Clustering of genes into regulons using integrated modeling-COGRIM
1 Department of Bioengineering, University of Pennsylvania, 240 Skirkanich Hall, 3320 Smith Walk, Philadelphia, Pennsylvania 19104, USA
2 Center for Bioinformatics, University of Pennsylvania,1420 Blockley Hall, 423 Guardian Drive, Philadelphia, Pennsylvania 19104, USA
3 Department of Statistics, The Wharton School, University of Pennsylvania, 463 Jon M. Huntsman Hall, 3730 Walnut Street, Philadelphia, Pennsylvania 19104, USA
4 Department of Genetics, School of Medicine, University of Pennsylvania, 415 Curie Boulevard, Philadelphia, Pennsylvania 19104, USA
Genome Biology 2007, 8:R4 doi:10.1186/gb-2007-8-1-r4Published: 4 January 2007
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.