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Abstract Details

Activity Number: 559
Type: Topic Contributed
Date/Time: Wednesday, August 1, 2012 : 2:00 PM to 3:50 PM
Sponsor: IMS
Abstract - #305708
Title: Base Calling and SNP Calling on Next-Generation Sequencing Data
Author(s): Xinping Cui*+ and Gabriel Murillo and Ping Ma and Na You
Companies: University of California at Riverside and University of California at Riverside and University of Illinois at Urbana-Champaign and Sun Yat-Sen University
Address: 1338 Olmsted Hall , Riverside, CA, , USA
Keywords: next generation sequencing ; base calling ; SNP calling ; random-effect-multivariate-mixture model

Recent advances in next-generation sequencing (NGS) technology now provide the potential to detect all single nucleotide polymorphisms (SNPs) including rare ones in a genomic region. The power of NGS based SNP detection is critically dependent upon the accuracy of base calling. In this talk, I will first discuss our recently developed base calling algorithm that was built on random-effect-multivariate-mixture-model. I will then discuss our new likelihood based SNP caller, Genotype Model Selection (GeMS), which accounts for genomic sample preparation errors as well as base-calling and alignment errors. We will demonstrate that the proposed base caller and SNP caller result in a significant improve in the power of SNP detection.

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