title: Comparative homology agreement search: an effective combination of homology-search methods.
abstract: Many methods have been developed to search for homologous members of a protein family in databases, and the reliability of results and conclusions may be compromised if only one method is used, neglecting the others. Here we introduce a general scheme for combining such methods. Based on this scheme, we implemented a tool called comparative homology agreement search (chase) that integrates different search strategies to obtain a combined "E value." Our results show that a consensus method integrating distinct strategies easily outperforms any of its component algorithms. More specifically, an evaluation based on the Structural Classification of Proteins database reveals that, on average, a coverage of 47% can be obtained in searches for distantly related homologues (i.e., members of the same superfamily but not the same family, which is a very difficult task), accepting only 10 false positives, whereas the individual methods obtain a coverage of 28-38%.
authors.affiliation: International NRW Graduate School in Bioinformatics and Genome Research, Center of Biotechnology, Bielefeld University, 33615 Bielefeld, Germany.
authors.forename: Intikhab
authors.initials: I
authors.lastname: Alam
authors.forename: Andreas
authors.initials: A
authors.lastname: Dress
authors.forename: Marc
authors.initials: M
authors.lastname: Rehmsmeier
authors.forename: Georg
authors.initials: G
authors.lastname: Fuellen
mesh_terms: D030562:Databases, Protein; D004798:Enzymes / Q000737:chemistry / Q000235:genetics; D019143:Evolution, Molecular; D005189:False Positive Reactions; D010802:Phylogeny; D011506:Proteins / Q000737:chemistry / Q000235:genetics; D015203:Reproducibility of Results
chemical_list: D004798:Enzymes; D011506:Proteins