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

doi:10.1111/j.1749-6632.2008.03746.x

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 output › peer-review

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.

Original language	English
Pages (from-to)	29-35
Journal	Annals of the New York Academy of Sciences
Volume	1158
DOIs	https://doi.org/10.1111/j.1749-6632.2008.03746.x
State	Published - 11 Mar 2009
Event	DREAM reverse engineering challenges - Broad Institute of MIT and Harvard; the MIT Computer Science and Artificial Intelligence Lab, Boston, Massachusetts Duration: 29 Oct 2008 → 2 Nov 2008

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10.1111/j.1749-6632.2008.03746.xLicense: Other

Cite this

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title = "The condition-dependent transcriptional network in Escherichia coli.",

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.",

keywords = "regulatory networks, bioinformatics, systems biology",

author = "Karen Lemmens and {De Bie}, Tijl and Thomas Dhollander and Pieter Monsieurs and {De Moor}, Bart and Julio Collado-Vides and Kristof Engelen and Kathleen Marchal",

note = "Score = 10; DREAM reverse engineering challenges ; Conference date: 29-10-2008 Through 02-11-2008",

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language = "English",

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TY - JOUR

T1 - The condition-dependent transcriptional network in Escherichia coli.

AU - Lemmens, Karen

AU - De Bie, Tijl

AU - Dhollander, Thomas

AU - Monsieurs, Pieter

AU - De Moor, Bart

AU - Collado-Vides, Julio

AU - Engelen, Kristof

AU - Marchal, Kathleen

N1 - Score = 10

PY - 2009/3/11

Y1 - 2009/3/11

N2 - 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.

AB - 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.

KW - regulatory networks

KW - bioinformatics

KW - systems biology

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UR - http://knowledgecentre.sckcen.be/so2/bibref/6566

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DO - 10.1111/j.1749-6632.2008.03746.x

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JO - Annals of the New York Academy of Sciences

JF - Annals of the New York Academy of Sciences

T2 - DREAM reverse engineering challenges

Y2 - 29 October 2008 through 2 November 2008

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