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Methods

Decoding Human Regulatory Circuits

¹ Center for Bioinformatics, The Wadsworth Center, New York State Department of Health, Albany, New York 12208, USA ² Centre for Molecular Medicine and Therapeutics, Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia V5Z 4H4, Canada ³ Department of Statistics, Harvard University, Cambridge, Massachusetts 02138, USA ⁴ Computer Science Department, Rensselaer Polytechnic Institute, Troy, New York 12180, USA

Clusters of transcription factor binding sites (TFBSs) which direct gene expression constitute cis-regulatory modules (CRMs). We present a novel algorithm, based on Gibbs sampling, which locates, de novo, the cis features of these CRMs, their component TFBSs, and the properties of their spatial distribution. The algorithm finds 69% of experimentally reported TFBSs and 85% of the CRMs in a reference data set of regions upstream of genes differentially expressed in skeletal muscle cells. A discriminant procedure based on the output of the model specifically discriminated regulatory sequences in muscle-specific genes in an independent test set. Application of the method to the analysis of 2710 10-kb fragments upstream of annotated human genes identified 17 novel candidate modules with a false discovery rate 0.05, demonstrating the applicability of the method to genome-scale data.

[Supplemental material is available online at www.genome.org.]

Article and publication are at http://www.genome.org/cgi/doi/10.1101/gr.2589004.

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