Gaussian Graphical Models to Infer Putative Genes Involved in Nitrogen Catabolite Repression in S. cerevisiae View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2009

AUTHORS

Kevin Kontos , Bruno André , Jacques van Helden , Gianluca Bontempi

ABSTRACT

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. We propose an approach based on Gaussian graphical models (GGMs), which enable to distinguish direct from indirect interactions between genes, to identify putative NCR genes from putative NCR regulatory motifs and over-represented motifs in the upstream noncoding sequences of annotated NCR genes. Because of the high-dimensionality of the data, we use a shrinkage estimator of the covariance matrix to infer the GGMs. We show that our approach makes significant and biologically valid predictions. We also show that GGMs are more effective than models that rely on measures of direct interactions between genes. More... »

PAGES

13-24

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-01184-9_2

DOI

http://dx.doi.org/10.1007/978-3-642-01184-9_2

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1010394981


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