Protein Coding Potential

Gene sequence protein coding potential classification with a Machine Learning approach.

More information and background for the project: https://www.ebi.ac.uk/seqdb/confluence/display/ENSGBD/Protein+Coding+Potential

project setup

dev environment setup

set up a Python virtual environment for the project and install its dependencies

pyenv install 3.9.7

pyenv virtualenv 3.9.7 protein_coding_potential

poetry install

dataset

generate full and dev dataset pickled dataframes

python dataset_generation.py --generate_datasets --coding_transcripts <coding transcripts FASTA path> --non_coding_transcripts <non-coding transcripts FASTA path>

experiment setup

Specify parameters and hyperparameters for your experiment by editing or copying one of the configuration YAML files.

train a classifier

run training directly

python <pipeline script> --train --configuration <experiment configuration file>

# e.g.
python transformer_pipeline.py --train --configuration transformer_configuration.yaml

submit a training job on LSF

python submit_LSF_job.py --pipeline <pipeline script> --configuration <experiment configuration file>

# e.g.
python submit_LSF_job.py --pipeline transformer_pipeline.py --configuration transformer_configuration.yaml

test a classifier

load a checkpoint and run testing directly

python <pipeline script> --test --checkpoint <checkpoint path>

submit a testing job on LSF

python submit_LSF_job.py --pipeline <pipeline script> --checkpoint <checkpoint path>

License

Apache License 2.0