Learning transcriptional networks from the integration of ChIP-chip and expression data in a non-parametric model

RESULTS We have developed LeTICE (Learning Transcriptional networks from the Integration of ChIP-chip and Expression data), an algorithm for learning a transcriptional network from ChIP-chip and expression data. The network is specified by a binary matrix of transcription factor (TF)-gene interactions partitioning genes into modules and a background of genes that are not involved in the transcriptional regulation. We define a likelihood of a network, and then search for the network optimizing the likelihood. We applied LeTICE to the location and expression data from yeast cells grown in rich media to learn the transcriptional network specific to the yeast cell cycle. It found 12 condition-specific TFs and 15 modules each of which is highly represented with functions related to particular phases of cell-cycle regulation. AVAILABILITY Our algorithm is available at http://linus.nci.nih.gov/Data/YounA/LeTICE.zip

[1]  Mario Medvedovic,et al.  Bayesian hierarchical model for transcriptional module discovery by jointly modeling gene expression and ChIP-chip data , 2007, BMC Bioinformatics.

[2]  Feng Gao,et al.  Defining transcriptional networks through integrative modeling of mRNA expression and transcription factor binding data , 2004, BMC Bioinformatics.

[3]  S. Rafii,et al.  Splitting vessels: Keeping lymph apart from blood , 2003, Nature Medicine.

[4]  D. Botstein,et al.  Genomic expression programs in the response of yeast cells to environmental changes. , 2000, Molecular biology of the cell.

[5]  Ronald W. Davis,et al.  A genome-wide transcriptional analysis of the mitotic cell cycle. , 1998, Molecular cell.

[6]  Michael Costanzo,et al.  Dual Regulation by Pairs of Cyclin-Dependent Protein Kinases and Histone Deacetylases Controls G1 Transcription in Budding Yeast , 2009, PLoS biology.

[7]  Inderjit S. Dhillon,et al.  Kernel k-means: spectral clustering and normalized cuts , 2004, KDD.

[8]  Richard Bonneau,et al.  The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo , 2006, Genome Biology.

[9]  James C. Liao,et al.  A Gibbs sampler for the identification of gene expression and network connectivity consistency , 2006, Bioinform..

[10]  Michael Ruogu Zhang,et al.  Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. , 1998, Molecular biology of the cell.

[11]  Nicola J. Rinaldi,et al.  Serial Regulation of Transcriptional Regulators in the Yeast Cell Cycle , 2001, Cell.

[12]  P. Hall,et al.  Kernel density estimation with spherical data , 1987 .

[13]  J. Winderickx,et al.  Inferring transcriptional modules from ChIP-chip, motif and microarray data , 2006, Genome Biology.

[14]  Peter J. Rousseeuw,et al.  Finding Groups in Data: An Introduction to Cluster Analysis , 1990 .

[15]  D. Pe’er,et al.  Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data , 2003, Nature Genetics.

[16]  Daphne Koller,et al.  Genome-wide discovery of transcriptional modules from DNA sequence and gene expression , 2003, ISMB.

[17]  B. Futcher,et al.  The Cln3‐Cdc28 kinase complex of S. cerevisiae is regulated by proteolysis and phosphorylation. , 1992, The EMBO journal.

[18]  Mike Tyers,et al.  Mechanisms that help the yeast cell cycle clock tick: G2 cyclins transcriptionally activate G2 cyclins and repress G1 cyclins , 1993, Cell.

[19]  John D. Storey,et al.  Significance analysis of time course microarray experiments. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[20]  Curt Wittenberg,et al.  G1-specific cyclins of S. cerevisiae: Cell cycle periodicity, regulation by mating pheromone, and association with the p34 CDC28 protein kinase , 1990, Cell.

[21]  Nicola J. Rinaldi,et al.  Transcriptional regulatory code of a eukaryotic genome , 2004, Nature.

[22]  Christian J Stoeckert,et al.  Clustering of genes into regulons using integrated modeling-COGRIM , 2007, Genome Biology.

[23]  Joshua E. S. Socolar,et al.  Global control of cell-cycle transcription by coupled CDK and network oscillators , 2008, Nature.

[24]  Chiara Sabatti,et al.  Network component analysis: Reconstruction of regulatory signals in biological systems , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[25]  Nicola J. Rinaldi,et al.  Computational discovery of gene modules and regulatory networks , 2003, Nature Biotechnology.

[26]  Nicola J. Rinaldi,et al.  Transcriptional Regulatory Networks in Saccharomyces cerevisiae , 2002, Science.

[27]  Michal Linial,et al.  Using Bayesian Networks to Analyze Expression Data , 2000, J. Comput. Biol..

[28]  M. Lefebvre.  Applied probability and statistics , 2006 .

[29]  B. Futcher.  Transcriptional regulatory networks and the yeast cell cycle. , 2002, Current opinion in cell biology.

[30]  Cheryl Wolting,et al.  Cluster analysis of protein array results via similarity of Gene Ontology annotation , 2006, BMC Bioinformatics.

[31]  P. Rousseeuw,et al.  Wiley Series in Probability and Mathematical Statistics , 2005 .