metadata

license: cc-by-nc-sa-4.0
widget:
  - text: ACCTGA<mask>TTCTGAGTC
tags:
  - DNA
  - biology
  - genomics
datasets:
  - InstaDeepAI/true-cds-protein-tasks

nucleotide-transformer-v2-50m-multi-species

The Nucleotide Transformers are a collection of foundational language models that were pre-trained on DNA sequences from whole-genomes. Compared to other approaches, our models do not only integrate information from single reference genomes, but leverage DNA sequences from over 3,200 diverse human genomes, as well as 850 genomes from a wide range of species, including model and non-model organisms. Through robust and extensive evaluation, we show that these large models provide extremely accurate molecular phenotype prediction compared to existing methods

Part of this collection is the nucleotide-transformer-v2-50m-3mer-multi-species, a 50M parameters transformer pre-trained on a collection of 850 genomes from a wide range of species, including model and non-model organisms.

This model was trained as part of an effort to evaluate the capability of genomic language models on proteins. In this work, 3mer tokenization was consider as one potential architecture change that may improve fine-grained downstream protein prediction.

Developed by: InstaDeep

Model Sources

Repository: Nucleotide Transformer
Paper: Are Genomic Language Models All You Need? Exploring Genomic Language Models on Protein Downstream Tasks
Nucleotide Transformer Paper: The Nucleotide Transformer: Building and Evaluating Robust Foundation Models for Human Genomics

How to use

Until its next release, the transformers library needs to be installed from source with the following command in order to use the models:

pip install --upgrade git+https://github.com/huggingface/transformers.git

A small snippet of code is given here in order to retrieve both logits and embeddings from a dummy DNA sequence.

from transformers import AutoTokenizer, AutoModelForMaskedLM
import torch

# Import the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained("InstaDeepAI/nucleotide-transformer-v2-50m-3mer-multi-species", trust_remote_code=True)
model = AutoModelForMaskedLM.from_pretrained("InstaDeepAI/nucleotide-transformer-v2-50m-3mer-multi-species", trust_remote_code=True)

# Choose the length to which the input sequences are padded. By default, the 
# model max length is chosen, but feel free to decrease it as the time taken to 
# obtain the embeddings increases significantly with it.
max_length = tokenizer.model_max_length

# Create a dummy dna sequence and tokenize it
sequences = ["ATTCCGATTCCGATTCCG", "ATTTCTCTCTCTCTCTGAGATCGATCGATCGAT"]
tokens_ids = tokenizer.batch_encode_plus(sequences, return_tensors="pt", padding="max_length", max_length = max_length)["input_ids"]

# Compute the embeddings
attention_mask = tokens_ids != tokenizer.pad_token_id
torch_outs = model(
    tokens_ids,
    attention_mask=attention_mask,
    encoder_attention_mask=attention_mask,
    output_hidden_states=True
)

# Compute sequences embeddings
embeddings = torch_outs['hidden_states'][-1].detach().numpy()
print(f"Embeddings shape: {embeddings.shape}")
print(f"Embeddings per token: {embeddings}")

# Add embed dimension axis
attention_mask = torch.unsqueeze(attention_mask, dim=-1)

# Compute mean embeddings per sequence
mean_sequence_embeddings = torch.sum(attention_mask*embeddings, axis=-2)/torch.sum(attention_mask, axis=1)
print(f"Mean sequence embeddings: {mean_sequence_embeddings}")

Training data

The nucleotide-transformer-v2-50m-3mer-multi-species model was pretrained on a total of 850 genomes downloaded from NCBI. Plants and viruses are not included in these genomes, as their regulatory elements differ from those of interest in the paper's tasks. Some heavily studied model organisms were picked to be included in the collection of genomes, which represents a total of 174B nucleotides, i.e roughly 29B tokens. The data has been released as a HuggingFace dataset here.

Training procedure

Preprocessing

The DNA sequences are tokenized using the Nucleotide Transformer Tokenizer, which tokenizes sequences as 6-mers tokenizer when possible, otherwise tokenizing each nucleotide separately as described in the Tokenization section of the associated repository. This tokenizer has a vocabulary size of 4105. The inputs of the model are then of the form:

<CLS> <ACGTGT> <ACGTGC> <ACGGAC> <GACTAG> <TCAGCA>

The tokenized sequence have a maximum length of 1,000.

The masking procedure used is the standard one for Bert-style training:

15% of the tokens are masked.
In 80% of the cases, the masked tokens are replaced by [MASK].
In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
In the 10% remaining cases, the masked tokens are left as is.

Pretraining

The model was trained with 64 TPUv4s on 300B tokens, with an effective batch size of 1M tokens. The sequence length used was 1000 tokens. The Adam optimizer [38] was used with a learning rate schedule, and standard values for exponential decay rates and epsilon constants, β1 = 0.9, β2 = 0.999 and ε=1e-8. During a first warmup period, the learning rate was increased linearly between 5e-5 and 1e-4 over 16k steps before decreasing following a square root decay until the end of training.

Architecture

The model belongs to the second generation of nucleotide transformers, with the changes in architecture consisting the use of rotary positional embeddings instead of learned ones, as well as the introduction of Gated Linear Units.

BibTeX entry and citation info

@article{boshar2024glmsforproteins,
  title={Are Genomic Language Models All You Need? Exploring Genomic Language Models on Protein Downstream Tasks},
  author={Boshar, Sam and Trop, Evan and de Almeida, Bernardo and Copoiu, Liviu and Pierrot, Thomas},
  journal={bioRxiv},
  pages={2024--01},
  year={2024},
  publisher={Cold Spring Harbor Laboratory}
}