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, Kathleen Marchal

    Research outputpeer-review


    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.
    Original languageEnglish
    Pages (from-to)29-35
    JournalAnnals of the New York Academy of Sciences
    StatePublished - 11 Mar 2009
    EventDREAM reverse engineering challenges - Broad Institute of MIT and Harvard; the MIT Computer Science and Artificial Intelligence Lab, Boston, Massachusetts
    Duration: 29 Oct 20082 Nov 2008

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