Predicted E. coli regulons

Regulons were predicted by clustering regulatory motifs that were detected by phylogenetic footprinting. The motifs used for clustering are those with statistical significance at p <= 0.2 as described in Genome Research, 12:1523-1532 and available at http://bayesweb.wadsworth.org/binding_sites. The clustering algorithm we used is BMC, described in Nature Biotech 21:435-443, but with the new feature of a sampling (or 50%) solution. Basically, the original algorithm found the clustering solution with the best (from among many seeds) log likelihood score. We have extended the algorithm to then take that "near optimal" clustering solution and iterate through the data, sampling motifs to determine if motifs are moving between clusters or sample into the same cluster repeatedly. The sampling solution of predicted regulons was used as data for the study described in Genome Research, 15:312-320 The even cluster models are here and the odd cluster models are here.


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