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GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics

Preprint

Available here: https://www.biorxiv.org/content/10.1101/2022.10.10.511571v2

Installation
Usage
Contributing
License
Citations

Installation

To install genslm on most systems:

pip install git+https://github.com/ramanathanlab/genslm

GenSLMs were trained on the Polaris and Perlmutter supercomputers. For installation on these systems, please see INSTALL.md.

Usage

:warning: Model weights will be unavailable May 5, 2023 to May 12, 2023

:warning: Model weights downloaded prior to May 3, 2023 have a small issue in name space. Please redownload models for fix.

Our pre-trained models and datasets can be downloaded from this Globus Endpoint.

Use GenSLMs to compute sequence embeddings for downsteam tasks, generate synthetic sequences, or easily extend them to your own application.

Compute embeddings

import torch
import numpy as np
from torch.utils.data import DataLoader
from genslm import GenSLM, SequenceDataset

# Load model
model = GenSLM("genslm_25M_patric", model_cache_dir="/content/gdrive/MyDrive")
model.eval()

# Select GPU device if it is available, else use CPU
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)

# Input data is a list of gene sequences
sequences = [
    "ATGAAAGTAACCGTTGTTGGAGCAGGTGCAGTTGGTGCAAGTTGCGCAGAATATATTGCA",
    "ATTAAAGATTTCGCATCTGAAGTTGTTTTGTTAGACATTAAAGAAGGTTATGCCGAAGGT",
]

dataset = SequenceDataset(sequences, model.seq_length, model.tokenizer)
dataloader = DataLoader(dataset)

# Compute averaged-embeddings for each input sequence
embeddings = []
with torch.no_grad():
    for batch in dataloader:
        outputs = model(
            batch["input_ids"].to(device),
            batch["attention_mask"].to(device),
            output_hidden_states=True,
        )
        # outputs.hidden_states shape: (layers, batch_size, sequence_length, hidden_size)
        # Use the embeddings of the last layer
        emb = outputs.hidden_states[-1].detach().cpu().numpy()
        # Compute average over sequence length
        emb = np.mean(emb, axis=1)
        embeddings.append(emb)

# Concatenate embeddings into an array of shape (num_sequences, hidden_size)
embeddings = np.concatenate(embeddings)
embeddings.shape
>>> (2, 512)

Generate synthetic sequences

from genslm import GenSLM

# Load model
model = GenSLM("genslm_25M_patric", model_cache_dir="/content/gdrive/MyDrive")
model.eval()

# Select GPU device if it is available, else use CPU
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)

# Prompt the language model with a start codon
prompt = model.tokenizer.encode("ATG", return_tensors="pt").to(device)

tokens = model.model.generate(
    prompt,
    max_length=10,  # Increase this to generate longer sequences
    min_length=10,
    do_sample=True,
    top_k=50,
    top_p=0.95,
    num_return_sequences=2,  # Change the number of sequences to generate
    remove_invalid_values=True,
    use_cache=True,
    pad_token_id=model.tokenizer.encode("[PAD]")[0],
    temperature=1.0,
)

sequences = model.tokenizer.batch_decode(tokens, skip_special_tokens=True)

for sequence in sequences:
    print(sequence)

>>> ATG GTT ATT TCA TCT GAT TTA CCA ACT
>>> ATG TTC ATT CTT CCG GCA CTT ATC GAA

Diffusion Model

A novel hierarchical language model with two levels: the top level uses a diffusion model to capture global context and longer-range interactions across the entire genome sequence; the bottom level uses a transformer for codon-level modeling, guided by the top-level diffusion model. This model enables us to prospectively model SARS-CoV-2 evolution by leveraging its generative capabilities.

Please refer to this codebase for diffusion model usage: https://github.com/da03/hierarchical_diffusion_LM

High Performance Computing

We have a CLI tool to make it easier to launch training jobs on various HPC platforms. You can specify which system you would like to submit to by specifiying the -T, --template option. We currently have templates for polaris and perlmutter. By default, submitted jobs will output results to the directory where the submit command was run, you can use the -w option to specifiy a different workdir. Please run python -m genslm.hpc.submit --help for more information. See config.py for documentation on the yaml options, and note that config.yaml paths MUST be absolute.

module load conda/2022-07-19
conda activate genslm
python -m genslm.hpc.submit -T polaris -a gpu_hack -q debug -t 00:10:00 -n 1 -j test-job-0 -v "-c config.yaml"

Module specific arguments are passed verbatim by the -v flag, args must be inside quotes.

For additional commands, please see COMMANDS.md.

Contributing

Please report bugs, enhancement requests, or questions through the Issue Tracker.

If you are looking to contribute, please see CONTRIBUTING.md.

License

genslm has a MIT license, as seen in the LICENSE.md file.

Citations

If you use our models in your research, please cite this paper:

@article{zvyagin2022genslms,
  title={GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics.},
  author={Zvyagin, Max T and Brace, Alexander and Hippe, Kyle and Deng, Yuntian and Zhang, Bin and Bohorquez, Cindy Orozco and Clyde, Austin and Kale, Bharat and Perez-Rivera, Danilo and Ma, Heng and others},
  journal={bioRxiv},
  year={2022},
  publisher={Cold Spring Harbor Laboratory}
}