Update README.md

README.md

@@ -8,14 +8,14 @@ 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  
+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:
@@ -32,10 +32,18 @@ If any packages are missing during python code execution, install them manually
 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.
 
 Run Mass_models.py using FEATURES_ALL.ndjson, Supp1.csv, and Supp2.xlsx.
 Example run settings:
 ```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   
@@ -48,6 +56,14 @@ python3 Mass_models.py
   --n_trials 30   
   --timeout 5400
 ```
+```python
+python3 predict_models_dir.py \
+  --genomes_dir /path/to/fasta_dir \
+  --models_dir results_models \
+  --outdir predictions
+```
+
+![Models benchmark](https://cdn-uploads.huggingface.co/production/uploads/6605ab970fabda441257b27e/Wt-I28Er9KGwEJOSOzSLu.png)
 
 
-![Models benchmark](https://cdn-uploads.huggingface.co/production/uploads/6605ab970fabda441257b27e/Wt-I28Er9KGwEJOSOzSLu.png)
+Code and files will be modified and further developed in a packaged container after all required tests and training are completed.