Ruei-Ting Chien, Mao-Jan Lin, Yang-Ming Yeh, Yi-Chang Lu
{"title":"Traceback Memory Reduction for Three-Sequence Alignment Algorithm with Affine Gap Models","authors":"Ruei-Ting Chien, Mao-Jan Lin, Yang-Ming Yeh, Yi-Chang Lu","doi":"10.23919/APSIPAASC55919.2022.9980113","DOIUrl":null,"url":null,"abstract":"In many hardware aligners, on-chip traceback is not supported because it requires large memory usage. The issue becomes even worse for three-sequence alignment, which is an algorithm to improve the accuracy of multiple sequence alignment. In this paper, we propose a design to reduce the usage of traceback memory for three-sequence alignment with affine gap penalty models. Using the pre-computed results from the forward dynamic programming stage, we are able to encode traceback directions with fewer bits. Our algorithm could save 37.5% memory usage when compared to direct implementations. The proposed bit-reduction method can be further combined with existing region-reduction traceback methods to lower required memory sizes.","PeriodicalId":382967,"journal":{"name":"2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","volume":"146 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/APSIPAASC55919.2022.9980113","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In many hardware aligners, on-chip traceback is not supported because it requires large memory usage. The issue becomes even worse for three-sequence alignment, which is an algorithm to improve the accuracy of multiple sequence alignment. In this paper, we propose a design to reduce the usage of traceback memory for three-sequence alignment with affine gap penalty models. Using the pre-computed results from the forward dynamic programming stage, we are able to encode traceback directions with fewer bits. Our algorithm could save 37.5% memory usage when compared to direct implementations. The proposed bit-reduction method can be further combined with existing region-reduction traceback methods to lower required memory sizes.