Use of artificial genomes in assessing methods for atypical gene detection.
| Autor: | Rajeev K Azad, Jeffrey G Lawrence |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2005 |
| Předmět: | |
| Zdroj: | PLoS Computational Biology, Vol 1, Iss 6, p e56 (2005) |
| Druh dokumentu: | article |
| ISSN: | 1553-734X 1553-7358 |
| DOI: | 10.1371/journal.pcbi.0010056 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
|