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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...
Autores principales: | , , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2004
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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 |
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author | Nagaraj, Vijayalakshmi H O'Flanagan, Ruadhan A Bruning, Adrian R Mathias, Jonathan R Vershon, Andrew K Sengupta, Anirvan M |
author_facet | Nagaraj, Vijayalakshmi H O'Flanagan, Ruadhan A Bruning, Adrian R Mathias, Jonathan R Vershon, Andrew K Sengupta, Anirvan M |
author_sort | Nagaraj, Vijayalakshmi H |
collection | PubMed |
description | 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. |
format | Text |
id | pubmed-517709 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2004 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-5177092004-09-19 Combined analysis of expression data and transcription factor binding sites in the yeast genome Nagaraj, Vijayalakshmi H O'Flanagan, Ruadhan A Bruning, Adrian R Mathias, Jonathan R Vershon, Andrew K Sengupta, Anirvan M BMC Genomics Research Article 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. BioMed Central 2004-08-26 /pmc/articles/PMC517709/ /pubmed/15331021 http://dx.doi.org/10.1186/1471-2164-5-59 Text en Copyright © 2004 Nagaraj et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open-access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Nagaraj, Vijayalakshmi H O'Flanagan, Ruadhan A Bruning, Adrian R Mathias, Jonathan R Vershon, Andrew K Sengupta, Anirvan M Combined analysis of expression data and transcription factor binding sites in the yeast genome |
title | Combined analysis of expression data and transcription factor binding sites in the yeast genome |
title_full | Combined analysis of expression data and transcription factor binding sites in the yeast genome |
title_fullStr | Combined analysis of expression data and transcription factor binding sites in the yeast genome |
title_full_unstemmed | Combined analysis of expression data and transcription factor binding sites in the yeast genome |
title_short | Combined analysis of expression data and transcription factor binding sites in the yeast genome |
title_sort | combined analysis of expression data and transcription factor binding sites in the yeast genome |
topic | Research Article |
url | 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 |
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