README.md

PhyloGPN: A Phylogenetic Approach to Genomic Language Modeling

A framework for training genomic language models (gLMs) that controls for phylogenetic biases

Setup

To setup the environment for PhyloGPN, follow these instructions:

1. Install a Python virtual environment with the necessary packages and activate it by running python3 -m venv venv && source venv/bin/activate && pip install -r requirements.txt

2. Download the PhyloGPN checkpoint:

   • gdown "https://drive.google.com/uc?id=1MSxLYbZKSnWjbM_w1cHGVFffrwh8j64V" -O ./PhyloGPN/checkpoint.pt

For a tutorial on how to obtain likelihoods, rate parameters, and embeddings from PhyloGPN, refer to notebooks/example.ipynb

Embedding-Based Evaluation

For instructions on how to benchmark PhyloGPN and PhyloGPN-pooled in BEND, refer to notebooks/bend.ipynb

Likelihood-Based Evaluation

To reproduce our results for the variant effect prediction evaluation, follow these instructions:

1. Download the required raw data:

   • invoke download-hg38
   • invoke download-omim
   • invoke download-latest-clinvar
   • invoke download-dms-data
   • invoke download-and-process-gnomad

2. Process the raw data:

   • invoke chunk-hg38
   • invoke process-clinvar
   • invoke process-dms-data

3. Generate log likelihood ratios:

   • invoke generate-vep-results --model phylogpn
   • invoke generate-vep-results --model caduceus_131k
   • invoke generate-vep-results --model hyenadna_medium_160k
   • invoke generate-vep-results --model nucleotide_transformer_v2_500m

4. Process the results:

   • invoke merge-clinvar-results
   • invoke merge-dms-results
   • invoke merge-omim-results

The results should be in data/clinvar_eval.csv, data/omim_eval.csv, and data/dms_eval.csv.