Abstract
Hidden Markov models (HMMs) have been extensively used in biological sequence analysis. In this paper, we give a tutorial review of HMMs and their applications in a variety of problems in molecular biology. We especially focus on three types of HMMs: the profile-HMMs, pair-HMMs, and context-sensitive HMMs. We show how these HMMs can be used to solve various sequence analysis problems, such as pairwise and multiple sequence alignments, gene annotation, classification, similarity search, and many others.
Keywords: Hidden Markov model (HMM), pair-HMM, profile-HMM, context-sensitive HMM (csHMM), profile-csHMM, sequence analysis, scleroderma
Current Genomics
Title: Hidden Markov Models and their Applications in Biological Sequence Analysis
Volume: 10 Issue: 6
Author(s): Byung-Jun Yoon
Affiliation:
Keywords: Hidden Markov model (HMM), pair-HMM, profile-HMM, context-sensitive HMM (csHMM), profile-csHMM, sequence analysis, scleroderma
Abstract: Hidden Markov models (HMMs) have been extensively used in biological sequence analysis. In this paper, we give a tutorial review of HMMs and their applications in a variety of problems in molecular biology. We especially focus on three types of HMMs: the profile-HMMs, pair-HMMs, and context-sensitive HMMs. We show how these HMMs can be used to solve various sequence analysis problems, such as pairwise and multiple sequence alignments, gene annotation, classification, similarity search, and many others.
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Cite this article as:
Yoon Byung-Jun, Hidden Markov Models and their Applications in Biological Sequence Analysis, Current Genomics 2009; 10 (6) . https://dx.doi.org/10.2174/138920209789177575
DOI https://dx.doi.org/10.2174/138920209789177575 |
Print ISSN 1389-2029 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5488 |
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