title: Subfamily hmms in functional genomics.
abstract: The limitations of homology-based methods for prediction of protein molecular function are well known; differences in domain structure, gene duplication events and errors in existing database annotations complicate this process. In this paper we present a method to detect and model protein subfamilies, which can be used in high-throughput, genome-scale phylogenomic inference of protein function. We demonstrate the method on a set of nine PFAM families, and show that subfamily HMMs provide greater separation of homologs and non-homologs than is possible with a single HMM for each family. We also show that subfamily HMMs can be used for functional classification with a very low expected error rate. The BETE method for identifying functional subfamilies is illustrated on a set of serotonin receptors.
authors.affiliation: Department of Bioengineering, University of California, Berkeley, CA 94720, USA.
authors.forename: Duncan
authors.initials: D
authors.lastname: Brown
authors.forename: Nandini
authors.initials: N
authors.lastname: Krishnamurthy
authors.forename: Joseph M
authors.initials: JM
authors.lastname: Dale
authors.forename: Wayne
authors.initials: W
authors.lastname: Christopher
authors.forename: Kimmen
authors.initials: K
authors.lastname: Sjölander
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D005075:Biological Evolution; D030561:Databases, Nucleic Acid; D030562:Databases, Protein; D004798:Enzymes / Q000235:genetics; D020440:Gene Duplication; D023281:Genomics; D008390:Markov Chains; D008957:Models, Genetic; D010802:Phylogeny; D011506:Proteins / Q000737:chemistry / Q000235:genetics; D016415:Sequence Alignment
chemical_list: D004798:Enzymes; D011506:Proteins