Handbook of Statistical Genetics (Balding/Handbook of Statistical Genetics, Third Edition) || Bayesian Methods in Biological Sequence Analysis

✍ Scribed by Balding, D. J.; Bishop, M.; Cannings, C.

• Publisher: John Wiley & Sons, Ltd
• Year: 2008
• Weight: 311 KB
• Edition: 3
• Category: Article
• ISBN: 0470058307
• DOI: 10.1002/9780470061619.ch3

✦ Synopsis

Hidden Markov models, the expectation-maximization algorithm, and the Gibbs sampler were introduced for biological sequence analysis in early 1990s. Since then the use of formal statistical models and inference procedures has revolutionized the field of computational biology. This chapter reviews the hidden Markov and related models, as well as their Bayesian inference procedures and algorithms, for sequence alignments and gene regulatory binding motif discoveries. We emphasize that the combination of Markov chain Monte Carlo and dynamic-programming techniques often results in effective algorithms for nondeterministic polynomial (NP)-hard problems in sequence analysis. We will also discuss some recent approaches to infer regulatory modules and to combine expression data with sequence data.