Inference and validation of a large Saccharomyces cerevisiae cis regulatory motif set|
Wellcome Trust Sanger Institute, UK
Saccharomyces cerevisiae is a popular model organism for investigating eukaryotic cis-regulatory elements given its
compact genome and the breadth of resources and datasets available for its study. We conducted a computational
regulatory motif discovery study with the NestedMICA algorithm with the aim to infer a close to complete core promoter
motif dictionary of the S. cerevisiae genome (200 motifs from upstream sequences of 2000 genes, totalling a megabase of sequence).
We show an analysis of the motif sets to identify known regulatory motifs and motif families amongst
the set (81 of 200 found to be close matches to known motifs), and analyze variation patterns of the motif matches.
We also describe a novel gene expression aided motif inference algorithm based on NestedMICA and demonstrate its performance
with several S. cerevisiae gene expression datasets.