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7 - Hidden Markov models

from Part II - Fundamentals of Biological Sequence Analysis

Published online by Cambridge University Press:  28 September 2023

Veli Mäkinen ,
Djamal Belazzougui ,
Fabio Cunial  and
Alexandru I. Tomescu
Veli Mäkinen
Affiliation:
University of Helsinki
Djamal Belazzougui
Affiliation:
Centre de Recherche sur l’Information Scientifique et Technique (CERIST), Algiers
Fabio Cunial
Affiliation:
Broad Institute, Massachusetts
Alexandru I. Tomescu
Affiliation:
University of Helsinki
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Summary

Analysing the content of a biological sequence can often be modeled as a segmentation problem. For example, one may wish to segment a genome in coding and non-coding regions, where only the former are translated to proteins. Statistical features of what genes usually look like can be used to derive an optimization framework. This process can be formalized through hidden Markov models, and the underlying segmentation problem can be solved using dynamic programming. This chapter introduces the key methods related to such optimization.

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Chapter
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Genome-Scale Algorithm Design
Bioinformatics in the Era of High-Throughput Sequencing
 , pp. 129 - 142
Publisher: Cambridge University Press
Print publication year: 2023

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