Mining for putative regulatory elements in the yeast genome using gene expression data.

Abstract

We have developed a set of methods and tools for automatic discovery of putative regulatory signals in genome sequences. The analysis pipeline consists of gene expression data clustering, sequence pattern discovery from upstream sequences of genes, a control experiment for pattern significance threshold limit detection, selection of interesting patterns, grouping of these patterns, representing the pattern groups in a concise form and evaluating the discovered putative signals against existing databases of regulatory signals. The pattern discovery is computationally the most expensive and crucial step. Our tool performs a rapid exhaustive search for a priori unknown statistically significant sequence patterns of unrestricted length. The statistical significance is determined for a set of sequences in each cluster with respect to a set of background sequences allowing the detection of subtle regulatory signals specific for each cluster. The potentially large number of significant patterns is reduced to a small number of groups by clustering them by mutual similarity. Automatically derived consensus patterns of these groups represent the results in a comprehensive way for a human investigator. We have performed a systematic analysis for the yeast Saccharomyces cerevisiae. We created a large number of independent clusterings of expression data simultaneously assessing the "goodness" of each cluster. For each of the over 52,000 clusters acquired in this way we discovered significant patterns in the upstream sequences of respective genes. We selected nearly 1,500 significant patterns by formal criteria and matched them against the experimentally mapped transcription factor binding sites in the SCPD database. We clustered the 1,500 patterns to 62 groups for which we derived automatically alignments and consensus patterns. Of these 62 groups 48 had patterns that have matching sites in SCPD database.