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: Contribution to journal › A1: Web of Science-article › 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 frame work 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 v,,ere predicted.

Original language	English
Journal	Annals of the New York Academy of Sciences
Pages (from-to)	29-35
Number of pages	7
ISSN	0077-8923
DOIs	https://doi.org/10.1111/j.1749-6632.2008.03746.x
Publication status	Published - 2009
Externally published	Yes
Event	DREAM2 Conference - New York Duration: 3-Dec-2007 → 4-Dec-2007

Keywords

transcriptional modules
frequent itemset mining
DISTILLER
EXPRESSION PROFILES
REGULATORY NETWORK
DATABASE
TOOLS

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10.1111/j.1749-6632.2008.03746.x

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 frame work 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 v,,ere predicted.",

keywords = "transcriptional modules, frequent itemset mining, DISTILLER, EXPRESSION PROFILES, REGULATORY NETWORK, DATABASE, TOOLS",

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",

year = "2009",

doi = "10.1111/j.1749-6632.2008.03746.x",

language = "English",

pages = "29--35",

journal = "Annals of the New York Academy of Sciences",

issn = "0077-8923",

publisher = "Wiley - Blackwell",

note = "DREAM2 Conference ; Conference date: 03-12-2007 Through 04-12-2007",

}

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

PY - 2009

Y1 - 2009

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 frame work 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 v,,ere 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 frame work 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 v,,ere predicted.

KW - transcriptional modules

KW - frequent itemset mining

KW - DISTILLER

KW - EXPRESSION PROFILES

KW - REGULATORY NETWORK

KW - DATABASE

KW - TOOLS

U2 - 10.1111/j.1749-6632.2008.03746.x

DO - 10.1111/j.1749-6632.2008.03746.x

M3 - A1: Web of Science-article

SN - 0077-8923

SP - 29

EP - 35

JO - Annals of the New York Academy of Sciences

JF - Annals of the New York Academy of Sciences

T2 - DREAM2 Conference

Y2 - 3 December 2007 through 4 December 2007

ER -

The Condition-Dependent Transcriptional Network in Escherichia coli

Abstract

Keywords

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