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FamSeq: A Variant Calling Program for Family-Based Sequencing Data Using Graphics Processing Units

Various algorithms have been developed for variant calling using next-generation sequencing data, and various methods have been applied to reduce the associated false positive and false negative rates. Few variant calling programs, however, utilize the pedigree information when the family-based sequ...

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Detalles Bibliográficos
Autores principales: Peng, Gang, Fan, Yu, Wang, Wenyi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4214554/
https://www.ncbi.nlm.nih.gov/pubmed/25357123
http://dx.doi.org/10.1371/journal.pcbi.1003880
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author Peng, Gang
Fan, Yu
Wang, Wenyi
author_facet Peng, Gang
Fan, Yu
Wang, Wenyi
author_sort Peng, Gang
collection PubMed
description Various algorithms have been developed for variant calling using next-generation sequencing data, and various methods have been applied to reduce the associated false positive and false negative rates. Few variant calling programs, however, utilize the pedigree information when the family-based sequencing data are available. Here, we present a program, FamSeq, which reduces both false positive and false negative rates by incorporating the pedigree information from the Mendelian genetic model into variant calling. To accommodate variations in data complexity, FamSeq consists of four distinct implementations of the Mendelian genetic model: the Bayesian network algorithm, a graphics processing unit version of the Bayesian network algorithm, the Elston-Stewart algorithm and the Markov chain Monte Carlo algorithm. To make the software efficient and applicable to large families, we parallelized the Bayesian network algorithm that copes with pedigrees with inbreeding loops without losing calculation precision on an NVIDIA graphics processing unit. In order to compare the difference in the four methods, we applied FamSeq to pedigree sequencing data with family sizes that varied from 7 to 12. When there is no inbreeding loop in the pedigree, the Elston-Stewart algorithm gives analytical results in a short time. If there are inbreeding loops in the pedigree, we recommend the Bayesian network method, which provides exact answers. To improve the computing speed of the Bayesian network method, we parallelized the computation on a graphics processing unit. This allowed the Bayesian network method to process the whole genome sequencing data of a family of 12 individuals within two days, which was a 10-fold time reduction compared to the time required for this computation on a central processing unit.
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spelling pubmed-42145542014-11-05 FamSeq: A Variant Calling Program for Family-Based Sequencing Data Using Graphics Processing Units Peng, Gang Fan, Yu Wang, Wenyi PLoS Comput Biol Research Article Various algorithms have been developed for variant calling using next-generation sequencing data, and various methods have been applied to reduce the associated false positive and false negative rates. Few variant calling programs, however, utilize the pedigree information when the family-based sequencing data are available. Here, we present a program, FamSeq, which reduces both false positive and false negative rates by incorporating the pedigree information from the Mendelian genetic model into variant calling. To accommodate variations in data complexity, FamSeq consists of four distinct implementations of the Mendelian genetic model: the Bayesian network algorithm, a graphics processing unit version of the Bayesian network algorithm, the Elston-Stewart algorithm and the Markov chain Monte Carlo algorithm. To make the software efficient and applicable to large families, we parallelized the Bayesian network algorithm that copes with pedigrees with inbreeding loops without losing calculation precision on an NVIDIA graphics processing unit. In order to compare the difference in the four methods, we applied FamSeq to pedigree sequencing data with family sizes that varied from 7 to 12. When there is no inbreeding loop in the pedigree, the Elston-Stewart algorithm gives analytical results in a short time. If there are inbreeding loops in the pedigree, we recommend the Bayesian network method, which provides exact answers. To improve the computing speed of the Bayesian network method, we parallelized the computation on a graphics processing unit. This allowed the Bayesian network method to process the whole genome sequencing data of a family of 12 individuals within two days, which was a 10-fold time reduction compared to the time required for this computation on a central processing unit. Public Library of Science 2014-10-30 /pmc/articles/PMC4214554/ /pubmed/25357123 http://dx.doi.org/10.1371/journal.pcbi.1003880 Text en © 2014 Peng et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Peng, Gang
Fan, Yu
Wang, Wenyi
FamSeq: A Variant Calling Program for Family-Based Sequencing Data Using Graphics Processing Units
title FamSeq: A Variant Calling Program for Family-Based Sequencing Data Using Graphics Processing Units
title_full FamSeq: A Variant Calling Program for Family-Based Sequencing Data Using Graphics Processing Units
title_fullStr FamSeq: A Variant Calling Program for Family-Based Sequencing Data Using Graphics Processing Units
title_full_unstemmed FamSeq: A Variant Calling Program for Family-Based Sequencing Data Using Graphics Processing Units
title_short FamSeq: A Variant Calling Program for Family-Based Sequencing Data Using Graphics Processing Units
title_sort famseq: a variant calling program for family-based sequencing data using graphics processing units
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4214554/
https://www.ncbi.nlm.nih.gov/pubmed/25357123
http://dx.doi.org/10.1371/journal.pcbi.1003880
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