Article

Detailing regulatory networks through large scale data integration.

Department of Computer Science, Princeton University, 35 Olden Street, Princeton, NJ 08540, USA.
Bioinformatics (impact factor: 5.47). 10/2009; 25(24):3267-74. DOI:10.1093/bioinformatics/btp588 pp.3267-74
Source: PubMed

ABSTRACT Much of a cell's regulatory response to changing environments occurs at the transcriptional level. Particularly in higher organisms, transcription factors (TFs), microRNAs and epigenetic modifications can combine to form a complex regulatory network. Part of this system can be modeled as a collection of regulatory modules: co-regulated genes, the conditions under which they are co-regulated and sequence-level regulatory motifs.
We present the Combinatorial Algorithm for Expression and Sequence-based Cluster Extraction (COALESCE) system for regulatory module prediction. The algorithm is efficient enough to discover expression biclusters and putative regulatory motifs in metazoan genomes (>20,000 genes) and very large microarray compendia (>10,000 conditions). Using Bayesian data integration, it can also include diverse supporting data types such as evolutionary conservation or nucleosome placement. We validate its performance using a functional evaluation of co-clustered genes, known yeast and Escherichea coli TF targets, synthetic data and various metazoan data compendia. In all cases, COALESCE performs as well or better than current biclustering and motif prediction tools, with high accuracy in functional and TF/target assignments and zero false positives on synthetic data. COALESCE provides an efficient and flexible platform within which large, diverse data collections can be integrated to predict metazoan regulatory networks.
Source code (C++) is available at http://function.princeton.edu/sleipnir, and supporting data and a web interface are provided at http://function.princeton.edu/coalesce.
ogt@cs.princeton.edu; hcoller@princeton.edu.
Supplementary data are available at Bioinformatics online.

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Keywords

Bayesian data integration
 
Bioinformatics online
 
cell's regulatory response
 
co-clustered genes
 
co-regulated genes
 
data types
 
diverse data collections
 
epigenetic modifications
 
functional evaluation
 
higher organisms
 
metazoan regulatory networks
 
motif prediction tools
 
nucleosome placement
 
putative regulatory motifs
 
regulatory module prediction
 
Sequence-based Cluster Extraction
 
sequence-level regulatory motifs
 
Supplementary data
 
synthetic data
 
various metazoan data compendia