Genetic Algorithm-Based Maximum-Likelihood Analysis for Molecular Phylogeny.
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| Title: | Genetic Algorithm-Based Maximum-Likelihood Analysis for Molecular Phylogeny. |
|---|---|
| Authors: | Katoh, Kazutaka1 katoh@biophys.kyoto-u.ac.jp, Kuma, Kei-ichi1, Miyata, Takashi1 |
| Source: | Journal of Molecular Evolution. Oct2001, Vol. 53 Issue 4/5, p477-484. 08p. |
| Subjects: | Genetic algorithms, Combinatorial optimization, Molecular phylogeny, Phylogeny, Molecular evolution, Origin of life, Evolutionary theories, Molecular biology |
| Abstract: | A heuristic approach to search for the maximum-likelihood (ML) phylogenetic tree based on a genetic algorithm (GA) has been developed. It outputs the best tree as well as multiple alternative trees that are not significantly worse than the best one on the basis of the likelihood criterion. These near-optimum trees are subjected to further statistical tests. This approach enables ones to infer phylogenetic trees of over 20 taxa taking account of the rate heterogeneity among sites on practical time scales on a PC cluster. Computer simulations were conducted to compare the efficiency of the present approach with that of several likelihood-based methods and distance-based methods, using amino acid sequence data of relatively large (5–24) taxa. The superiority of the ML method over distance-based methods increases as the condition of simulations becomes more realistic (an incorrect model is assumed or many taxa are involved). This approach was applied to the inference of the universal tree based on the concatenated amino acid sequences of vertically descendent genes that are shared among all genomes whose complete sequences have been reported. The inferred tree strongly supports that Archaea is paraphyletic and Eukarya is specifically related to Crenarchaeota. Apart from the paraphyly of Archaea and some minor disagreements, the universal tree based on these genes is largely consistent with the universal tree based on SSU rRNA. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Molecular Evolution is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Items | – Name: Title Label: Title Group: Ti Data: Genetic Algorithm-Based Maximum-Likelihood Analysis for Molecular Phylogeny. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Katoh%2C+Kazutaka%22">Katoh, Kazutaka</searchLink><relatesTo>1</relatesTo><i> katoh@biophys.kyoto-u.ac.jp</i><br /><searchLink fieldCode="AR" term="%22Kuma%2C+Kei-ichi%22">Kuma, Kei-ichi</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Miyata%2C+Takashi%22">Miyata, Takashi</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Molecular+Evolution%22">Journal of Molecular Evolution</searchLink>. Oct2001, Vol. 53 Issue 4/5, p477-484. 08p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Genetic+algorithms%22">Genetic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Combinatorial+optimization%22">Combinatorial optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Molecular+phylogeny%22">Molecular phylogeny</searchLink><br /><searchLink fieldCode="DE" term="%22Phylogeny%22">Phylogeny</searchLink><br /><searchLink fieldCode="DE" term="%22Molecular+evolution%22">Molecular evolution</searchLink><br /><searchLink fieldCode="DE" term="%22Origin+of+life%22">Origin of life</searchLink><br /><searchLink fieldCode="DE" term="%22Evolutionary+theories%22">Evolutionary theories</searchLink><br /><searchLink fieldCode="DE" term="%22Molecular+biology%22">Molecular biology</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: A heuristic approach to search for the maximum-likelihood (ML) phylogenetic tree based on a genetic algorithm (GA) has been developed. It outputs the best tree as well as multiple alternative trees that are not significantly worse than the best one on the basis of the likelihood criterion. These near-optimum trees are subjected to further statistical tests. This approach enables ones to infer phylogenetic trees of over 20 taxa taking account of the rate heterogeneity among sites on practical time scales on a PC cluster. Computer simulations were conducted to compare the efficiency of the present approach with that of several likelihood-based methods and distance-based methods, using amino acid sequence data of relatively large (5–24) taxa. The superiority of the ML method over distance-based methods increases as the condition of simulations becomes more realistic (an incorrect model is assumed or many taxa are involved). This approach was applied to the inference of the universal tree based on the concatenated amino acid sequences of vertically descendent genes that are shared among all genomes whose complete sequences have been reported. The inferred tree strongly supports that Archaea is paraphyletic and Eukarya is specifically related to Crenarchaeota. Apart from the paraphyly of Archaea and some minor disagreements, the universal tree based on these genes is largely consistent with the universal tree based on SSU rRNA. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Molecular Evolution is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s002390010238 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 08 StartPage: 477 Subjects: – SubjectFull: Genetic algorithms Type: general – SubjectFull: Combinatorial optimization Type: general – SubjectFull: Molecular phylogeny Type: general – SubjectFull: Phylogeny Type: general – SubjectFull: Molecular evolution Type: general – SubjectFull: Origin of life Type: general – SubjectFull: Evolutionary theories Type: general – SubjectFull: Molecular biology Type: general Titles: – TitleFull: Genetic Algorithm-Based Maximum-Likelihood Analysis for Molecular Phylogeny. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Katoh, Kazutaka – PersonEntity: Name: NameFull: Kuma, Kei-ichi – PersonEntity: Name: NameFull: Miyata, Takashi IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2001 Type: published Y: 2001 Identifiers: – Type: issn-print Value: 00222844 Numbering: – Type: volume Value: 53 – Type: issue Value: 4/5 Titles: – TitleFull: Journal of Molecular Evolution Type: main |
| ResultId | 1 |
