A graph-based approach to systematically reconstruct human transcriptional regulatory modules
1IBM T. J. Watson Research Center, Hawthorne NY and 2Program in Molecular and Computational Biology, University of Southern California, Los Angeles CA, USA
*To whom correspondence should be addressed.
 |
Abstract |
|---|
Motivation: A major challenge in studying gene regulation is to systematically reconstruct transcription regulatory modules, which are defined as sets of genes that are regulated by a common set of transcription factors. A commonly used approach for transcription module reconstruction is to derive coexpression clusters from a microarray dataset. However, such results often contain false positives because genes from many transcription modules may be simultaneously perturbed upon a given type of conditions. In this study, we propose and validate that genes, which form a coexpression cluster in multiple microarray datasets across diverse conditions, are more likely to form a transcription module. However, identifying genes coexpressed in a subset of many microarray datasets is not a trivial computational problem.
Results: We propose a graph-based data-mining approach to efficiently and systematically identify frequent coexpression clusters. Given m microarray datasets, we model each microarray dataset as a coexpression graph, and search for vertex sets which are frequently densely connected across 
m 
datasets (0 
1). For this novel graph-mining problem, we designed two techniques to narrow down the search space: (1) partition the input graphs into (overlapping) groups sharing common properties; (2) summarize the vertex neighbor information from the partitioned datasets onto the ‘Neighbor Association Summary Graph's for effective mining. We applied our method to 105 human microarray datasets, and identified a large number of potential transcription modules, activated under different subsets of conditions. Validation by ChIP-chip data demonstrated that the likelihood of a coexpression cluster being a transcription module increases significantly with its recurrence. Our method opens a new way to exploit the vast amount of existing microarray data accumulation for gene regulation study. Furthermore, the algorithm is applicable to other biological networks for approximate network module mining.
Availability: http://zhoulab.usc.edu/NeMo/
Contact: xjzhou{at}usc.edu
The authors wish it to be known that, in this opinion, the first two authors should be regarded as joint first authors.
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  J. D. Wren A global meta-analysis of microarray expression data to predict unknown gene functions and estimate the literature-data divide Bioinformatics, July 1, 2009; 25(13): 1694 - 1701. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
E. Georgii, S. Dietmann, T. Uno, P. Pagel, and K. Tsuda Enumeration of condition-dependent dense modules in protein interaction networks Bioinformatics, April 1, 2009; 25(7): 933 - 940. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. Rajaram A novel meta-analysis method exploiting consistency of high-throughput experiments Bioinformatics, March 1, 2009; 25(5): 636 - 642. [Abstract] [Full Text] [PDF]
|
|