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 two steps: genome indexing followed by efficient search to locate likely
positions for given reads. Building on the success of Large Language Models
(LLM) in encoding text into embeddings, where the distance metric captures
semantic similarity, recent efforts have explored whether the same Transformer
architecture can produce numerical representations for DNA sequences. Such
models have shown early promise in tasks involving classification of short DNA
sequences, such as the detection of coding vs non-coding regions, as well as
the identification of enhancer and promoter sequences. Performance at sequence
classification tasks does not, however, translate to sequence alignment, where
it is necessary to conduct a genome-wide search to successfully align every
read. We address this open problem by framing it as an Embed-Search-Align task.
In this framework, a novel encoder model DNA-ESA generates representations of
reads and fragments of the reference, which are projected into a shared vector
space where the read-fragment distance is used as surrogate for alignment. In
particular, DNA-ESA introduces: (1) Contrastive loss for self-supervised
training of DNA sequence representations, facilitating rich sequence-level
embeddings, and (2) a DNA vector store to enable search across fragments on a
global scale. DNA-ESA is >97% accurate when aligning 250-length reads onto a
human reference genome of 3 gigabases (single-haploid), far exceeds the
performance of 6 recent DNA-Transformer model baselines and shows task transfer
across chromosomes and species.

DNA-ESA 是一种新颖的编码器模型，通过产生读数和参考片段的表示，将其投影到共享向量空间，其中读段的距离用作对准的替代指标。