FamSeq: A Variant Calling Program for Family-Based Sequencing Data Using Graphics Processing Units
Year: 2014
Type: article
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.
Source: PLoS Computational Biology
Authors: Gang Peng, Yu Fan, Wenyi Wang
Institution: The University of Texas MD Anderson Cancer Center
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,043
Awards: Personalized risk assessment for families with Li-Fraumeni Syndrome, G1901027039, G214409461, Statistical methods for genomic analysis of heterogeneous tumors, G3746653997, G4276892241, Bioinformatics Tools for Genomic Analysis of Tumor and Stromal Pathways in Cancer, Integrative Pipeline for Analysis & Translational Application of TCGA Data (GDAC), Continuation of Computational Cancer Biomedicine Training Program, G7782258788, G788993117, Cancer Center Support Grant
OpenAlex ID: W2025144591