Abstracts
The rapid advancements in sequencing length necessitate the adoption of increasingly efficient sequence alignment algorithms. The Needleman-Wunsch method introduces the foundational dynamic programming (DP) 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 & Implementation Source code are available at https://github.com/bxskdh/TSTA.
Competing Interest Statement
The authors have declared no competing interest.
DATA AVAILABILITY
All datasets are available at https://figshare.com/s/5a1b0c89ff00ce83e804.
Copyright
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
Posted September 22, 2024.
TSTA: Thread and SIMD-Based Trapezoidal Pairwise/Multiple Sequence Alignment Method
