GENA-LM (gena-lm-bigbird-base-t2t)
GENA-LM is a Family of Open-Source Foundational Models for Long DNA Sequences.
GENA-LM models are transformer masked language models trained on human DNA sequence.
gena-lm-bigbird-base-t2t follows the BigBird architecture and its HuggingFace implementation.
Differences between GENA-LM (gena-lm-bigbird-base-t2t) and DNABERT:
- BPE tokenization instead of k-mers;
- input sequence size is about 36000 nucleotides (4096 BPE tokens) compared to 512 nucleotides of DNABERT;
- pre-training on T2T vs. GRCh38.p13 human genome assembly.
Source code and data: https://github.com/AIRI-Institute/GENA_LM
Paper: https://academic.oup.com/nar/article/53/2/gkae1310/7954523
This repository also contains models that are finetuned on downstream tasks and models that are used in our GENA-Web web tool for genomic sequence annotation:
- splice sites prediction (branch gena_web_spliceai)
Examples
Load pre-trained model
from transformers import AutoTokenizer, BigBirdForMaskedLM
tokenizer = AutoTokenizer.from_pretrained('AIRI-Institute/gena-lm-bigbird-base-t2t')
model = BigBirdForMaskedLM.from_pretrained('AIRI-Institute/gena-lm-bigbird-base-t2t')
How to load the model to fine-tune it on classification task
from transformers import AutoTokenizer, BigBirdForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained('AIRI-Institute/gena-lm-bigbird-base-t2t')
model = BigBirdForSequenceClassification.from_pretrained('AIRI-Institute/gena-lm-bigbird-base-t2t')
Model description
GENA-LM (gena-lm-bigbird-base-t2t) model is trained in a masked language model (MLM) fashion, following the methods proposed in the BigBird paper by masking 15% of tokens. Model config for gena-lm-bigbird-base-t2t is similar to the google/bigbird-roberta-base:
- 4096 Maximum sequence length
- 12 Layers, 12 Attention heads
- 768 Hidden size
- sparse config:
- block size: 64
- random blocks: 3
- global blocks: 2
- sliding window blocks: 3
- 32k Vocabulary size, tokenizer trained on DNA data.
We pre-trained gena-lm-bigbird-base-t2t using the latest T2T human genome assembly (https://www.ncbi.nlm.nih.gov/assembly/GCA_009914755.3/). The data was augmented by sampling mutations from 1000-genome SNPs (gnomAD dataset). Pre-training was performed for 1,070,000 iterations with batch size 256.
Evaluation
For evaluation results, see our paper: https://academic.oup.com/nar/article/53/2/gkae1310/7954523
Citation
@article{GENA_LM,
author = {Fishman, Veniamin and Kuratov, Yuri and Shmelev, Aleksei and Petrov, Maxim and Penzar, Dmitry and Shepelin, Denis and Chekanov, Nikolay and Kardymon, Olga and Burtsev, Mikhail},
title = {GENA-LM: a family of open-source foundational DNA language models for long sequences},
journal = {Nucleic Acids Research},
volume = {53},
number = {2},
pages = {gkae1310},
year = {2025},
month = {01},
issn = {0305-1048},
doi = {10.1093/nar/gkae1310},
url = {https://doi.org/10.1093/nar/gkae1310},
eprint = {https://academic.oup.com/nar/article-pdf/53/2/gkae1310/61443229/gkae1310.pdf},
}