Saturday, January 28, 2012

Fwd: Genotype Calling from Next Generation Sequencing Data using Haplotype Information of Reads

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Genotype Calling from Next Generation Sequencing Data using Haplotype Information of Reads:
Motivation: Low coverage sequencing provides an economic strategy for whole genome sequencing. When sequencing a set of individuals, genotype calling can be challenging due to low sequencing coverage. Linkage disequilibrium (LD) based refinement of genotyping calling is essential to improve the accuracy. Current LD-based methods use read counts or genotype likelihoods at individual potential polymorphic sites. Reads that span multiple potential polymorphic sites (jumping reads) can provide additional haplotype information overlooked by current methods.
Results: In this paper we introduce a new Hidden Markov Model (HMM)-based method that can take into account jumping reads information across adjacent potential polymorphic sites and implement it in the HapSeq program. Our method extends the HMM in Thunder (Li, et al., 2010) and explicitly models jumping reads information as emission probabilities conditional on the states of adjacent potential polymorphic sites. Our simulation results show that, compared to Thunder, HapSeq reduces the genotyping error rate by 30%, from 0.86% to 0.60%. The results from the 1000 Genomes Project show that HapSeq reduces the genotyping error rate by 12% and 9%, from 2.24% and 2.76% to 1.97% and 2.50% for individuals with European and African ancestry, respectively. We expect our program can improve genotyping qualities of the large number of ongoing and planned whole genome sequencing projects.
Contact:dzhi@ms.soph.uab.edu and kzhang@ms.soph.uab.edu.

Availability: The software package HapSeq and its manual can be found and downloaded at www.ssg.uab.edu/hapseq/.


(Original Post: Bioinformatics - Advance Access.)