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


CONCLUSIONS
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.
Sun
Hong
H
Department of Electrical Engineering, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, 3001 Leuven, Belgium. hong.sun@esat.kuleuven.be
De Bie
Tijl
T
Storms
Valerie
V
Fu
Qiang
Q
Dhollander
Thomas
T
Lemmens
Karen
K
Verstuyf
Annemieke
A
De Moor
Bart
B
Marchal
Kathleen
K
D000465:Algorithms; D001665:Binding Sites; D030541:Databases, Genetic; D008957:Models, Genetic; D050436:Regulatory Elements, Transcriptional; D012984:Software; D014157:Transcription Factors / Q000378:metabolism*
D014157:Transcription Factors