GigaByte (Nov 2024)

TSTA: thread and SIMD-based trapezoidal pairwise/multiple sequence-alignment method

  • Peiyu Zong ,
  • Wenpeng Deng ,
  • Jian Liu ,
  • Jue Ruan

DOI
https://doi.org/10.46471/gigabyte.141

Abstract

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The rapid advancements in sequencing length necessitate the adoption of increasingly efficient sequence alignment algorithms. The Needleman–Wunsch method introduces the foundational dynamic-programming matrix calculation for global alignment, which evaluates the overall alignment of sequences. However, this method is known to be highly time-consuming. The proposed TSTA algorithm leverages both vector-level and thread-level parallelism to accelerate pairwise and multiple sequence alignments. Availability and implementation Source codes are available at https://github.com/bxskdh/TSTA.