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Multiomics data integration to reveal chromatin remodeling and reorganization induced by gene mutational synergy
Recurrent gene mutations often cooperate in a predefined stepwise and synergistic manner to alter global transcription, through directly or indirectly remodeling epigenetic landscape on linear and three-dimensional (3D) scales. Here, we present a multiomics data integration approach to investigate t...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9579702/ https://www.ncbi.nlm.nih.gov/pubmed/36242770 http://dx.doi.org/10.1016/j.xpro.2022.101770 |
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author | Yun, Haiyang Vohra, Shabana Lara-Astiaso, David Huntly, Brian J.P. |
author_facet | Yun, Haiyang Vohra, Shabana Lara-Astiaso, David Huntly, Brian J.P. |
author_sort | Yun, Haiyang |
collection | PubMed |
description | Recurrent gene mutations often cooperate in a predefined stepwise and synergistic manner to alter global transcription, through directly or indirectly remodeling epigenetic landscape on linear and three-dimensional (3D) scales. Here, we present a multiomics data integration approach to investigate the impact of gene mutational synergy on transcription, chromatin states, and 3D chromatin organization in a murine leukemia model. This protocol provides an executable framework to study epigenetic remodeling induced by cooperating gene mutations and to identify the critical regulatory network involved. For complete details on the use and execution of this protocol, please refer to Yun et al. (2021). |
format | Online Article Text |
id | pubmed-9579702 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-95797022022-10-20 Multiomics data integration to reveal chromatin remodeling and reorganization induced by gene mutational synergy Yun, Haiyang Vohra, Shabana Lara-Astiaso, David Huntly, Brian J.P. STAR Protoc Protocol Recurrent gene mutations often cooperate in a predefined stepwise and synergistic manner to alter global transcription, through directly or indirectly remodeling epigenetic landscape on linear and three-dimensional (3D) scales. Here, we present a multiomics data integration approach to investigate the impact of gene mutational synergy on transcription, chromatin states, and 3D chromatin organization in a murine leukemia model. This protocol provides an executable framework to study epigenetic remodeling induced by cooperating gene mutations and to identify the critical regulatory network involved. For complete details on the use and execution of this protocol, please refer to Yun et al. (2021). Elsevier 2022-10-14 /pmc/articles/PMC9579702/ /pubmed/36242770 http://dx.doi.org/10.1016/j.xpro.2022.101770 Text en © 2022 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Protocol Yun, Haiyang Vohra, Shabana Lara-Astiaso, David Huntly, Brian J.P. Multiomics data integration to reveal chromatin remodeling and reorganization induced by gene mutational synergy |
title | Multiomics data integration to reveal chromatin remodeling and reorganization induced by gene mutational synergy |
title_full | Multiomics data integration to reveal chromatin remodeling and reorganization induced by gene mutational synergy |
title_fullStr | Multiomics data integration to reveal chromatin remodeling and reorganization induced by gene mutational synergy |
title_full_unstemmed | Multiomics data integration to reveal chromatin remodeling and reorganization induced by gene mutational synergy |
title_short | Multiomics data integration to reveal chromatin remodeling and reorganization induced by gene mutational synergy |
title_sort | multiomics data integration to reveal chromatin remodeling and reorganization induced by gene mutational synergy |
topic | Protocol |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9579702/ https://www.ncbi.nlm.nih.gov/pubmed/36242770 http://dx.doi.org/10.1016/j.xpro.2022.101770 |
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