Understanding the Natural Language of DNA using Encoder-Decoder Foundation Models with Byte-level Precision

• URL: http://arxiv.org/abs/2311.02333v2
• Date: Wed, 14 Feb 2024 03:48:41 GMT
• Title: Understanding the Natural Language of DNA using Encoder-Decoder Foundation Models with Byte-level Precision
• Authors: Aditya Malusare and Harish Kothandaraman and Dipesh Tamboli and Nadia A. Lanman and Vaneet Aggarwal
• Abstract summary: This paper presents the Ensemble Nucleotide Byte-level-Decoder (ENBED) foundation model, analyzing DNA sequences at byte-level precision with an encoder-decoder Transformer architecture. We use Masked Language Modeling to pre-train the foundation model using reference genome sequences and apply it in the following downstream tasks. In each of these tasks, we demonstrate significant improvement as compared to the existing state-of-the-art results.
• Score: 27.66444598769979
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents the Ensemble Nucleotide Byte-level Encoder-Decoder (ENBED) foundation model, analyzing DNA sequences at byte-level precision with an encoder-decoder Transformer architecture. ENBED uses a sub-quadratic implementation of attention to develop an efficient model capable of sequence-to-sequence transformations, generalizing previous genomic models with encoder-only or decoder-only architectures. We use Masked Language Modeling to pre-train the foundation model using reference genome sequences and apply it in the following downstream tasks: (1) identification of enhancers, promotors and splice sites, (2) recognition of sequences containing base call mismatches and insertion/deletion errors, an advantage over tokenization schemes involving multiple base pairs, which lose the ability to analyze with byte-level precision, (3) identification of biological function annotations of genomic sequences, and (4) generating mutations of the Influenza virus using the encoder-decoder architecture and validating them against real-world observations. In each of these tasks, we demonstrate significant improvement as compared to the existing state-of-the-art results.

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