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

BACKGROUND: 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 tr...

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Detalles Bibliográficos
Autores principales: Nagaraj, Vijayalakshmi H, O'Flanagan, Ruadhan A, Bruning, Adrian R, Mathias, Jonathan R, Vershon, Andrew K, Sengupta, Anirvan M
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2004
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC517709/
https://www.ncbi.nlm.nih.gov/pubmed/15331021
http://dx.doi.org/10.1186/1471-2164-5-59
Descripción
Sumario:BACKGROUND: 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. RESULTS: 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-α2 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-α2 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-α2 complex, as well as a1-α2 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. CONCLUSIONS: These findings show advantages of combining these two forms of data to investigate the mechanism of co-regulation of specific sets of genes.