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Transcription factor regulation can be accurately predicted from the presence of target gene signatures in microarray gene expression data
Deciphering transcription factor networks from microarray data remains difficult. This study presents a simple method to infer the regulation of transcription factors from microarray data based on well-characterized target genes. We generated a catalog containing transcription factors associated wit...
Autores principales: | , , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2010
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2887972/ https://www.ncbi.nlm.nih.gov/pubmed/20215436 http://dx.doi.org/10.1093/nar/gkq149 |
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author | Essaghir, Ahmed Toffalini, Federica Knoops, Laurent Kallin, Anders van Helden, Jacques Demoulin, Jean-Baptiste |
author_facet | Essaghir, Ahmed Toffalini, Federica Knoops, Laurent Kallin, Anders van Helden, Jacques Demoulin, Jean-Baptiste |
author_sort | Essaghir, Ahmed |
collection | PubMed |
description | Deciphering transcription factor networks from microarray data remains difficult. This study presents a simple method to infer the regulation of transcription factors from microarray data based on well-characterized target genes. We generated a catalog containing transcription factors associated with 2720 target genes and 6401 experimentally validated regulations. When it was available, a distinction between transcriptional activation and inhibition was included for each regulation. Next, we built a tool (www.tfacts.org) that compares submitted gene lists with target genes in the catalog to detect regulated transcription factors. TFactS was validated with published lists of regulated genes in various models and compared to tools based on in silico promoter analysis. We next analyzed the NCI60 cancer microarray data set and showed the regulation of SOX10, MITF and JUN in melanomas. We then performed microarray experiments comparing gene expression response of human fibroblasts stimulated by different growth factors. TFactS predicted the specific activation of Signal transducer and activator of transcription factors by PDGF-BB, which was confirmed experimentally. Our results show that the expression levels of transcription factor target genes constitute a robust signature for transcription factor regulation, and can be efficiently used for microarray data mining. |
format | Text |
id | pubmed-2887972 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-28879722010-06-22 Transcription factor regulation can be accurately predicted from the presence of target gene signatures in microarray gene expression data Essaghir, Ahmed Toffalini, Federica Knoops, Laurent Kallin, Anders van Helden, Jacques Demoulin, Jean-Baptiste Nucleic Acids Res Methods Online Deciphering transcription factor networks from microarray data remains difficult. This study presents a simple method to infer the regulation of transcription factors from microarray data based on well-characterized target genes. We generated a catalog containing transcription factors associated with 2720 target genes and 6401 experimentally validated regulations. When it was available, a distinction between transcriptional activation and inhibition was included for each regulation. Next, we built a tool (www.tfacts.org) that compares submitted gene lists with target genes in the catalog to detect regulated transcription factors. TFactS was validated with published lists of regulated genes in various models and compared to tools based on in silico promoter analysis. We next analyzed the NCI60 cancer microarray data set and showed the regulation of SOX10, MITF and JUN in melanomas. We then performed microarray experiments comparing gene expression response of human fibroblasts stimulated by different growth factors. TFactS predicted the specific activation of Signal transducer and activator of transcription factors by PDGF-BB, which was confirmed experimentally. Our results show that the expression levels of transcription factor target genes constitute a robust signature for transcription factor regulation, and can be efficiently used for microarray data mining. Oxford University Press 2010-06 2010-03-09 /pmc/articles/PMC2887972/ /pubmed/20215436 http://dx.doi.org/10.1093/nar/gkq149 Text en © The Author(s) 2010. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/2.5 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Methods Online Essaghir, Ahmed Toffalini, Federica Knoops, Laurent Kallin, Anders van Helden, Jacques Demoulin, Jean-Baptiste Transcription factor regulation can be accurately predicted from the presence of target gene signatures in microarray gene expression data |
title | Transcription factor regulation can be accurately predicted from the presence of target gene signatures in microarray gene expression data |
title_full | Transcription factor regulation can be accurately predicted from the presence of target gene signatures in microarray gene expression data |
title_fullStr | Transcription factor regulation can be accurately predicted from the presence of target gene signatures in microarray gene expression data |
title_full_unstemmed | Transcription factor regulation can be accurately predicted from the presence of target gene signatures in microarray gene expression data |
title_short | Transcription factor regulation can be accurately predicted from the presence of target gene signatures in microarray gene expression data |
title_sort | transcription factor regulation can be accurately predicted from the presence of target gene signatures in microarray gene expression data |
topic | Methods Online |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2887972/ https://www.ncbi.nlm.nih.gov/pubmed/20215436 http://dx.doi.org/10.1093/nar/gkq149 |
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