HybriDNA: A Hybrid Transformer-Mamba2 Long-Range DNA Language Model
Journal:
arXiv
Published Date:
Feb 15, 2025
Abstract
Advances in natural language processing and large language models have
sparked growing interest in modeling DNA, often referred to as the "language of
life". However, DNA modeling poses unique challenges. First, it requires the
ability to process ultra-long DNA sequences while preserving single-nucleotide
resolution, as individual nucleotides play a critical role in DNA function.
Second, success in this domain requires excelling at both generative and
understanding tasks: generative tasks hold potential for therapeutic and
industrial applications, while understanding tasks provide crucial insights
into biological mechanisms and diseases. To address these challenges, we
propose HybriDNA, a decoder-only DNA language model that incorporates a hybrid
Transformer-Mamba2 architecture, seamlessly integrating the strengths of
attention mechanisms with selective state-space models. This hybrid design
enables HybriDNA to efficiently process DNA sequences up to 131kb in length
with single-nucleotide resolution. HybriDNA achieves state-of-the-art
performance across 33 DNA understanding datasets curated from the BEND, GUE,
and LRB benchmarks, and demonstrates exceptional capability in generating
synthetic cis-regulatory elements (CREs) with desired properties. Furthermore,
we show that HybriDNA adheres to expected scaling laws, with performance
improving consistently as the model scales from 300M to 3B and 7B parameters.
These findings underscore HybriDNA's versatility and its potential to advance
DNA research and applications, paving the way for innovations in understanding
and engineering the "language of life".
