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Learning the language of viral evolution and escape

This repository contains the analysis code, links to the data, and pretrained models for the paper "Learning the language of viral evolution and escape" by Brian Hie, Ellen Zhong, Bonnie Berger, and Bryan Bryson (2021).

Data

You can download the relevant datasets (including training and validation data) using the commands

wget http://cb.csail.mit.edu/cb/viral-mutation/data.tar.gz
tar xvf data.tar.gz

within the same directory as this repository.

Dependencies

The major Python package requirements and their tested versions are in requirements.txt.

Our experiments were run with Python version 3.7 on Ubuntu 18.04.

Experiments

Key results from our experiments can be found in the results/ directory and can be reproduced with the commands below. The models/ directory contains key pretrained models used in our analyses.

To run the experiments below, download the data (instructions above). Our experiments require a maximum of 400 GB of CPU RAM and 32 GB of GPU RAM (though often much less); in silico fitness and escape model inference can take around 35 minutes for influenza HA, 90 minutes for HIV Env, and 10 hours for SARS-CoV-2 Spike.

Influenza HA

Influenza HA semantic embedding UMAPs and log files with statistics can be generated with the command

python bin/flu.py bilstm --checkpoint models/flu.hdf5 --embed \
    > flu_embed.log 2>&1

Single-residue escape prediction using validation data from Doud et al. (2018) and Lee et al. (2019) can be done with the command

python bin/flu.py bilstm --checkpoint models/flu.hdf5 --semantics \
    > flu_semantics.log 2>&1

Combinatorial fitness experiments measuring correlation with grammaticality and semantic change using data from Doud and Bloom (2016) and from Wu et al. (2020) can be done with the command

python bin/flu.py bilstm --checkpoint models/flu.hdf5 --combfit \
    > flu_combfit.log 2>&1

Training a new model on flu HA sequences can be done with the command

python bin/flu.py bilstm --train --test \
    > flu_train.log 2>&1

HIV Env

HIV Env semantic embedding UMAPs and log files with statistics can be generated with the command

python bin/hiv.py bilstm --checkpoint models/hiv.hdf5 --embed \
    > hiv_embed.log 2>&1

Single-residue escape prediction using validation data from Dingens et al. (2019) can be done with the command

python bin/hiv.py bilstm --checkpoint models/hiv.hdf5 --semantics \
    > hiv_semantics.log 2>&1

Combinatorial fitness experiments measuring correlation with grammaticality and semantic change using data from Haddox et al. (2018) can be done with the command

python bin/hiv.py bilstm --checkpoint models/hiv.hdf5 --combfit \
    > hiv_combfit.log 2>&1

Training a new model on HIV Env sequences can be done with the command

python bin/hiv.py bilstm --train --test \
    > hiv_train.log 2>&1

SARS-CoV-2 Spike

Coronavirus spike semantic embedding UMAPs and log files with statistics can be generated with the command

python bin/cov.py bilstm --checkpoint models/cov.hdf5 --embed \
    > cov_embed.log 2>&1

Single-residue escape prediction using validation data from Baum et al. (2020) and DMS data from Greaney et al. (2020) can be done with the command

python bin/cov.py bilstm --checkpoint models/cov.hdf5 --semantics \
    > cov_semantics.log 2>&1

Combinatorial fitness experiments measuring correlation with grammaticality and semantic change using data from Starr et al. (2020) can be done with the command

python bin/cov.py bilstm --checkpoint models/cov.hdf5 --combfit \
    > cov_combfit.log 2>&1

Experiments measuring grammaticality and semantic change of a SARS-CoV-2 reinfection event documented by To et al. (2020) can be done with the command

python bin/cov.py bilstm --checkpoint models/cov.hdf5 --reinfection \
    > cov_reinfection.log 2>&1

Training a new model on coronavirus spike sequences can be done with the command

python bin/cov.py bilstm --train --test \
    > cov_train.log 2>&1

Omicron Spike experiments

Evaluating the Coronaviridae language model on major SARS-CoV-2 Spike variants, including Omicron, as well as on SARS-CoV-1 Spike, can be done with the commands

python bin/cov_fasta.py \
    examples/example_wt.fa \
    examples/example_target.fa \
    --checkpoint models/cov.hdf5 | tail -n+31 \
    > cov_fasta.log
python bin/plot_variants.py cov_fasta.log

Benchmarking experiments

Performing a sweep of escape cutoffs to compare the AUC of CSCS to that of baseline methods can be done with the command

bash bin/benchmark_escape.sh

Questions

For questions, please use the GitHub Discussions forum. For bugs or other problems, please file an issue.