title: Use of artificial genomes in assessing methods for atypical gene detection.
abstract: Parametric methods for identifying laterally transferred genes exploit the directional mutational biases unique to each genome. Yet the development of new, more robust methods--as well as the evaluation and proper implementation of existing methods--relies on an arbitrary assessment of performance using real genomes, where the evolutionary histories of genes are not known. We have used the framework of a generalized hidden Markov model to create artificial genomes modeled after genuine genomes. To model a genome, "core" genes--those displaying patterns of mutational biases shared among large numbers of genes--are identified by a novel gene clustering approach based on the Akaike information criterion. Gene models derived from multiple "core" gene clusters are used to generate an artificial genome that models the properties of a genuine genome. Chimeric artificial genomes--representing those having experienced lateral gene transfer--were created by combining genes from multiple artificial genomes, and the performance of the parametric methods for identifying "atypical" genes was assessed directly. We found that a hidden Markov model that included multiple gene models, each trained on sets of genes representing the range of genotypic variability within a genome, could produce artificial genomes that mimicked the properties of genuine genomes. Moreover, different methods for detecting foreign genes performed differently--i.e., they had different sets of strengths and weaknesses--when identifying atypical genes within chimeric artificial genomes.
authors.affiliation: Department of Biological Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
authors.forename: Rajeev K
authors.initials: RK
authors.lastname: Azad
authors.forename: Jeffrey G
authors.initials: JG
authors.lastname: Lawrence
mesh_terms: D000465:Algorithms; D001482:Base Composition; D001483:Base Sequence; D046528:Chimerism; D004926:Escherichia coli / Q000235:genetics*; D005798:Genes, Bacterial / Q000235:genetics*; D014644:Genetic Variation / Q000235:genetics; D016678:Genome / Q000235:genetics*; D016680:Genome, Bacterial / Q000235:genetics; D008390:Markov Chains; D008957:Models, Genetic; D016366:Open Reading Frames / Q000235:genetics