title: Hidden Markov Models in Bioinformatics: SNV Inference from Next Generation Sequence.
abstract: The rapid development of next generation sequencing (NGS) technology provides a novel avenue for genomic exploration and research. Hidden Markov models (HMMs) have wide applications in pattern recognition as well as Bioinformatics such as transcription factor binding sites and cis-regulatory modules detection. An application of HMM is introduced in this chapter with the in-deep developing of NGS. Single nucleotide variants (SNVs) inferred from NGS are expected to reveal gene mutations in cancer. However, NGS has lower sequence coverage and poor SNV detection capability in the regulatory regions of the genome. A specific HMM is developed for this purpose to infer the genotype for each position on the genome by incorporating the mapping quality of each read and the corresponding base quality on the reads into the emission probability of HMM. The procedure and the implementation of the algorithm is presented in detail for understanding and programming.
authors.affiliation: School of Mathematics and Physics, China University of Geosciences, Wuhan, 430074, China.
authors.forename: Jiawen
authors.initials: J
authors.lastname: Bian
authors.affiliation: Department of Diagnostic Radiology, Center for Bioinformatics & Systems Biology, Wake Forest University-School of Medicine, Winston-Salem, 27103, NC, USA. xizhou@wakehealth.edu.
authors.forename: Xiaobo
authors.initials: X
authors.lastname: Zhou
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods; D023281:Genomics / Q000379:methods*; D005838:Genotype; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D008390:Markov Chains; D008958:Models, Molecular; D020641:Polymorphism, Single Nucleotide; D012045:Regulatory Sequences, Nucleic Acid; D017422:Sequence Analysis, DNA / Q000379:methods*
keywords: Hidden Markov model; Low sequencing depth; Next generation sequencing; Posterior probability; Single nucleotide variation