gLM2: A mixed-modality Genomic Language Model

Usage | Citing

gLM2 is a mixed-modality genomic language model, trained on the OMG Dataset.

The model encodes a genomic scaffold with both both amino-acid and DNA tokens. gLM2 is trained at two scales: 150M and 650M parameters.

Model Description

gLM2 is a transformer encoder trained with the masked language modeling objective.
It encodes a genomic contig as a sequence of protein coding sequences (CDS) and DNA inter-genic sequences (IGS).
CDS elements are tokenized using per-amino acid tokens, and IGS elements are tokenized using per-nucleotide tokens.

• To encode the genomic strand, we prepended each genomic element with a special token, either <+> or <-> to indicate the positive and negative strands.
• To avoid collision between amino acid and nucleotide tokens, the tokenizer expects all amino acids to be uppercase, and all nucleotides to be lowercase.

UPDATE(09/2024): We updated the model with longer context length (4096 tokens vs. 2048 tokens) and per-nucleotide IGS tokenization.

Usage

import torch
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained('tattabio/gLM2_650M', torch_dtype=torch.bfloat16, trust_remote_code=True).cuda()
tokenizer = AutoTokenizer.from_pretrained('tattabio/gLM2_650M', trust_remote_code=True)

# A contig with two proteins and an inter-genic sequence.
# NOTE: Nucleotides should always be lowercase, and prepended with `<+>`.
sequence = "<+>MALTKVEKRNRIKRRVRGKISGTQASPRLSVYKSNK<+>aatttaaggaa<->MLGIDNIERVKPGGLELVDRLVAVNRVTKVTKGGRAFGFSAIVVVGNED"

# Tokenize the sequence.
encodings = tokenizer([sequence], return_tensors='pt')
# Extract embeddings.
with torch.no_grad():
    embeddings = model(encodings.input_ids.cuda(), output_hidden_states=True).last_hidden_state

Scripts

We provide a script to visualize protein-protein interaction by computing the gLM2 categorical jacobian (Zhang et al. 2024).

For example, gLM2 correctly predicts interactions between 2ONK_A (ModA) and 2ONK_C (ModC). In comparison, ESM2 and Evo do not predict any interactions.

Training Data

gLM2 is trained on the OMG dataset. To improve the dataset balance and remove near-duplicate examples, the data is tokenized and pruned by applying Semantic Deduplication (SemDedup).
We use an embedding distance threshold of 2e-3, resulting in 49% of the dataset being pruned.

Citing

gLM2 was introduced in "The OMG dataset: An Open MetaGenomic corpus for mixed-modality genomic language modeling", feel free to cite:

@article{Cornman2024,
  title = {The OMG dataset: An Open MetaGenomic corpus for mixed-modality genomic language modeling},
  url = {https://www.biorxiv.org/content/early/2024/08/17/2024.08.14.607850},
  DOI = {10.1101/2024.08.14.607850},
  publisher = {Cold Spring Harbor Laboratory},
  author = {Cornman, Andre and West-Roberts, Jacob and Camargo, Antonio Pedro and Roux, Simon and Beracochea, Martin and Mirdita, Milot and Ovchinnikov, Sergey and Hwang, Yunha},
  year = {2024},
}