https://github.com/ramanathanlab/genslm

GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics
https://github.com/ramanathanlab/genslm

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

• Host: GitHub
• URL: https://github.com/ramanathanlab/genslm
• Owner: ramanathanlab
• License: mit
• Created: 2021-12-16T19:11:54.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2024-03-30T19:18:43.000Z (8 months ago)
• Last Synced: 2024-08-01T16:52:34.955Z (3 months ago)
• Language: Python
• Homepage:
• Size: 24.3 MB
• Stars: 114
• Watchers: 8
• Forks: 23
• Open Issues: 8
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE.md
  • Code of conduct: CODE_OF_CONDUCT.md

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README

        
# 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

## Table of Contents

1. [Installation](#installation)

2. [Usage](#usage)

3. [Contributing](#contributing)

4. [License](#license)

5. [Citations](#citations)

## Installation

To install `genslm` on most systems:

```bash

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

```

GenSLMs were trained on the [Polaris](https://www.alcf.anl.gov/polaris) and [Perlmutter](https://perlmutter.carrd.co/) supercomputers. For installation on these systems, please see [`INSTALL.md`](https://github.com/ramanathanlab/genslm/blob/main/docs/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](https://app.globus.org/file-manager?origin_id=25918ad0-2a4e-4f37-bcfc-8183b19c3150&origin_path=%2F).

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

### Compute embeddings [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ramanathanlab/genslm/blob/main/examples/embedding.ipynb)

```python

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 [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ramanathanlab/genslm/blob/main/examples/generate.ipynb)

```python

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`](https://github.com/ramanathanlab/genslm/blob/main/docs/COMMANDS.md).

## Contributing

Please report **bugs**, **enhancement requests**, or **questions** through the [Issue Tracker](https://github.com/ramanathanlab/genslm/issues).

If you are looking to contribute, please see [`CONTRIBUTING.md`](https://github.com/ramanathanlab/genslm/blob/main/CONTRIBUTING.md).

## License

genslm has a MIT license, as seen in the [`LICENSE.md`](https://github.com/ramanathanlab/genslm/blob/main/LICENSE.md) file.

## Citations

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

```bibtex

@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}

}

```