Mining for Putative Regulatory Elements in the Yeast Genome Using Gene Expression Data

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 patte...

• Type: Conference
• Year: 2000
• Where: ISMB
• Authors: Jaak Vilo, Alvis Brazma, Inge Jonassen, Alan J. Robinson, Esko Ukkonen
• Keywords: Computational Biology, Consensus Patterns, ISMB 2000, Regulatory Signals, Significant Patterns