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Clustering of genes into regulons using integrated modeling-COGRIM

Guang Chen12, Shane T Jensen3 and Christian J Stoeckert24*

Author Affiliations

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

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Genome Biology 2007, 8:R4  doi:10.1186/gb-2007-8-1-r4

Published: 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.