Scholarlink

<p>The recent emergence of long-read sequencing technologies has enabled substantial improvements in accuracy and reduced computational costs. Nonetheless, pairwise sequence alignment remains a time-consuming step in common bioinformatics pipelines, becoming a bottleneck in de novo whole-genome assembly. Speeding up this step requires heuristics and the development of memory-frugal and efficient implementations. A promising candidate for all of the above is Myers’ algorithm. However, the state-of-the-art implementations face scalability challenges when dealing with longer reads and large datasets. To address these challenges, we propose <i>SeqMatcher</i>, a fast and memory-frugal genomics sequence aligner. By leveraging the long registers of AVX-512, <i>SeqMatcher</i> reduces the data movement and memory footprint. In a comprehensive performance evaluation, <i>SeqMatcher</i> achieves speedups of up to 12.32x for the unbanded version and 26.70x for the banded version compared to the non-vectorized implementation, along with energy footprint reductions of up to 2.59x. It also outperforms state-of-the-art implementations by factors of up to 29.21x, 17.56x, 13.47x, 9.12x, and 8.81x compared to <i>Edlib</i>, <i>WFA2-lib</i>, <i>SeqAn</i>, <i>BSAlign</i>, and <i>QuickEd</i>, while improving energy consumption with reductions of up to 6.78x.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

SeqMatcher: efficient genome sequence matching with AVX-512 extensions

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2024年12月31日

查看全文
  • Elena Espinosa,
  • Ricardo Quislant,
  • Rafael Larrosa,
  • Oscar Plata

摘要

The recent emergence of long-read sequencing technologies has enabled substantial improvements in accuracy and reduced computational costs. Nonetheless, pairwise sequence alignment remains a time-consuming step in common bioinformatics pipelines, becoming a bottleneck in de novo whole-genome assembly. Speeding up this step requires heuristics and the development of memory-frugal and efficient implementations. A promising candidate for all of the above is Myers’ algorithm. However, the state-of-the-art implementations face scalability challenges when dealing with longer reads and large datasets. To address these challenges, we propose SeqMatcher, a fast and memory-frugal genomics sequence aligner. By leveraging the long registers of AVX-512, SeqMatcher reduces the data movement and memory footprint. In a comprehensive performance evaluation, SeqMatcher achieves speedups of up to 12.32x for the unbanded version and 26.70x for the banded version compared to the non-vectorized implementation, along with energy footprint reductions of up to 2.59x. It also outperforms state-of-the-art implementations by factors of up to 29.21x, 17.56x, 13.47x, 9.12x, and 8.81x compared to Edlib, WFA2-lib, SeqAn, BSAlign, and QuickEd, while improving energy consumption with reductions of up to 6.78x.