Hidden Markov Models and their Applications in Biological Sequence Analysis

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-sensitiv...

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Detalles Bibliográficos
Autor principal: Yoon, Byung-Jun
Formato: Texto
Lenguaje:English
Publicado: Bentham Science Publishers Ltd. 2009
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2766791/
https://www.ncbi.nlm.nih.gov/pubmed/20190955
http://dx.doi.org/10.2174/138920209789177575
Descripción
Sumario: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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