PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

The Condition-Dependent Transcriptional Network in Escherichia coli
Karen Lemmens, Tijl De Bie, Thomas Dhollander, Pieter Monsieurs, Bart De Moor, Julio Collado-Vides, Kristof Engelen and Kathleen Marchal
Annals of the New York Academy of Sciences Volume 1158, Number 1, pp. 29-35, 2009.

Abstract

Thanks to the availability of high-throughput omics data, bioinformatics approaches are able to hypothesize thus-far undocumented genetic interactions. However, due to the amount of noise in these data, inferences based on a single data source are often unreliable. A popular approach to overcome this problem is to integrate different data sources. In this study, we describe DISTILLER, a novel framework for data integration that simultaneously analyzes microarray and motif information to find modules that consist of genes that are co-expressed in a subset of conditions, and their corresponding regulators. By applying our method on publicly available data, we evaluated the condition-specific transcriptional network of Escherichia coli. DISTILLER confirmed 62% of 736 interactions described in RegulonDB, and 278 novel interactions were predicted.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Multimodal Integration
ID Code:5937
Deposited By:Tijl De Bie
Deposited On:08 March 2010