The condition-dependent transcriptional network in Escherichia coli.
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.
Department of Electrical Engineering, Katholieke Universiteit Leuven, Leuven, Belgium.
Karen
K
Lemmens
Tijl
T
De Bie
Thomas
T
Dhollander
Pieter
P
Monsieurs
Bart
B
De Moor
Julio
J
Collado-Vides
Kristof
K
Engelen
Kathleen
K
Marchal
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D004926:Escherichia coli / Q000235:genetics*; D020869:Gene Expression Profiling; D015964:Gene Expression Regulation, Bacterial; D053263:Gene Regulatory Networks; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis