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• W2022651431 abstract "Both 454 and Ion Torrent sequencers are capable of producing large amounts of long high-quality sequencing reads. However, as both methods sequence homopolymers in one cycle, they both suffer from homopolymer uncertainty and incorporation asynchronization. In mapping, such sequencing errors could shift alignments around homopolymers and thus induce incorrect mismatches, which have become a critical barrier against the accurate detection of single nucleotide polymorphisms (SNPs). In this article, we propose a hidden Markov model (HMM) to statistically and explicitly formulate homopolymer sequencing errors by the overcall, undercall, insertion and deletion. We use a hierarchical model to describe the sequencing and base-calling processes, and we estimate parameters of the HMM from resequencing data by an expectation-maximization algorithm. Based on the HMM, we develop a realignment-based SNP-calling program, termed PyroHMMsnp, which realigns read sequences around homopolymers according to the error model and then infers the underlying genotype by using a Bayesian approach. Simulation experiments show that the performance of PyroHMMsnp is exceptional across various sequencing coverages in terms of sensitivity, specificity and F1 measure, compared with other tools. Analysis of the human resequencing data shows that PyroHMMsnp predicts 12.9% more SNPs than Samtools while achieving a higher specificity. (http://code.google.com/p/pyrohmmsnp/)." @default.
• W2022651431 created "2016-06-24" @default.
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• W2022651431 date "2013-05-21" @default.
• W2022651431 modified "2023-10-17" @default.
• W2022651431 title "PyroHMMsnp: an SNP caller for Ion Torrent and 454 sequencing data" @default.
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• W2022651431 doi "https://doi.org/10.1093/nar/gkt372" @default.
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