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3DCoop: An approach for computational inference of cell-type-specific transcriptional regulators cooperation in 3D chromatin

Precise identification of context-specific transcriptional regulators (TRs) cooperation facilitates the understanding of complex gene regulation. However, previous methods are highly reliant on the availability of ChIPped TRs. Here, we provide a protocol for running 3DCoop, a pipeline for computatio...

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Detalles Bibliográficos
Autores principales: Yi, Xianfu, Luo, Menghan, Feng, Xiangling, Zhou, Yao, Wang, Jianhua, Li, Mulin Jun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9114683/
https://www.ncbi.nlm.nih.gov/pubmed/35600920
http://dx.doi.org/10.1016/j.xpro.2022.101382
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author Yi, Xianfu
Luo, Menghan
Feng, Xiangling
Zhou, Yao
Wang, Jianhua
Li, Mulin Jun
author_facet Yi, Xianfu
Luo, Menghan
Feng, Xiangling
Zhou, Yao
Wang, Jianhua
Li, Mulin Jun
author_sort Yi, Xianfu
collection PubMed
description Precise identification of context-specific transcriptional regulators (TRs) cooperation facilitates the understanding of complex gene regulation. However, previous methods are highly reliant on the availability of ChIPped TRs. Here, we provide a protocol for running 3DCoop, a pipeline for computational inference of cell type-specific TR cooperation in 3D chromatin by integrating TR motifs, open chromatin profiles, gene expression, and chromatin loops. 3DCoop provides a feasible solution to study the potential interplay among TRs across multiple human or mouse tissue/cell types. For complete details on the use and execution of this protocol, please refer to Yi et al. (2021).
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spelling pubmed-91146832022-05-19 3DCoop: An approach for computational inference of cell-type-specific transcriptional regulators cooperation in 3D chromatin Yi, Xianfu Luo, Menghan Feng, Xiangling Zhou, Yao Wang, Jianhua Li, Mulin Jun STAR Protoc Protocol Precise identification of context-specific transcriptional regulators (TRs) cooperation facilitates the understanding of complex gene regulation. However, previous methods are highly reliant on the availability of ChIPped TRs. Here, we provide a protocol for running 3DCoop, a pipeline for computational inference of cell type-specific TR cooperation in 3D chromatin by integrating TR motifs, open chromatin profiles, gene expression, and chromatin loops. 3DCoop provides a feasible solution to study the potential interplay among TRs across multiple human or mouse tissue/cell types. For complete details on the use and execution of this protocol, please refer to Yi et al. (2021). Elsevier 2022-05-11 /pmc/articles/PMC9114683/ /pubmed/35600920 http://dx.doi.org/10.1016/j.xpro.2022.101382 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Protocol
Yi, Xianfu
Luo, Menghan
Feng, Xiangling
Zhou, Yao
Wang, Jianhua
Li, Mulin Jun
3DCoop: An approach for computational inference of cell-type-specific transcriptional regulators cooperation in 3D chromatin
title 3DCoop: An approach for computational inference of cell-type-specific transcriptional regulators cooperation in 3D chromatin
title_full 3DCoop: An approach for computational inference of cell-type-specific transcriptional regulators cooperation in 3D chromatin
title_fullStr 3DCoop: An approach for computational inference of cell-type-specific transcriptional regulators cooperation in 3D chromatin
title_full_unstemmed 3DCoop: An approach for computational inference of cell-type-specific transcriptional regulators cooperation in 3D chromatin
title_short 3DCoop: An approach for computational inference of cell-type-specific transcriptional regulators cooperation in 3D chromatin
title_sort 3dcoop: an approach for computational inference of cell-type-specific transcriptional regulators cooperation in 3d chromatin
topic Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9114683/
https://www.ncbi.nlm.nih.gov/pubmed/35600920
http://dx.doi.org/10.1016/j.xpro.2022.101382
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