title: Computational genetics: finding protein function by nonhomology methods.
abstract: During the past year, computational methods have been developed that use the rapidly accumulating genomic data to discover protein function. The methods rely on properties shared by functionally related proteins other than sequence or structural similarity. Instead, these 'nonhomology' methods analyze patterns such as domain fusion, conserved gene position and gene co-inheritance and coexpression to identify protein-protein relationships. The methods can identify functions for proteins that are without characterized homologs and have been applied to genome-wide predictions of protein function.
authors.affiliation: Molecular Biology Institute, UCLA-DOE Laboratory of Structural Biology and Molecular Medicine, University of California Los Angeles, Protein Pathways Inc., PO Box 951570, Los Angeles, Los Angeles, CA 90095-1570, CA 90024, USA. marcotte@mbi.u.
authors.forename: E M
authors.initials: EM
authors.lastname: Marcotte
mesh_terms: D000818:Animals; D003198:Computer Simulation; D019143:Evolution, Molecular; D006801:Humans; D010802:Phylogeny; D011506:Proteins / Q000032:analysis / Q000235:genetics*; D017421:Sequence Analysis / Q000379:methods*; D017386:Sequence Homology, Amino Acid
chemical_list: D011506:Proteins