A Unified Algorithmic Framework for Biological Sequence Alignment

Sequence alignment is pivotal in both homology searches and the mapping of reads from next-generation sequencing (NGS) and third-generation sequencing (TGS) technologies. Currently, the majority of sequence alignment algorithms utilize the “seed-and-extend” paradigm, designed to filter out unrelated or nonhomologous sequences when no highly similar subregions are detected. A well-known implementation of this paradigm is BLAST, one of the most widely used multipurpose aligners. Over time, this paradigm has been optimized in various ways to suit different alignment tasks. However, while these specialized aligners often deliver high performance and efficiency, they are typically restricted to one or few alignment applications. To the best of our knowledge, no existing aligner can perform all alignment tasks while maintaining superior performance and efficiency.

In this work, we introduce a unified sequence alignment framework to address this limitation. Our alignment framework is built on the seed-and-extend paradigm but incorporates novel designs in its seeding and indexing components to maximize both flexibility and efficiency. The resulting software, the Versatile Alignment Toolkit (VAT), allows the users to switch seamlessly between nearly all major alignment tasks through command-line parameter configuration. VAT was rigorously benchmarked against leading aligners for DNA and protein homolog searches, NGS and TGS read mapping, and whole-genome alignment. The results demonstrated VAT’s top-tier performance across all benchmarks, underscoring the feasibility of using a unified algorithmic framework to handle diverse alignment tasks. VAT can simplify and standardize bioinformatic analysis workflows that involve multiple alignment tasks.