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Bioinformatics 2005 21(Suppl 1):i468-i477; doi:10.1093/bioinformatics/bti1012
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© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oupjournals.org

Supervised enzyme network inference from the integration of genomic data and chemical information

Yoshihiro Yamanishi 1,*, Jean-Philippe Vert 2 and Minoru Kanehisa 1

1Bioinformatics Center, Institute for Chemical Research, Kyoto University Gokasho, Uji, Kyoto 611-0011, Japan
2Computational Biology Group, Ecole des Mines de Paris 35 rue Saint-Honoré, 77305 Fontainebleau cedex, France

*To whom correspondence should be addressed.

Motivation: The metabolic network is an important biological network which relates enzyme proteins and chemical compounds. A large number of metabolic pathways remain unknown nowadays, and many enzymes are missing even in known metabolic pathways. There is, therefore, an incentive to develop methods to reconstruct the unknown parts of the metabolic network and to identify genes coding for missing enzymes.

Results: This paper presents new methods to infer enzyme networks from the integration of multiple genomic data and chemical information, in the framework of supervised graph inference. The originality of the methods is the introduction of chemical compatibility as a constraint for refining the network predicted by the network inference engine. The chemical compatibility between two enzymes is obtained automatically from the information encoded by their Enzyme Commission (EC) numbers. The proposed methods are tested and compared on their ability to infer the enzyme network of the yeast Saccharomyces cerevisiae from four datasets for enzymes with assigned EC numbers: gene expression data, protein localization data, phylogenetic profiles and chemical compatibility information. It is shown that the prediction accuracy of the network reconstruction consistently improves owing to the introduction of chemical constraints, the use of a supervised approach and the weighted integration of multiple datasets. Finally, we conduct a comprehensive prediction of a global enzyme network consisting of all enzyme candidate proteins of the yeast to obtain new biological findings.

Availability: Softwares are available upon request.

Contact: yoshi{at}kuicr.kyoto-u.ac.jp

Received on January 15, 2005; accepted on March 27, 2005

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[Abstract] [Full Text] [PDF]


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