |
| | # tRNA-based classification model |
| |
|
| | The dataset contains: |
| | 1. Generic files used for training the dataset |
| | 2. Supplementary data used for labeling |
| | 3. An HTML file with a step-by-step description of the research |
| | 4. Python scripts used to train the models |
| | 5. The two best models were selected based on the lowest number of false negatives (FNs) on a third, independent test dataset. |
| |
|
| | ## Setup |
| | Download Miniconda and use: |
| | ```bash |
| | conda env create -f environment.yml |
| | ``` |
| | to replicate the working environment. |
| |
|
| | If any packages are missing during python code execution, install them manually using pip, based on import error messages. |
| |
|
| | ## Steps for replication: |
| | 1. Download supplementary data from https://doi.org/10.7554/eLife.71402 |
| | 2. **ftp_urls.txt** contains a list of genome download addresses (most of them are available). |
| | 3. Run **full.sh** to download genomes and extract features for model training from full dataset, saved as **FEATURES_ALL.ndjson** (genomes are removed to preserve memory) |
| | 4. Run **80_20_split_fixed.py** on **FEATURES_ALL.ndjson** together with both supplementary files to perform an automatic stratified 80/20 split, with archaeal and contaminated genomes filtered out. |
| | 5. Run **Mass_models.py** on **FEATURES_ALL.ndjson**, **Supp1.csv**, **Supp2.xlsx** |
| | 6. Run **predict_dir.py** to generate predictions for all trained models on FASTA genomes. If files provided, annotate predictions with ground truth from the TSV file, and report metrics separately for Isolate and MAG genomes. |
| | |
| | Example run settings (All resutls were obtained using seed=42): |
| | ```python |
| | python3 80_20_split_fixed.py |
| | --ndjson FEATURE_ALL.ndjson |
| | --supp1 Supp1.csv |
| | --supp2 Supp2.xlsx |
| | --outdir split_dataset |
| | ``` |
| | ```python |
| | python3 Mass_models.py |
| | --ndjson split_dataset/subset01/ |
| | --supp2 Supp2.xlsx |
| | --supp1 Supp1.csv |
| | --outdir . |
| | --train_mode both |
| | --weight_mode both |
| | --model all |
| | --metric all |
| | --n_trials 30 |
| | --timeout 5400 |
| | ``` |
| | ```python |
| | python3 predict_models_dir.py \ |
| | --genomes_dir /path/to/fasta_dir \ |
| | --models_dir results_models \ |
| | --outdir predictions |
| | ``` |
| | |
| |  |
| | |
| | |
| | Code and files will be modified and further developed in a packaged container after all required tests and training are completed. |
