{"title":"将现场可编程门阵列引入基因型分期和估算。","authors":"Lars Wienbrandt, David Ellinghaus","doi":"10.1093/bioadv/vbae114","DOIUrl":null,"url":null,"abstract":"<p><strong>Summary: </strong>We recently developed <i>EagleImp</i>, a free software that combines genotype phasing and imputation in a single tool. By introducing algorithmic and technical improvements we accelerated the classical two-step approach using <i>Eagle2</i> and <i>PBWT</i>. Here, we demonstrate how to use field-programmable gate arrays (FPGAs) to accelerate <i>EagleImp</i> even further by a factor of up to 93% without loss of phasing and imputation quality. Due to the speed advantage over a not accelerated processor-based implementation, the FPGA extension of <i>EagleImp</i> allows the user to choose a more resource-intensive parameter setting in exchange for computation time to further improve phasing and imputation quality.</p><p><strong>Availability and implementation: </strong><i>EagleImp</i> and its FPGA extension are freely available at https://github.com/ikmb/eagleimp and https://github.com/ikmb/eagleimp-fpga.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11333566/pdf/","citationCount":"0","resultStr":"{\"title\":\"Introducing field-programmable gate arrays in genotype phasing and imputation.\",\"authors\":\"Lars Wienbrandt, David Ellinghaus\",\"doi\":\"10.1093/bioadv/vbae114\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Summary: </strong>We recently developed <i>EagleImp</i>, a free software that combines genotype phasing and imputation in a single tool. By introducing algorithmic and technical improvements we accelerated the classical two-step approach using <i>Eagle2</i> and <i>PBWT</i>. Here, we demonstrate how to use field-programmable gate arrays (FPGAs) to accelerate <i>EagleImp</i> even further by a factor of up to 93% without loss of phasing and imputation quality. Due to the speed advantage over a not accelerated processor-based implementation, the FPGA extension of <i>EagleImp</i> allows the user to choose a more resource-intensive parameter setting in exchange for computation time to further improve phasing and imputation quality.</p><p><strong>Availability and implementation: </strong><i>EagleImp</i> and its FPGA extension are freely available at https://github.com/ikmb/eagleimp and https://github.com/ikmb/eagleimp-fpga.</p>\",\"PeriodicalId\":72368,\"journal\":{\"name\":\"Bioinformatics advances\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2024-07-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11333566/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Bioinformatics advances\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1093/bioadv/vbae114\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"MATHEMATICAL & COMPUTATIONAL BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bioinformatics advances","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/bioadv/vbae114","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
Introducing field-programmable gate arrays in genotype phasing and imputation.
Summary: We recently developed EagleImp, a free software that combines genotype phasing and imputation in a single tool. By introducing algorithmic and technical improvements we accelerated the classical two-step approach using Eagle2 and PBWT. Here, we demonstrate how to use field-programmable gate arrays (FPGAs) to accelerate EagleImp even further by a factor of up to 93% without loss of phasing and imputation quality. Due to the speed advantage over a not accelerated processor-based implementation, the FPGA extension of EagleImp allows the user to choose a more resource-intensive parameter setting in exchange for computation time to further improve phasing and imputation quality.
Availability and implementation: EagleImp and its FPGA extension are freely available at https://github.com/ikmb/eagleimp and https://github.com/ikmb/eagleimp-fpga.