Literature DB >> 18689840

An environmental perspective on large-scale genome clustering based on metabolic capabilities.

Gabi Kastenmüller1, Johann Gasteiger, Hans-Werner Mewes.   

Abstract

MOTIVATION: In principle, an organism's ability to survive in a speci.c environment, is an observable result of the organism's regulatory and metabolic capabilities. Nonetheless, current knowledge about the global relation of the metabolisms and the niches of organisms is still limited.
RESULTS: In order to further investigate this relation, we grouped species showing similar metabolic capabilities and systematically mapped their habitats onto these groups. For this purpose, we predicted the metabolic capabilities for 214 sequenced genomes. Based on these predictions, we grouped the genomes by hierarchical clustering. Finally, we mapped different environmental conditions and diseases related to the genomes onto the resulting clusters. This mapping uncovered several conditions and diseases that were unexpectedly enriched in clusters of metabolically similar species. As an example, Encephalitozoon cuniculi--a microsporidian causing a multisystemic disease accompanied by CNS problems in rabbits--occurred in the same metabolism-based cluster as bacteria causing similar symptoms in humans. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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Year:  2008        PMID: 18689840     DOI: 10.1093/bioinformatics/btn302

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  2 in total

1.  Machine learning methods for metabolic pathway prediction.

Authors:  Joseph M Dale; Liviu Popescu; Peter D Karp
Journal:  BMC Bioinformatics       Date:  2010-01-08       Impact factor: 3.169

2.  Comparative genome analysis and identification of competitive and cooperative interactions in a polymicrobial disease.

Authors:  Akiko Endo; Takayasu Watanabe; Nachiko Ogata; Takashi Nozawa; Chihiro Aikawa; Shinichi Arakawa; Fumito Maruyama; Yuichi Izumi; Ichiro Nakagawa
Journal:  ISME J       Date:  2014-08-29       Impact factor: 10.302

  2 in total

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