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Detecting clusters of transcription factors based on a nonhomogeneous poisson process model
BACKGROUND: Rapidly growing genome-wide ChIP-seq data have provided unprecedented opportunities to explore transcription factor (TF) binding under various cellular conditions. Despite the rich resources, development of analytical methods for studying the interaction among TFs in gene regulation stil...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9738027/ https://www.ncbi.nlm.nih.gov/pubmed/36494794 http://dx.doi.org/10.1186/s12859-022-05090-2 |
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author | Wu, Xiaowei Liu, Shicheng Liang, Guanying |
author_facet | Wu, Xiaowei Liu, Shicheng Liang, Guanying |
author_sort | Wu, Xiaowei |
collection | PubMed |
description | BACKGROUND: Rapidly growing genome-wide ChIP-seq data have provided unprecedented opportunities to explore transcription factor (TF) binding under various cellular conditions. Despite the rich resources, development of analytical methods for studying the interaction among TFs in gene regulation still lags behind. RESULTS: In order to address cooperative TF binding and detect TF clusters with coordinative functions, we have developed novel computational methods based on clustering the sample paths of nonhomogeneous Poisson processes. Simulation studies demonstrated the capability of these methods to accurately detect TF clusters and uncover the hierarchy of TF interactions. A further application to the multiple-TF ChIP-seq data in mouse embryonic stem cells (ESCs) showed that our methods identified the cluster of core ESC regulators reported in the literature and provided new insights on functional implications of transcrisptional regulatory modules. CONCLUSIONS: Effective analytical tools are essential for studying protein-DNA relations. Information derived from this research will help us better understand the orchestration of transcription factors in gene regulation processes. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-022-05090-2. |
format | Online Article Text |
id | pubmed-9738027 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-97380272022-12-11 Detecting clusters of transcription factors based on a nonhomogeneous poisson process model Wu, Xiaowei Liu, Shicheng Liang, Guanying BMC Bioinformatics Research BACKGROUND: Rapidly growing genome-wide ChIP-seq data have provided unprecedented opportunities to explore transcription factor (TF) binding under various cellular conditions. Despite the rich resources, development of analytical methods for studying the interaction among TFs in gene regulation still lags behind. RESULTS: In order to address cooperative TF binding and detect TF clusters with coordinative functions, we have developed novel computational methods based on clustering the sample paths of nonhomogeneous Poisson processes. Simulation studies demonstrated the capability of these methods to accurately detect TF clusters and uncover the hierarchy of TF interactions. A further application to the multiple-TF ChIP-seq data in mouse embryonic stem cells (ESCs) showed that our methods identified the cluster of core ESC regulators reported in the literature and provided new insights on functional implications of transcrisptional regulatory modules. CONCLUSIONS: Effective analytical tools are essential for studying protein-DNA relations. Information derived from this research will help us better understand the orchestration of transcription factors in gene regulation processes. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-022-05090-2. BioMed Central 2022-12-09 /pmc/articles/PMC9738027/ /pubmed/36494794 http://dx.doi.org/10.1186/s12859-022-05090-2 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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 | Research Wu, Xiaowei Liu, Shicheng Liang, Guanying Detecting clusters of transcription factors based on a nonhomogeneous poisson process model |
title | Detecting clusters of transcription factors based on a nonhomogeneous poisson process model |
title_full | Detecting clusters of transcription factors based on a nonhomogeneous poisson process model |
title_fullStr | Detecting clusters of transcription factors based on a nonhomogeneous poisson process model |
title_full_unstemmed | Detecting clusters of transcription factors based on a nonhomogeneous poisson process model |
title_short | Detecting clusters of transcription factors based on a nonhomogeneous poisson process model |
title_sort | detecting clusters of transcription factors based on a nonhomogeneous poisson process model |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9738027/ https://www.ncbi.nlm.nih.gov/pubmed/36494794 http://dx.doi.org/10.1186/s12859-022-05090-2 |
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