Combined analysis of expression data and transcription factor binding sites in the yeast genome

• Published in: BMC genomics Vol. 5; no. 1; p. 59
• Main Authors: Nagaraj, Vijayalakshmi H, O'Flanagan, Ruadhan A, Bruning, Adrian R, Mathias, Jonathan R, Vershon, Andrew K, Sengupta, Anirvan M
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 26.08.2004; BioMed Central; BMC
• Subjects: Algorithms; Binding Sites - genetics; Chromosome Mapping - methods; Computational Biology - methods; Diploidy; DNA Mutational Analysis - statistics & numerical data; Gene Expression Profiling - statistics & numerical data; Gene Expression Regulation, Fungal - genetics; Genome, Fungal; Haploidy; Mutation - genetics; Oligonucleotide Array Sequence Analysis - statistics & numerical data; Predictive Value of Tests; Promoter Regions, Genetic - genetics; Saccharomyces cerevisiae; Saccharomyces cerevisiae - genetics; Software; Transcription Factors - genetics
• ISSN: 1471-2164; 1471-2164
• DOI: 10.1186/1471-2164-5-59

The analysis of gene expression using DNA microarrays provides genome wide profiles of the genes controlled by the presence or absence of a specific transcription factor. However, the question arises of whether a change in the level of transcription of a specific gene is caused by the transcription factor acting directly at the promoter of the gene or through regulation of other transcription factors working at the promoter. To address this problem we have devised a computational method that combines microarray expression and site preference data. We have tested this approach by identifying functional targets of the a1-alpha2 complex, which represses haploid-specific genes in the yeast Saccharomyces cerevisiae. Our analysis identified many known or suspected haploid-specific genes that are direct targets of the a1-alpha2 complex, as well as a number of previously uncharacterized targets. We were also able to identify a number of haploid-specific genes which do not appear to be direct targets of the a1-alpha2 complex, as well as a1-alpha2 target sites that do not repress transcription of nearby genes. Our method has a much lower false positive rate when compared to some of the conventional bioinformatic approaches. These findings show advantages of combining these two forms of data to investigate the mechanism of co-regulation of specific sets of genes.