Probabilistic clustering of sequences: inferring new bacterial regulons by comparative genomics.

ABSTRACT: 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 matrices (WMs), we derive a unique probability distribution for assignments of sites into clusters. Our algorithm, "PROCSE" (probabilistic clustering of sequences), uses Monte Carlo sampling of this distribution to partition and align thousands of short DNA sequences into clusters. The algorithm internally determines the number of clusters from the data and assigns significance to the resulting clusters. We place theoretical limits on the ability of any algorithm to correctly cluster sequences drawn from WMs when these WMs are unknown. Our

SUBMITTER: van Nimwegen E 

PROVIDER: S-EPMC124229 | biostudies-literature | 2002 May

REPOSITORIES: biostudies-literature