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.