title: Hidden Markov Models for Protein Domain Homology Identification and Analysis.
abstract: Protein domain identification and analysis are cornerstones of modern proteomics. The tools available to protein domain researchers avail a variety of approaches to understanding large protein domain families. Hidden Markov Models (HMM) form the basis for identifying and categorizing evolutionarily linked protein domains. Here I describe the use of HMM models for predicting and identifying Src Homology 2 (SH2) domains within the proteome.
authors.affiliation: Division of Emergency Medicine, Department of Medicine, University of Washington, 325 9th Ave., Seattle, WA, USA. kdj6@uw.edu.|Division of Biomedical and Health Informatics, Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA. kdj6@uw.edu.
authors.forename: Karl
authors.initials: K
authors.lastname: Jablonowski
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D019295:Computational Biology / Q000379:methods; D030562:Databases, Protein; D006801:Humans; D008390:Markov Chains; D010802:Phylogeny; D000072417:Protein Domains; D054730:Protein Interaction Domains and Motifs; D011506:Proteins / Q000737:chemistry* / Q000145:classification / Q000235:genetics / Q000378:metabolism*; D016415:Sequence Alignment; D017386:Sequence Homology, Amino Acid; D012984:Software; D018909:src Homology Domains
chemical_list: D011506:Proteins
keywords: Hidden Markov model; Neighbor-joining phylogenetic tree; SH2 domains; Sequence alignment