The increasing popularity of profile/hidden Markov model (HMM) methods has led to an increase in the number of databases that support these algorithms. Although most bioinformatics books reference some of these available methods, few mention any databases other than PFAM. Offering practical advice that draws on the author's own experience, the Handbook of Hidden Markov Models in Bioinformatics discusses how to effectively use these databases and methods in order to identify features of biological interest. This comprehensive guide includes links to additional resources in each chapter and a CD-ROM with related material and programs.
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