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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.

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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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Overview of the approach. The three major steps of our approach are: metabolic reconstruction of completely sequenced genomes resulting in pathway profiles; pathway selection resulting in lists of pathways ranked by relevance; and cross-checking of the resulting pathway rankings by classification in order to estimate their significance (Figure S1 in Additional data file 2).
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Figure 1: Overview of the approach. The three major steps of our approach are: metabolic reconstruction of completely sequenced genomes resulting in pathway profiles; pathway selection resulting in lists of pathways ranked by relevance; and cross-checking of the resulting pathway rankings by classification in order to estimate their significance (Figure S1 in Additional data file 2).

Mentions: Assessing the metabolic complements of completely sequenced genomes, therefore, represents the first of the three major steps of our approach. For each phenotype under consideration, we then selected the subset of metabolic pathways that are most relevant in distinguishing the genomes showing the phenotype and the genomes lacking it. For this step we used (multivariate) statistical attribute selection methods. In a third step, we cross-checked the resulting sets of relevant pathways by classifying the genomes (into those showing a specific phenotype and those lacking it) based only on our predictions for the relevant pathways in the respective genomes. Figure 1 shows an overview of the method delineated in the following. A detailed description of each of its three steps is given in Materials and methods.


Uncovering metabolic pathways relevant to phenotypic traits of microbial genomes.

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

Overview of the approach. The three major steps of our approach are: metabolic reconstruction of completely sequenced genomes resulting in pathway profiles; pathway selection resulting in lists of pathways ranked by relevance; and cross-checking of the resulting pathway rankings by classification in order to estimate their significance (Figure S1 in Additional data file 2).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: Overview of the approach. The three major steps of our approach are: metabolic reconstruction of completely sequenced genomes resulting in pathway profiles; pathway selection resulting in lists of pathways ranked by relevance; and cross-checking of the resulting pathway rankings by classification in order to estimate their significance (Figure S1 in Additional data file 2).
Mentions: Assessing the metabolic complements of completely sequenced genomes, therefore, represents the first of the three major steps of our approach. For each phenotype under consideration, we then selected the subset of metabolic pathways that are most relevant in distinguishing the genomes showing the phenotype and the genomes lacking it. For this step we used (multivariate) statistical attribute selection methods. In a third step, we cross-checked the resulting sets of relevant pathways by classifying the genomes (into those showing a specific phenotype and those lacking it) based only on our predictions for the relevant pathways in the respective genomes. Figure 1 shows an overview of the method delineated in the following. A detailed description of each of its three steps is given in Materials and methods.

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