{"title":"TSTA：基于线程和 SIMD 的梯形配对/多序列比对方法。","authors":"Peiyu Zong, Wenpeng Deng, Jian Liu, Jue Ruan","doi":"10.46471/gigabyte.141","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p><p><strong>Availability and implementation: </strong>Source codes are available at https://github.com/bxskdh/TSTA.</p>","PeriodicalId":73157,"journal":{"name":"GigaByte (Hong Kong, China)","volume":"2024 ","pages":"gigabyte141"},"PeriodicalIF":0.0000,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11558659/pdf/","citationCount":"0","resultStr":"{\"title\":\"TSTA: thread and SIMD-based trapezoidal pairwise/multiple sequence-alignment method.\",\"authors\":\"Peiyu Zong, Wenpeng Deng, Jian Liu, Jue Ruan\",\"doi\":\"10.46471/gigabyte.141\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p><p><strong>Availability and implementation: </strong>Source codes are available at https://github.com/bxskdh/TSTA.</p>\",\"PeriodicalId\":73157,\"journal\":{\"name\":\"GigaByte (Hong Kong, China)\",\"volume\":\"2024 \",\"pages\":\"gigabyte141\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-11-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11558659/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"GigaByte (Hong Kong, China)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.46471/gigabyte.141\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"GigaByte (Hong Kong, China)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.46471/gigabyte.141","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"","JCRName":"","Score":null,"Total":0}
TSTA: thread and SIMD-based trapezoidal pairwise/multiple sequence-alignment method.
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.
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