Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
From promoter sequence to expression: a probabilistic framework
Proceedings of the sixth annual international conference on Computational biology
Proceedings of the sixth annual international conference on Computational biology
An Efficient Algorithm for the Identification of Structured Motifs in DNA Promoter Sequences
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Probabilistic in silico prediction of protein-peptide interactions
RECOMB'05 Proceedings of the 2005 joint annual satellite conference on Systems biology and regulatory genomics
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Transcriptional regulation is mediated by the coordinated binding of transcription factors to the upstream region of genes. In higher eukaryotes, the binding sites of cooperating transcription factors are organized into short sequence units, called cis-regulatory modules. In this paper we propose a method for identifying modules of transcription factor binding sites in a set of co-regulated genes, using only the raw sequence data as input. Our method is based on a novel probabilistic model that describes the mechanism of cis-regulation, including the binding sites of cooperating transcription factors, the organization of these binding sites into short sequence modules, and the regulation of a gene by its modules. We show that our method is successful in discovering planted modules in simulated data and known modules in yeast. More importantly, we applied our method to a large collection of human gene sets, and found 83 significant cis-regulatory modules, which included 36 known motifs and many novel ones. Thus, our results provide one of the first comprehensive compendiums of putative cis-regulatory modules in human.