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Use of Artificial Genomes in Assessing Methods for Atypical Gene Detection.

Author(s): Azad | Lawrence

Journal: PLoS Computational Biology
ISSN 1553-734X

Volume: 1;
Issue: 6;
Start page: e56;
Date: 2005;
Original page

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.
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