Subfamily hmms in functional genomics.
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.
Brown
Duncan
D
Department of Bioengineering, University of California, Berkeley, CA 94720, USA.
Krishnamurthy
Nandini
N
Dale
Joseph M
JM
Christopher
Wayne
W
Sjölander
Kimmen
K
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
D004798:Enzymes; D011506:Proteins