ISCB-Asia/SCCG 2012, BGI Special Session
Junwen John Wang (王俊文)
The University of Hong Kong
A fast and accurate SNP detection algorithm for next-generation-sequencing data
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Abstract
Various methods have been developed for calling single nucleotide polymorphisms (SNPs) from next-generation sequencing (NGS) data. However, for satisfactory performance, most of these methods require expensive high-depth sequencing. Here, we propose a fast and accurate SNP detection (FaSD) program that uses a binomial distribution based algorithm and a mutation probability. We extensively assess this program on normal and cancer NGS data from The Cancer Genome Atlas project and pooled data from the 1000 Genomes Project. We also compare the performance of several state-of-the-art programs for SNP calling and evaluate their pros and cons. We demonstrate that FaSD is a fast and highly accurate SNP detection method, particularly when the sequence depth is low. FaSD can finish SNP calling within four hours for ten-fold human genome NGS data (30 gigabases) on a standard desktop computer.
Biography
Dr. Junwen John Wang is currently an Assistant Professor at Department of Biochemistry and Centre for Genome Sciences, the University of Hong Kong. He was trained as Food Engineer in China, obtained his undergraduate from Huazhong Agricultural University and master degree from Jiannan University. After a short experience in Food industry in Shanghai, he went to USA to obtain PhD from the University of Washington and MS from the University of Pennsylvania. He was a postdoctoral fellow at Center for Bioinformatics, University of Pennsylvania, and a staff scientist at the National Institute of Cancer of USA before his current position.