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<p>Many sequence mapping tools for next-generation sequencing (NGS) have emerged over the past decades, but most are inefficient in mapping large-scale sequencing NGS data on high-performance servers. This paper proposes a novel and ultra-fast mapping algorithm, ScalableAligner (SA), designed for mapping short reads on multi-core servers. We optimize map-ping logic by combining Divide&amp;Conquer strategy, BWT-FM index, and auxiliary hash table to accelerate mapping while keeping mapping precision. We optimize software implementation with lock-free queues, lock-free thread pools, reclaimed memory blocks, separate read–write threads, and asynchronous IO to improve thread, memory, and IO bandwidth utilization. Comparative experiments with Kart, Bowtie2, Hisat2, BWA-MEM2, and Accel-Align show that SA achieves the fastest mapping speed while maintaining high sensitivity and accuracy on multiple datasets, with a speed improvement of 22–50% compared to other mapping algorithms. Meanwhile, SA maintains high-precision alignment at the fastest speed under a high mismatch rate. SA does not involve hardware and instruction set acceleration to run on different hardware platforms robustly while achieving higher thread scalability and better overall performance in multi-threaded environments. SA is available at <a href="https://github.com/langbi25/ScalableAligner.git">https://github.com/langbi25/ScalableAligner.git</a>.</p>

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ScalableAligner: a fast NGS mapping tool for shared-memory system

  • 期刊论文

  • 发表日期：

    2025年10月18日

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  • Deyou Tang,
  • Jialang Liang,
  • Pingjian Zhang,
  • Qingwen Deng

摘要

Many sequence mapping tools for next-generation sequencing (NGS) have emerged over the past decades, but most are inefficient in mapping large-scale sequencing NGS data on high-performance servers. This paper proposes a novel and ultra-fast mapping algorithm, ScalableAligner (SA), designed for mapping short reads on multi-core servers. We optimize map-ping logic by combining Divide&Conquer strategy, BWT-FM index, and auxiliary hash table to accelerate mapping while keeping mapping precision. We optimize software implementation with lock-free queues, lock-free thread pools, reclaimed memory blocks, separate read–write threads, and asynchronous IO to improve thread, memory, and IO bandwidth utilization. Comparative experiments with Kart, Bowtie2, Hisat2, BWA-MEM2, and Accel-Align show that SA achieves the fastest mapping speed while maintaining high sensitivity and accuracy on multiple datasets, with a speed improvement of 22–50% compared to other mapping algorithms. Meanwhile, SA maintains high-precision alignment at the fastest speed under a high mismatch rate. SA does not involve hardware and instruction set acceleration to run on different hardware platforms robustly while achieving higher thread scalability and better overall performance in multi-threaded environments. SA is available at https://github.com/langbi25/ScalableAligner.git.