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Unveiling combinatorial regulation through the combination of ChIP information and in silico cis-regulatory module detection
Computationally retrieving biologically relevant cis-regulatory modules (CRMs) is not straightforward. Because of the large number of candidates and the imperfection of the screening methods, many spurious CRMs are detected that are as high scoring as the biologically true ones. Using ChIP-informati...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3384348/ https://www.ncbi.nlm.nih.gov/pubmed/22422841 http://dx.doi.org/10.1093/nar/gks237 |
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author | Sun, Hong Guns, Tias Fierro, Ana Carolina Thorrez, Lieven Nijssen, Siegfried Marchal, Kathleen |
author_facet | Sun, Hong Guns, Tias Fierro, Ana Carolina Thorrez, Lieven Nijssen, Siegfried Marchal, Kathleen |
author_sort | Sun, Hong |
collection | PubMed |
description | Computationally retrieving biologically relevant cis-regulatory modules (CRMs) is not straightforward. Because of the large number of candidates and the imperfection of the screening methods, many spurious CRMs are detected that are as high scoring as the biologically true ones. Using ChIP-information allows not only to reduce the regions in which the binding sites of the assayed transcription factor (TF) should be located, but also allows restricting the valid CRMs to those that contain the assayed TF (here referred to as applying CRM detection in a query-based mode). In this study, we show that exploiting ChIP-information in a query-based way makes in silico CRM detection a much more feasible endeavor. To be able to handle the large datasets, the query-based setting and other specificities proper to CRM detection on ChIP-Seq based data, we developed a novel powerful CRM detection method ‘CPModule’. By applying it on a well-studied ChIP-Seq data set involved in self-renewal of mouse embryonic stem cells, we demonstrate how our tool can recover combinatorial regulation of five known TFs that are key in the self-renewal of mouse embryonic stem cells. Additionally, we make a number of new predictions on combinatorial regulation of these five key TFs with other TFs documented in TRANSFAC. |
format | Online Article Text |
id | pubmed-3384348 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-33843482012-06-28 Unveiling combinatorial regulation through the combination of ChIP information and in silico cis-regulatory module detection Sun, Hong Guns, Tias Fierro, Ana Carolina Thorrez, Lieven Nijssen, Siegfried Marchal, Kathleen Nucleic Acids Res Methods Online Computationally retrieving biologically relevant cis-regulatory modules (CRMs) is not straightforward. Because of the large number of candidates and the imperfection of the screening methods, many spurious CRMs are detected that are as high scoring as the biologically true ones. Using ChIP-information allows not only to reduce the regions in which the binding sites of the assayed transcription factor (TF) should be located, but also allows restricting the valid CRMs to those that contain the assayed TF (here referred to as applying CRM detection in a query-based mode). In this study, we show that exploiting ChIP-information in a query-based way makes in silico CRM detection a much more feasible endeavor. To be able to handle the large datasets, the query-based setting and other specificities proper to CRM detection on ChIP-Seq based data, we developed a novel powerful CRM detection method ‘CPModule’. By applying it on a well-studied ChIP-Seq data set involved in self-renewal of mouse embryonic stem cells, we demonstrate how our tool can recover combinatorial regulation of five known TFs that are key in the self-renewal of mouse embryonic stem cells. Additionally, we make a number of new predictions on combinatorial regulation of these five key TFs with other TFs documented in TRANSFAC. Oxford University Press 2012-07 2012-03-15 /pmc/articles/PMC3384348/ /pubmed/22422841 http://dx.doi.org/10.1093/nar/gks237 Text en © The Author(s) 2012. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/3.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Methods Online Sun, Hong Guns, Tias Fierro, Ana Carolina Thorrez, Lieven Nijssen, Siegfried Marchal, Kathleen Unveiling combinatorial regulation through the combination of ChIP information and in silico cis-regulatory module detection |
title | Unveiling combinatorial regulation through the combination of ChIP information and in silico cis-regulatory module detection |
title_full | Unveiling combinatorial regulation through the combination of ChIP information and in silico cis-regulatory module detection |
title_fullStr | Unveiling combinatorial regulation through the combination of ChIP information and in silico cis-regulatory module detection |
title_full_unstemmed | Unveiling combinatorial regulation through the combination of ChIP information and in silico cis-regulatory module detection |
title_short | Unveiling combinatorial regulation through the combination of ChIP information and in silico cis-regulatory module detection |
title_sort | unveiling combinatorial regulation through the combination of chip information and in silico cis-regulatory module detection |
topic | Methods Online |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3384348/ https://www.ncbi.nlm.nih.gov/pubmed/22422841 http://dx.doi.org/10.1093/nar/gks237 |
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