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Identifying transcription factor complexes and their roles
Motivation: Eukaryotic gene expression is controlled through molecular logic circuits that combine regulatory signals of many different factors. In particular, complexation of transcription factors (TFs) and other regulatory proteins is a prevailing and highly conserved mechanism of signal integrati...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4147901/ https://www.ncbi.nlm.nih.gov/pubmed/25161228 http://dx.doi.org/10.1093/bioinformatics/btu448 |
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author | Will, Thorsten Helms, Volkhard |
author_facet | Will, Thorsten Helms, Volkhard |
author_sort | Will, Thorsten |
collection | PubMed |
description | Motivation: Eukaryotic gene expression is controlled through molecular logic circuits that combine regulatory signals of many different factors. In particular, complexation of transcription factors (TFs) and other regulatory proteins is a prevailing and highly conserved mechanism of signal integration within critical regulatory pathways and enables us to infer controlled genes as well as the exerted regulatory mechanism. Common approaches for protein complex prediction that only use protein interaction networks, however, are designed to detect self-contained functional complexes and have difficulties to reveal dynamic combinatorial assemblies of physically interacting proteins. Results: We developed the novel algorithm DACO that combines protein–protein interaction networks and domain–domain interaction networks with the cluster-quality metric cohesiveness. The metric is locally maximized on the holistic level of protein interactions, and connectivity constraints on the domain level are used to account for the exclusive and thus inherently combinatorial nature of the interactions within such assemblies. When applied to predicting TF complexes in the yeast Saccharomyces cerevisiae, the proposed approach outperformed popular complex prediction methods by far. Furthermore, we were able to assign many of the predictions to target genes, as well as to a potential regulatory effect in agreement with literature evidence. Availability and implementation: A prototype implementation is freely available at https://sourceforge.net/projects/dacoalgorithm/. Contact: volkhard.helms@bioinformatik.uni-saarland.de Supplementary information: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-4147901 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-41479012014-09-02 Identifying transcription factor complexes and their roles Will, Thorsten Helms, Volkhard Bioinformatics Eccb 2014 Proceedings Papers Committee Motivation: Eukaryotic gene expression is controlled through molecular logic circuits that combine regulatory signals of many different factors. In particular, complexation of transcription factors (TFs) and other regulatory proteins is a prevailing and highly conserved mechanism of signal integration within critical regulatory pathways and enables us to infer controlled genes as well as the exerted regulatory mechanism. Common approaches for protein complex prediction that only use protein interaction networks, however, are designed to detect self-contained functional complexes and have difficulties to reveal dynamic combinatorial assemblies of physically interacting proteins. Results: We developed the novel algorithm DACO that combines protein–protein interaction networks and domain–domain interaction networks with the cluster-quality metric cohesiveness. The metric is locally maximized on the holistic level of protein interactions, and connectivity constraints on the domain level are used to account for the exclusive and thus inherently combinatorial nature of the interactions within such assemblies. When applied to predicting TF complexes in the yeast Saccharomyces cerevisiae, the proposed approach outperformed popular complex prediction methods by far. Furthermore, we were able to assign many of the predictions to target genes, as well as to a potential regulatory effect in agreement with literature evidence. Availability and implementation: A prototype implementation is freely available at https://sourceforge.net/projects/dacoalgorithm/. Contact: volkhard.helms@bioinformatik.uni-saarland.de Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press 2014-09-01 2014-08-22 /pmc/articles/PMC4147901/ /pubmed/25161228 http://dx.doi.org/10.1093/bioinformatics/btu448 Text en © The Author 2014. 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 non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Eccb 2014 Proceedings Papers Committee Will, Thorsten Helms, Volkhard Identifying transcription factor complexes and their roles |
title | Identifying transcription factor complexes and their roles |
title_full | Identifying transcription factor complexes and their roles |
title_fullStr | Identifying transcription factor complexes and their roles |
title_full_unstemmed | Identifying transcription factor complexes and their roles |
title_short | Identifying transcription factor complexes and their roles |
title_sort | identifying transcription factor complexes and their roles |
topic | Eccb 2014 Proceedings Papers Committee |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4147901/ https://www.ncbi.nlm.nih.gov/pubmed/25161228 http://dx.doi.org/10.1093/bioinformatics/btu448 |
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