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Uncovering metabolic pathways relevant to phenotypic traits of microbial genomes.

Kastenmüller G, Schenk ME, Gasteiger J, Mewes HW - Genome Biol. (2009)

Bottom Line: Identifying the biochemical basis of microbial phenotypes is a main objective of comparative genomics.Here we present a novel method using multivariate machine learning techniques for comparing automatically derived metabolic reconstructions of sequenced genomes on a large scale.Applying our method to 266 genomes directly led to testable hypotheses such as the link between the potential of microorganisms to cause periodontal disease and their ability to degrade histidine, a link also supported by clinical studies.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstrasse, Neuherberg, Germany. g.kastenmueller@helmholtz-muenchen.de

ABSTRACT
Identifying the biochemical basis of microbial phenotypes is a main objective of comparative genomics. Here we present a novel method using multivariate machine learning techniques for comparing automatically derived metabolic reconstructions of sequenced genomes on a large scale. Applying our method to 266 genomes directly led to testable hypotheses such as the link between the potential of microorganisms to cause periodontal disease and their ability to degrade histidine, a link also supported by clinical studies.

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Classification quality for the classification of archaea into methanogens and non-methanogens using the nearest neighbor classifier while omitting the pathway of methane synthesis. Omitting the pathway of methane synthesis (methane1) in our analyses, the classification based on the most relevant pathways still reaches perfect separation of methanogenic archaea and non-methanogenic archaea for all attribute subset selection methods used (green, ReliefF; yellow, SVMAttributeEval; blue, wrapper (naïve Bayes)). Classification based on all pathways (marked by a horizontal line) and based on randomly picked pathways (red) show lower classification quality.
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Figure 5: Classification quality for the classification of archaea into methanogens and non-methanogens using the nearest neighbor classifier while omitting the pathway of methane synthesis. Omitting the pathway of methane synthesis (methane1) in our analyses, the classification based on the most relevant pathways still reaches perfect separation of methanogenic archaea and non-methanogenic archaea for all attribute subset selection methods used (green, ReliefF; yellow, SVMAttributeEval; blue, wrapper (naïve Bayes)). Classification based on all pathways (marked by a horizontal line) and based on randomly picked pathways (red) show lower classification quality.

Mentions: In order to ensure that the good classification quality was not mainly due to the high relevance of methane1, we deleted methane1 from the pathway profiles and repeated our analysis. Thereby, we received almost the same set of relevant pathways (Table 1) and an almost as high classification quality as with methane1 (Table 3 and Figure 5).


Uncovering metabolic pathways relevant to phenotypic traits of microbial genomes.

Kastenmüller G, Schenk ME, Gasteiger J, Mewes HW - Genome Biol. (2009)

Classification quality for the classification of archaea into methanogens and non-methanogens using the nearest neighbor classifier while omitting the pathway of methane synthesis. Omitting the pathway of methane synthesis (methane1) in our analyses, the classification based on the most relevant pathways still reaches perfect separation of methanogenic archaea and non-methanogenic archaea for all attribute subset selection methods used (green, ReliefF; yellow, SVMAttributeEval; blue, wrapper (naïve Bayes)). Classification based on all pathways (marked by a horizontal line) and based on randomly picked pathways (red) show lower classification quality.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2690999&req=5

Figure 5: Classification quality for the classification of archaea into methanogens and non-methanogens using the nearest neighbor classifier while omitting the pathway of methane synthesis. Omitting the pathway of methane synthesis (methane1) in our analyses, the classification based on the most relevant pathways still reaches perfect separation of methanogenic archaea and non-methanogenic archaea for all attribute subset selection methods used (green, ReliefF; yellow, SVMAttributeEval; blue, wrapper (naïve Bayes)). Classification based on all pathways (marked by a horizontal line) and based on randomly picked pathways (red) show lower classification quality.
Mentions: In order to ensure that the good classification quality was not mainly due to the high relevance of methane1, we deleted methane1 from the pathway profiles and repeated our analysis. Thereby, we received almost the same set of relevant pathways (Table 1) and an almost as high classification quality as with methane1 (Table 3 and Figure 5).

Bottom Line: Identifying the biochemical basis of microbial phenotypes is a main objective of comparative genomics.Here we present a novel method using multivariate machine learning techniques for comparing automatically derived metabolic reconstructions of sequenced genomes on a large scale.Applying our method to 266 genomes directly led to testable hypotheses such as the link between the potential of microorganisms to cause periodontal disease and their ability to degrade histidine, a link also supported by clinical studies.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstrasse, Neuherberg, Germany. g.kastenmueller@helmholtz-muenchen.de

ABSTRACT
Identifying the biochemical basis of microbial phenotypes is a main objective of comparative genomics. Here we present a novel method using multivariate machine learning techniques for comparing automatically derived metabolic reconstructions of sequenced genomes on a large scale. Applying our method to 266 genomes directly led to testable hypotheses such as the link between the potential of microorganisms to cause periodontal disease and their ability to degrade histidine, a link also supported by clinical studies.

Show MeSH
Related in: MedlinePlus