Use of artificial genomes in assessing methods for atypical gene detection.
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 u...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2005-11-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.0010056 |
| _version_ | 1819020312306515968 |
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| author | Rajeev K Azad Jeffrey G Lawrence |
| author_facet | Rajeev K Azad Jeffrey G Lawrence |
| author_sort | Rajeev K Azad |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-21T03:49:12Z |
| format | Article |
| id | doaj.art-322bc778f5ea4eefb0b9a3f3a89bb8c0 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-21T03:49:12Z |
| publishDate | 2005-11-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-322bc778f5ea4eefb0b9a3f3a89bb8c02022-12-21T19:17:01ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582005-11-0116e5610.1371/journal.pcbi.0010056Use of artificial genomes in assessing methods for atypical gene detection.Rajeev K AzadJeffrey G LawrenceParametric 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.https://doi.org/10.1371/journal.pcbi.0010056 |
| spellingShingle | Rajeev K Azad Jeffrey G Lawrence Use of artificial genomes in assessing methods for atypical gene detection. PLoS Computational Biology |
| title | Use of artificial genomes in assessing methods for atypical gene detection. |
| title_full | Use of artificial genomes in assessing methods for atypical gene detection. |
| title_fullStr | Use of artificial genomes in assessing methods for atypical gene detection. |
| title_full_unstemmed | Use of artificial genomes in assessing methods for atypical gene detection. |
| title_short | Use of artificial genomes in assessing methods for atypical gene detection. |
| title_sort | use of artificial genomes in assessing methods for atypical gene detection |
| url | https://doi.org/10.1371/journal.pcbi.0010056 |
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