Jaime Lotero, Andrés Benavides, Aníbal Guerra, S. Isaza
{"title":"UdeAlignC: Fast Alignment for the Compression of DNA Reads","authors":"Jaime Lotero, Andrés Benavides, Aníbal Guerra, S. Isaza","doi":"10.1109/COLCOMCON.2018.8466336","DOIUrl":null,"url":null,"abstract":"Referential compression algorithms are one of the main strategies to cope with the exponential growth of DNA data available to scientists. One of the techniques used to build a referential compression is sequence alignment, which in turn requires a lot of computing. In this article we present UdeAlignC, a fast alignment algorithm for the compression of DNA reads. We demonstrate that our algorithm is 2× faster than prominent state of the art tools while optimality is only reduced by 5.6%. We also implement a GPU-accelerated version and show local speedups of up to 12×. Source code available at https://bitbucket.org/BioMRcomp/udealignc","PeriodicalId":151973,"journal":{"name":"2018 IEEE Colombian Conference on Communications and Computing (COLCOM)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE Colombian Conference on Communications and Computing (COLCOM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/COLCOMCON.2018.8466336","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
Referential compression algorithms are one of the main strategies to cope with the exponential growth of DNA data available to scientists. One of the techniques used to build a referential compression is sequence alignment, which in turn requires a lot of computing. In this article we present UdeAlignC, a fast alignment algorithm for the compression of DNA reads. We demonstrate that our algorithm is 2× faster than prominent state of the art tools while optimality is only reduced by 5.6%. We also implement a GPU-accelerated version and show local speedups of up to 12×. Source code available at https://bitbucket.org/BioMRcomp/udealignc