Embed-Search-Align: DNA Sequence Alignment using Transformer Models

• URL: http://arxiv.org/abs/2309.11087v5
• Date: Sat, 19 Oct 2024 00:17:16 GMT
• Title: Embed-Search-Align: DNA Sequence Alignment using Transformer Models
• Authors: Pavan Holur, K. C. Enevoldsen, Shreyas Rajesh, Lajoyce Mboning, Thalia Georgiou, Louis-S. Bouchard, Matteo Pellegrini, Vwani Roychowdhury,
• Abstract summary: We bridge the gap by framing the sequence alignment task for Transformer models as an "Embed-Search-Align" task. A novel Reference-Free DNA Embedding model generates embeddings of reads and reference fragments, which are projected into a shared vector space. DNA-ESA is 99% accurate when aligning 250-length reads onto a human genome (3gb), rivaling conventional methods such as Bowtie and BWA-Mem.
• Score: 2.48439258515764
• License:
• Abstract: DNA sequence alignment involves assigning short DNA reads to the most probable locations on an extensive reference genome. This process is crucial for various genomic analyses, including variant calling, transcriptomics, and epigenomics. Conventional methods, refined over decades, tackle this challenge in 2 steps: genome indexing followed by efficient search to locate likely positions for given reads. Building on the success of Large Language Models in encoding text into embeddings, where the distance metric captures semantic similarity, recent efforts have explored whether the same Transformer architecture can produce embeddings for DNA sequences. Such models have shown early promise in classifying short DNA sequences, such as detecting coding/non-coding regions, and enhancer, promoter sequences. However, performance at sequence classification tasks does not translate to sequence alignment, where it is necessary to search across the genome to align each read, a significantly longer-range task. We bridge this gap by framing the Sequence Alignment task for Transformer models as an "Embed-Search-Align" task. In this framework, a novel Reference-Free DNA Embedding model generates embeddings of reads and reference fragments, which are projected into a shared vector space where the read-fragment distance is used as a surrogate for alignment. Technical contributions include: (1) Contrastive loss for self-supervised training of DNA sequence representations, facilitating rich reference-free, sequence-level embeddings, and (2) a DNA vector store to enable search across fragments on a global scale. DNA-ESA is 99% accurate when aligning 250-length reads onto a human genome (3gb), rivaling conventional methods such as Bowtie and BWA-Mem. DNA-ESA exceeds the performance of 6 Transformer model baselines such as Nucleotide Transformer, Hyena-DNA, and shows task transfer across chromosomes and species.

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