HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide
Resolution
- URL: http://arxiv.org/abs/2306.15794v2
- Date: Tue, 14 Nov 2023 07:09:04 GMT
- Title: HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide
Resolution
- Authors: Eric Nguyen, Michael Poli, Marjan Faizi, Armin Thomas, Callum
Birch-Sykes, Michael Wornow, Aman Patel, Clayton Rabideau, Stefano Massaroli,
Yoshua Bengio, Stefano Ermon, Stephen A. Baccus, Chris R\'e
- Abstract summary: We present HyenaDNA, a genomic foundation model pretrained on the human reference genome with context lengths of up to 1 million tokens at the single nucleotide-level.
On fine-tuned benchmarks from the Nucleotide Transformer, HyenaDNA reaches state-of-the-art (SotA) on 12 of 18 datasets using a model with orders of magnitude less parameters and pretraining data.
- Score: 76.97231739317259
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Genomic (DNA) sequences encode an enormous amount of information for gene
regulation and protein synthesis. Similar to natural language models,
researchers have proposed foundation models in genomics to learn generalizable
features from unlabeled genome data that can then be fine-tuned for downstream
tasks such as identifying regulatory elements. Due to the quadratic scaling of
attention, previous Transformer-based genomic models have used 512 to 4k tokens
as context (<0.001% of the human genome), significantly limiting the modeling
of long-range interactions in DNA. In addition, these methods rely on
tokenizers or fixed k-mers to aggregate meaningful DNA units, losing single
nucleotide resolution where subtle genetic variations can completely alter
protein function via single nucleotide polymorphisms (SNPs). Recently, Hyena, a
large language model based on implicit convolutions was shown to match
attention in quality while allowing longer context lengths and lower time
complexity. Leveraging Hyena's new long-range capabilities, we present
HyenaDNA, a genomic foundation model pretrained on the human reference genome
with context lengths of up to 1 million tokens at the single nucleotide-level -
an up to 500x increase over previous dense attention-based models. HyenaDNA
scales sub-quadratically in sequence length (training up to 160x faster than
Transformer), uses single nucleotide tokens, and has full global context at
each layer. We explore what longer context enables - including the first use of
in-context learning in genomics. On fine-tuned benchmarks from the Nucleotide
Transformer, HyenaDNA reaches state-of-the-art (SotA) on 12 of 18 datasets
using a model with orders of magnitude less parameters and pretraining data. On
the GenomicBenchmarks, HyenaDNA surpasses SotA on 7 of 8 datasets on average by
+10 accuracy points. Code at https://github.com/HazyResearch/hyena-dna.
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