Journal Article

Detailing regulatory networks through large scale data integration

Curtis Huttenhower, K. Tsheko Mutungu, Natasha Indik, Woongcheol Yang, Mark Schroeder, Joshua J. Forman, Olga G. Troyanskaya and Hilary A. Coller

in Bioinformatics

Volume 25, issue 24, pages 3267-3274
Published in print December 2009 | ISSN: 1367-4803
Published online October 2009 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btp588
Detailing regulatory networks through large scale data integration

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Motivation: 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.

Results: 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.

Availability: 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.

Contact: ogt@cs.princeton.edu; hcoller@princeton.edu.

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  6205 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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