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Localizing potentially active post-transcriptional regulations in the Ewing's sarcoma gene regulatory network
BACKGROUND: A wide range of techniques is now available for analyzing regulatory networks. Nonetheless, most of these techniques fail to interpret large-scale transcriptional data at the post-translational level. RESULTS: We address the question of using large-scale transcriptomic observation of a s...
Autores principales: | , , , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2010
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2987883/ https://www.ncbi.nlm.nih.gov/pubmed/21044309 http://dx.doi.org/10.1186/1752-0509-4-146 |
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author | Baumuratova, Tatiana Surdez, Didier Delyon, Bernard Stoll, Gautier Delattre, Olivier Radulescu, Ovidiu Siegel, Anne |
author_facet | Baumuratova, Tatiana Surdez, Didier Delyon, Bernard Stoll, Gautier Delattre, Olivier Radulescu, Ovidiu Siegel, Anne |
author_sort | Baumuratova, Tatiana |
collection | PubMed |
description | BACKGROUND: A wide range of techniques is now available for analyzing regulatory networks. Nonetheless, most of these techniques fail to interpret large-scale transcriptional data at the post-translational level. RESULTS: We address the question of using large-scale transcriptomic observation of a system perturbation to analyze a regulatory network which contained several types of interactions - transcriptional and post-translational. Our method consisted of post-processing the outputs of an open-source tool named BioQuali - an automatic constraint-based analysis mimicking biologist's local reasoning on a large scale. The post-processing relied on differences in the behavior of the transcriptional and post-translational levels in the network. As a case study, we analyzed a network representation of the genes and proteins controlled by an oncogene in the context of Ewing's sarcoma. The analysis allowed us to pinpoint active interactions specific to this cancer. We also identified the parts of the network which were incomplete and should be submitted for further investigation. CONCLUSIONS: The proposed approach is effective for the qualitative analysis of cancer networks. It allows the integrative use of experimental data of various types in order to identify the specific information that should be considered a priority in the initial - and possibly very large - experimental dataset. Iteratively, new dataset can be introduced into the analysis to improve the network representation and make it more specific. |
format | Text |
id | pubmed-2987883 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-29878832010-11-19 Localizing potentially active post-transcriptional regulations in the Ewing's sarcoma gene regulatory network Baumuratova, Tatiana Surdez, Didier Delyon, Bernard Stoll, Gautier Delattre, Olivier Radulescu, Ovidiu Siegel, Anne BMC Syst Biol Methodology Article BACKGROUND: A wide range of techniques is now available for analyzing regulatory networks. Nonetheless, most of these techniques fail to interpret large-scale transcriptional data at the post-translational level. RESULTS: We address the question of using large-scale transcriptomic observation of a system perturbation to analyze a regulatory network which contained several types of interactions - transcriptional and post-translational. Our method consisted of post-processing the outputs of an open-source tool named BioQuali - an automatic constraint-based analysis mimicking biologist's local reasoning on a large scale. The post-processing relied on differences in the behavior of the transcriptional and post-translational levels in the network. As a case study, we analyzed a network representation of the genes and proteins controlled by an oncogene in the context of Ewing's sarcoma. The analysis allowed us to pinpoint active interactions specific to this cancer. We also identified the parts of the network which were incomplete and should be submitted for further investigation. CONCLUSIONS: The proposed approach is effective for the qualitative analysis of cancer networks. It allows the integrative use of experimental data of various types in order to identify the specific information that should be considered a priority in the initial - and possibly very large - experimental dataset. Iteratively, new dataset can be introduced into the analysis to improve the network representation and make it more specific. BioMed Central 2010-11-02 /pmc/articles/PMC2987883/ /pubmed/21044309 http://dx.doi.org/10.1186/1752-0509-4-146 Text en Copyright ©2010 Baumuratova 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 | Methodology Article Baumuratova, Tatiana Surdez, Didier Delyon, Bernard Stoll, Gautier Delattre, Olivier Radulescu, Ovidiu Siegel, Anne Localizing potentially active post-transcriptional regulations in the Ewing's sarcoma gene regulatory network |
title | Localizing potentially active post-transcriptional regulations in the Ewing's sarcoma gene regulatory network |
title_full | Localizing potentially active post-transcriptional regulations in the Ewing's sarcoma gene regulatory network |
title_fullStr | Localizing potentially active post-transcriptional regulations in the Ewing's sarcoma gene regulatory network |
title_full_unstemmed | Localizing potentially active post-transcriptional regulations in the Ewing's sarcoma gene regulatory network |
title_short | Localizing potentially active post-transcriptional regulations in the Ewing's sarcoma gene regulatory network |
title_sort | localizing potentially active post-transcriptional regulations in the ewing's sarcoma gene regulatory network |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2987883/ https://www.ncbi.nlm.nih.gov/pubmed/21044309 http://dx.doi.org/10.1186/1752-0509-4-146 |
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