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FamSeq: a variant calling program for family-based sequencing data using graphics processing units.

Peng G, Fan Y, Wang W - PLoS Comput. Biol. (2014)

Bottom Line: 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.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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America.

ABSTRACT
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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Workflow of FamSeq.We use a pedigree file and a file that includes the likelihood () as the input to estimate the posterior probability () for each variant genotype. (E-S: Elston-Stewart algorithm; BN: Bayesian network method; BN-GPU: The computer needs a GPU card installed to run the GPU version of the Bayesian network method; MCMC: Markov chain Monte Carlo method; VCF: variant call format.)
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pcbi-1003880-g001: Workflow of FamSeq.We use a pedigree file and a file that includes the likelihood () as the input to estimate the posterior probability () for each variant genotype. (E-S: Elston-Stewart algorithm; BN: Bayesian network method; BN-GPU: The computer needs a GPU card installed to run the GPU version of the Bayesian network method; MCMC: Markov chain Monte Carlo method; VCF: variant call format.)

Mentions: As outlined in the workflow of FamSeq (Fig. 1), two files are required as data input: a pedigree structure file and a file containing the genotype likelihood , where D denotes the raw sequencing measurements, i.e., read counts, read quality and mapping quality, and G denotes the genotype of the individual. The pedigree file stores the individual identification (ID), parents' IDs, and gender and sample name, as is used to denote samples in the likelihood data file (Fig. 2). FamSeq accepts likelihood data files in two formats: a variant call format (VCF) [21] and a likelihood-only format (see description in our software manual). We introduced the likelihood-only format to allow for data generated from other sequencing platforms, with the requirement that the likelihood for each genotype is available.


FamSeq: a variant calling program for family-based sequencing data using graphics processing units.

Peng G, Fan Y, Wang W - PLoS Comput. Biol. (2014)

Workflow of FamSeq.We use a pedigree file and a file that includes the likelihood () as the input to estimate the posterior probability () for each variant genotype. (E-S: Elston-Stewart algorithm; BN: Bayesian network method; BN-GPU: The computer needs a GPU card installed to run the GPU version of the Bayesian network method; MCMC: Markov chain Monte Carlo method; VCF: variant call format.)
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4214554&req=5

pcbi-1003880-g001: Workflow of FamSeq.We use a pedigree file and a file that includes the likelihood () as the input to estimate the posterior probability () for each variant genotype. (E-S: Elston-Stewart algorithm; BN: Bayesian network method; BN-GPU: The computer needs a GPU card installed to run the GPU version of the Bayesian network method; MCMC: Markov chain Monte Carlo method; VCF: variant call format.)
Mentions: As outlined in the workflow of FamSeq (Fig. 1), two files are required as data input: a pedigree structure file and a file containing the genotype likelihood , where D denotes the raw sequencing measurements, i.e., read counts, read quality and mapping quality, and G denotes the genotype of the individual. The pedigree file stores the individual identification (ID), parents' IDs, and gender and sample name, as is used to denote samples in the likelihood data file (Fig. 2). FamSeq accepts likelihood data files in two formats: a variant call format (VCF) [21] and a likelihood-only format (see description in our software manual). We introduced the likelihood-only format to allow for data generated from other sequencing platforms, with the requirement that the likelihood for each genotype is available.

Bottom Line: 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.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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America.

ABSTRACT
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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