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Integrating binding and expression data to predict transcription factors combined function
BACKGROUND: Transcription factor binding to the regulatory region of a gene induces or represses its gene expression. Transcription factors share their binding sites with other factors, co-factors and/or DNA-binding proteins. These proteins form complexes which bind to the DNA as one-units. The bind...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7487729/ https://www.ncbi.nlm.nih.gov/pubmed/32894066 http://dx.doi.org/10.1186/s12864-020-06977-1 |
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author | Ahmed, Mahmoud Min, Do Sik Kim, Deok Ryong |
author_facet | Ahmed, Mahmoud Min, Do Sik Kim, Deok Ryong |
author_sort | Ahmed, Mahmoud |
collection | PubMed |
description | BACKGROUND: Transcription factor binding to the regulatory region of a gene induces or represses its gene expression. Transcription factors share their binding sites with other factors, co-factors and/or DNA-binding proteins. These proteins form complexes which bind to the DNA as one-units. The binding of two factors to a shared site does not always lead to a functional interaction. RESULTS: We propose a method to predict the combined functions of two factors using comparable binding and expression data (target). We based this method on binding and expression target analysis (BETA), which we re-implemented in R and extended for this purpose. target ranks the factor’s targets by importance and predicts the dominant type of interaction between two transcription factors. We applied the method to simulated and real datasets of transcription factor-binding sites and gene expression under perturbation of factors. We found that Yin Yang 1 transcription factor (YY1) and YY2 have antagonistic and independent regulatory targets in HeLa cells, but they may cooperate on a few shared targets. CONCLUSION: We developed an R package and a web application to integrate binding (ChIP-seq) and expression (microarrays or RNA-seq) data to determine the cooperative or competitive combined function of two transcription factors. |
format | Online Article Text |
id | pubmed-7487729 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-74877292020-09-16 Integrating binding and expression data to predict transcription factors combined function Ahmed, Mahmoud Min, Do Sik Kim, Deok Ryong BMC Genomics Software BACKGROUND: Transcription factor binding to the regulatory region of a gene induces or represses its gene expression. Transcription factors share their binding sites with other factors, co-factors and/or DNA-binding proteins. These proteins form complexes which bind to the DNA as one-units. The binding of two factors to a shared site does not always lead to a functional interaction. RESULTS: We propose a method to predict the combined functions of two factors using comparable binding and expression data (target). We based this method on binding and expression target analysis (BETA), which we re-implemented in R and extended for this purpose. target ranks the factor’s targets by importance and predicts the dominant type of interaction between two transcription factors. We applied the method to simulated and real datasets of transcription factor-binding sites and gene expression under perturbation of factors. We found that Yin Yang 1 transcription factor (YY1) and YY2 have antagonistic and independent regulatory targets in HeLa cells, but they may cooperate on a few shared targets. CONCLUSION: We developed an R package and a web application to integrate binding (ChIP-seq) and expression (microarrays or RNA-seq) data to determine the cooperative or competitive combined function of two transcription factors. BioMed Central 2020-09-07 /pmc/articles/PMC7487729/ /pubmed/32894066 http://dx.doi.org/10.1186/s12864-020-06977-1 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Software Ahmed, Mahmoud Min, Do Sik Kim, Deok Ryong Integrating binding and expression data to predict transcription factors combined function |
title | Integrating binding and expression data to predict transcription factors combined function |
title_full | Integrating binding and expression data to predict transcription factors combined function |
title_fullStr | Integrating binding and expression data to predict transcription factors combined function |
title_full_unstemmed | Integrating binding and expression data to predict transcription factors combined function |
title_short | Integrating binding and expression data to predict transcription factors combined function |
title_sort | integrating binding and expression data to predict transcription factors combined function |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7487729/ https://www.ncbi.nlm.nih.gov/pubmed/32894066 http://dx.doi.org/10.1186/s12864-020-06977-1 |
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