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This article is part of the supplement: Selected Proceedings of Machine Learning in Systems Biology: MLSB 2007

Open Access Proceedings

Machine learning techniques to identify putative genes involved in nitrogen catabolite repression in the yeast Saccharomyces cerevisiae

Kevin Kontos1*, Patrice Godard23, Bruno André2, Jacques van Helden4 and Gianluca Bontempi1

Author Affiliations

1 Machine Learning Group, Département d'Informatique, Faculté des Sciences, Université Libre de Bruxelles (ULB), Boulevard du Triomphe CP 212, 1050 Brussels, Belgium

2 Physiologie Moléculaire de la Cellule, IBMM, Faculté des Sciences, ULB, Rue des Pr. Jeener et Brachet 12, 6041 Gosselies, Belgium

3 Unité de Recherche en Biologie Cellulaire, Département de Biologie, Faculté des Sciences, Facultés Universitaires Notre-Dame de la Paix Namur (FUNDP), Rue de Bruxelles 61, 5000 Namur, Belgium

4 Laboratoire de Bioinformatique des Génomes et des Réseaux, Faculté des Sciences, ULB, Boulevard du Triomphe CP 263, 1050 Brussels, Belgium

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BMC Proceedings 2008, 2(Suppl 4):S5  doi:

Published: 17 December 2008

Abstract

Background

Nitrogen is an essential nutrient for all life forms. Like most unicellular organisms, the yeast Saccharomyces cerevisiae transports and catabolizes good nitrogen sources in preference to poor ones. Nitrogen catabolite repression (NCR) refers to this selection mechanism. All known nitrogen catabolite pathways are regulated by four regulators. The ultimate goal is to infer the complete nitrogen catabolite pathways. Bioinformatics approaches offer the possibility to identify putative NCR genes and to discard uninteresting genes.

Results

We present a machine learning approach where the identification of putative NCR genes in the yeast Saccharomyces cerevisiae is formulated as a supervised two-class classification problem. Classifiers predict whether genes are NCR-sensitive or not from a large number of variables related to the GATA motif in the upstream non-coding sequences of the genes. The positive and negative training sets are composed of annotated NCR genes and manually-selected genes known to be insensitive to NCR, respectively. Different classifiers and variable selection methods are compared. We show that all classifiers make significant and biologically valid predictions by comparing these predictions to annotated and putative NCR genes, and by performing several negative controls. In particular, the inferred NCR genes significantly overlap with putative NCR genes identified in three genome-wide experimental and bioinformatics studies.

Conclusion

These results suggest that our approach can successfully identify potential NCR genes. Hence, the dimensionality of the problem of identifying all genes involved in NCR is drastically reduced.