Omni-DNA: A Unified Genomic Foundation Model for Cross-Modal and Multi-Task Learning
Journal:
arXiv
Published Date:
Feb 5, 2025
Abstract
Large Language Models (LLMs) demonstrate remarkable generalizability across
diverse tasks, yet genomic foundation models (GFMs) still require separate
finetuning for each downstream application, creating significant overhead as
model sizes grow. Moreover, existing GFMs are constrained by rigid output
formats, limiting their applicability to various genomic tasks. In this work,
we revisit the transformer-based auto-regressive models and introduce Omni-DNA,
a family of cross-modal multi-task models ranging from 20 million to 1 billion
parameters. Our approach consists of two stages: (i) pretraining on DNA
sequences with next token prediction objective, and (ii) expanding the
multi-modal task-specific tokens and finetuning for multiple downstream tasks
simultaneously. When evaluated on the Nucleotide Transformer and GB benchmarks,
Omni-DNA achieves state-of-the-art performance on 18 out of 26 tasks. Through
multi-task finetuning, Omni-DNA addresses 10 acetylation and methylation tasks
at once, surpassing models trained on each task individually. Finally, we
design two complex genomic tasks, DNA2Function and Needle-in-DNA, which map DNA
sequences to textual functional descriptions and images, respectively,
indicating Omni-DNA's cross-modal capabilities to broaden the scope of genomic
applications. All the models are available through
https://huggingface.co/collections/zehui127
