MOTIVATION: Finding putative transcription factor binding sites in the upstream sequences of similarly expressed genes has recently become a subject of intensive studies. In this paper we investigate how much gene expression regulation can be attributed to the presence of various binding sites in the gene promoters by correlating the binding sites and the changes in gene expression resulting from gene disruptions (e.g. knockouts). RESULTS: We have developed a data analysis method for comparing mRNA measurements of gene disruption experiments with information about gene promoters. The method was applied to a well-known dataset to uncover correlations between known transcription factor binding site motifs in the upstream regions of all S. cerevisiae genes and the gene expression changes in various gene disruption experiments. The possible explanations of the correlations were categorized and analyzed using e.g. expression cascades. Several correlations turned out to be consistent with existing biological knowledge while some new ones suggest themselves for further study. AVAILABILITY: The resulting tables are available at http://www.cs.helsinki.fi/u/kpalin/CorrDisrupt/.
MOTIVATION: Finding putative transcription factor binding sites in the upstream sequences of similarly expressed genes has recently become a subject of intensive studies. In this paper we investigate how much gene expression regulation can be attributed to the presence of various binding sites in the gene promoters by correlating the binding sites and the changes in gene expression resulting from gene disruptions (e.g. knockouts). RESULTS: We have developed a data analysis method for comparing mRNA measurements of gene disruption experiments with information about gene promoters. The method was applied to a well-known dataset to uncover correlations between known transcription factor binding site motifs in the upstream regions of all S. cerevisiae genes and the gene expression changes in various gene disruption experiments. The possible explanations of the correlations were categorized and analyzed using e.g. expression cascades. Several correlations turned out to be consistent with existing biological knowledge while some new ones suggest themselves for further study. AVAILABILITY: The resulting tables are available at http://www.cs.helsinki.fi/u/kpalin/CorrDisrupt/.
Authors: Thomas Schlitt; Kimmo Palin; Johan Rung; Sabine Dietmann; Michael Lappe; Esko Ukkonen; Alvis Brazma Journal: Genome Res Date: 2003-12 Impact factor: 9.043
Authors: Jüri Reimand; Juan M Vaquerizas; Annabel E Todd; Jaak Vilo; Nicholas M Luscombe Journal: Nucleic Acids Res Date: 2010-04-12 Impact factor: 16.971