GigabyteBioinformaticsPeer-reviewed journal article
TSTA: Thread and SIMD-Based Trapezoidal Pairwise/Multiple Sequence Alignment Method
Abstract
AbstractsThe 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 & ImplementationSource code are available at https://github.com/bxskdh/TSTA.
- assay: protein sequence
- research focus: sequence-to-function modeling
- research focus: benchmarking
- research focus: software / tool
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 3
- FWCI
- 0.41
- Field percentile
- top 45%
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