Understanding the Natural Language of DNA using Encoder-Decoder Foundation Models with Byte-level Precision
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866911999748734976 |
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| author | Malusare, Aditya Kothandaraman, Harish Tamboli, Dipesh Lanman, Nadia A. Aggarwal, Vaneet |
| author_facet | Malusare, Aditya Kothandaraman, Harish Tamboli, Dipesh Lanman, Nadia A. Aggarwal, Vaneet |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_02333 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Understanding the Natural Language of DNA using Encoder-Decoder Foundation Models with Byte-level Precision Malusare, Aditya Kothandaraman, Harish Tamboli, Dipesh Lanman, Nadia A. Aggarwal, Vaneet Machine Learning Genomics 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. |
| title | Understanding the Natural Language of DNA using Encoder-Decoder Foundation Models with Byte-level Precision |
| topic | Machine Learning Genomics |
| url | https://arxiv.org/abs/2311.02333 |