Clustering of genes into regulons using integrated modeling-COGRIM.
We present a Bayesian hierarchical model and Gibbs Sampling implementation that integrates gene expression, ChIP binding, and transcription factor motif data in a principled and robust fashion. COGRIM was applied to both unicellular and mammalian organisms under different scenarios of available data. In these applications, we demonstrate the ability to predict gene-transcription factor interactions with reduced numbers of false-positive findings and to make predictions beyond what is obtained when single types of data are considered.
Chen
Guang
G
Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA. ggchen@pcbi.upenn.edu
Jensen
Shane T
ST
Stoeckert
Christian J
CJ
D022782:CCAAT-Enhancer-Binding Protein-beta / Q000378:metabolism; D047369:Chromatin Immunoprecipitation; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D030561:Databases, Nucleic Acid; D004742:Enhancer Elements, Genetic / Q000235:genetics; D017353:Gene Deletion; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D016014:Linear Models; D008957:Models, Genetic; D011485:Protein Binding; D018085:Regulon / Q000235:genetics*; D012441:Saccharomyces cerevisiae / Q000235:genetics*; D026362:Serum Response Factor / Q000378:metabolism; D012984:Software; D014157:Transcription Factors / Q000378:metabolism
D022782:CCAAT-Enhancer-Binding Protein-beta; D026362:Serum Response Factor; D014157:Transcription Factors