TransFew

Improving protein function prediction by learning and integrating representations of protein sequences and function labels

TransFew leaverages representations of both protein sequences and function labels (Gene Ontology (GO) terms) to predict the function of proteins. It improves the accuracy of predicting both common and rare function terms (GO terms).

Installation

# clone project
git clone https://github.com/BioinfoMachineLearning/TransFew.git
cd TransFew/

# download trained models and test sample
https://calla.rnet.missouri.edu/rnaminer/tfew/TFewDataset

# Unzip Dataset
unzip TFewDataset


# create conda environment
conda env create -f transfew.yaml
conda activate transfew

Prediction

Predict protein functions with TransFew

options:
  -h, --help            show this help message and exit

  --data-path DATA_PATH  Path to data files (models)

  --working-dir WORKING_DIR  Path to generate temporary 
  files

  --ontology ONTOLOGY   Path to data files

  --no-cuda NO_CUDA     Disables CUDA training.

  --batch-size BATCH_SIZE Batch size.

  --fasta-path FASTA_PATH Path to Fasta

  --output OUTPUT       File to save output

4. An example of predicting cellular component of some proteins:

1. Change ROOT_DIR in CONSTANTS.py to path of data directory

2. python predict.py  --data-path /TFewData/ --fasta-path output_dir/test_fasta.fasta --ontology cc --working-dir output_dir --output result.tsv

Output format

  protein   GO term  score
  A0A7I2V2M2	GO:0043227	0.996
  A0A7I2V2M2	GO:0043226	0.996
  A0A7I2V2M2	GO:0005737	0.926
  A0A7I2V2M2	GO:0043233	0.924
  A0A7I2V2M2	GO:0031974	0.913
  A0A7I2V2M2	GO:0070013	0.912
  A0A7I2V2M2	GO:0031981	0.831
  A0A7I2V2M2	GO:0005654	0.767

Dataset

See DATASET.md (https://github.com/BioinfoMachineLearning/TransFew/blob/main/DATASET.md) for description of data

Training

The training program is available in training.py, to train the model:

    1. Change ROOT_DIR in CONSTANTS.py to path of data directory
    2. Run: python training.py

Reference

Boadu, F., & Cheng, J. (2024). Improving protein function prediction by learning and integrating representations of protein sequences and function labels. Bioinformatics Advances. Volume 4, Issue 1, vbae120.