Probabilistic clustering of sequences: Inferring new bacterial regulons by comparative genomics

摘要

Genome-wide comparisons between enteric bacteria yield large sets of conserved putative regulatory sites on a gene-by-gene basis that need to be clustered into regulons. Using the assumption that regulatory sites can be represented as samples from weight ma- trices (WMs), we derive a unique probability distribution for assignments of sites into clusters. Our algorithm, "PROCSE" (prob- abilistic clustering of sequences), uses Monte Carlo sampling of this distribution to partition and align thousands of short DNA se- quences into clusters. The algorithm internally determines the number of clusters from the data and assigns significance to the resulting clusters.