ModuleDigger: an itemset mining framework for the detection of cis-regulatory modules

doi:10.1186/1471-2105-10-S1-S30

BMC Bioinformatics
Volume 10
Suppl 1

Viewing options:

Abstract
Full text
PDF (832KB)
Additional files

Associated material:

PubMed record

Related literature:

Articles citing this article
on Google Scholar
on ISI Web of Science
on PubMed Central
Other articles by authors
on Google Scholar

Sun H
De Bie T
Storms V
Fu Q
Dhollander T
Lemmens K
Verstuyf A
De Moor B
Marchal K

on PubMed

Sun H
De Bie T
Storms V
Fu Q
Dhollander T
Lemmens K
Verstuyf A
De Moor B
Marchal K
Related articles/pages
on Google

on Google Scholar

on PubMed

Tools:

Nominate for award

Post to:

This article is part of the supplement: Selected papers from the Seventh Asia-Pacific Bioinformatics Conference (APBC 2009) .

Research

ModuleDigger: an itemset mining framework for the detection of cis-regulatory modules

Hong Sun¹ , Tijl De Bie² , Valerie Storms³ , Qiang Fu³ , Thomas Dhollander¹ , Karen Lemmens¹ , Annemieke Verstuyf⁴ , Bart De Moor¹ and Kathleen Marchal³

¹Department of Electrical Engineering, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, 3001 Leuven, Belgium

²Department of Engineering Mathematics, university of Bristol, Bristol BS8 1TR, UK

³Department of Microbial and Molecular systems, Katholieke Universiteit Leuven, Kasteelpark Arenberg 20, 3001 Leuven, Belgium

⁴Laboratory for experimental medicine and endocrinology, Katholieke Universiteit Leuven, 3000 Leuven, Belgium

author email corresponding author email

BMC Bioinformatics 2009, 10(Suppl 1):S30doi:10.1186/1471-2105-10-S1-S30


Published:	30 January 2009

Abstract

Background

The detection of cis-regulatory modules (CRMs) that mediate transcriptional responses in eukaryotes remains a key challenge in the postgenomic era. A CRM is characterized by a set of co-occurring transcription factor binding sites (TFBS). In silico methods have been developed to search for CRMs by determining the combination of TFBS that are statistically overrepresented in a certain geneset. Most of these methods solve this combinatorial problem by relying on computational intensive optimization methods. As a result their usage is limited to finding CRMs in small datasets (containing a few genes only) and using binding sites for a restricted number of transcription factors (TFs) out of which the optimal module will be selected.

Results

We present an itemset mining based strategy for computationally detecting cis-regulatory modules (CRMs) in a set of genes. We tested our method by applying it on a large benchmark data set, derived from a ChIP-Chip analysis and compared its performance with other well known cis-regulatory module detection tools.

Conclusion

We show that by exploiting the computational efficiency of an itemset mining approach and combining it with a well-designed statistical scoring scheme, we were able to prioritize the biologically valid CRMs in a large set of coregulated genes using binding sites for a large number of potential TFs as input.