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, Hilary A. Coller

Research output: Contribution to journalArticlepeer-review

54 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article numberbtp588
Pages (from-to)3267-3274
Number of pages8
JournalBioinformatics
Volume25
Issue number24
DOIs
StatePublished - Oct 13 2009

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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