title: Detailing regulatory networks through large scale data integration.
abstract: BACKGROUND
  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.
  
  
  BACKGROUND
  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.
  
  
  BACKGROUND
  ogt@cs.princeton.edu; hcoller@princeton.edu.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Computer Science, Princeton University, 35 Olden Street, Princeton, NJ 08540, USA.
authors.forename: Curtis
authors.initials: C
authors.lastname: Huttenhower
authors.forename: K Tsheko
authors.initials: KT
authors.lastname: Mutungu
authors.forename: Natasha
authors.initials: N
authors.lastname: Indik
authors.forename: Woongcheol
authors.initials: W
authors.lastname: Yang
authors.forename: Mark
authors.initials: M
authors.lastname: Schroeder
authors.forename: Joshua J
authors.initials: JJ
authors.lastname: Forman
authors.forename: Olga G
authors.initials: OG
authors.lastname: Troyanskaya
authors.forename: Hilary A
authors.initials: HA
authors.lastname: Coller
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D014157:Transcription Factors / Q000235:genetics / Q000378:metabolism
chemical_list: D014157:Transcription Factors