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.
Highlights
The successful completion of many genome sequencing projects has left us with an enormous amount of sequence data
After reviewing the basic concept of hidden Markov models (HMMs), we introduce three types of HMM variants, namely, profile-HMMs, pairHMMs, and context-sensitive HMMs, that have been useful in various sequence analysis problems
A hidden Markov model (HMM) is a statistical model that can be used to describe the evolution of observable events that depend on internal factors, which are not directly observable
Summary
The successful completion of many genome sequencing projects has left us with an enormous amount of sequence data. We give a tutorial review of HMMs and their applications in biological sequence analysis. Algorithms for solving these problems are introduced. We show how these models and other types of HMMs can be employed in RNA sequence analysis
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