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scMEGA: single-cell multi-omic enhancer-based gene regulatory network inference

SUMMARY: The increasing availability of single-cell multi-omics data allows to quantitatively characterize gene regulation. We here describe scMEGA (Single-cell Multiomic Enhancer-based Gene Regulatory Network Inference) that enables an end-to-end analysis of multi-omics data for gene regulatory net...

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Autores principales: Li, Zhijian, Nagai, James S, Kuppe, Christoph, Kramann, Rafael, Costa, Ivan G
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9853317/
https://www.ncbi.nlm.nih.gov/pubmed/36698768
http://dx.doi.org/10.1093/bioadv/vbad003
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author Li, Zhijian
Nagai, James S
Kuppe, Christoph
Kramann, Rafael
Costa, Ivan G
author_facet Li, Zhijian
Nagai, James S
Kuppe, Christoph
Kramann, Rafael
Costa, Ivan G
author_sort Li, Zhijian
collection PubMed
description SUMMARY: The increasing availability of single-cell multi-omics data allows to quantitatively characterize gene regulation. We here describe scMEGA (Single-cell Multiomic Enhancer-based Gene Regulatory Network Inference) that enables an end-to-end analysis of multi-omics data for gene regulatory network inference including modalities integration, trajectory analysis, enhancer-to-promoter association, network analysis and visualization. This enables to study the complex gene regulation mechanisms for dynamic biological processes, such as cellular differentiation and disease-driven cellular remodeling. We provide a case study on gene regulatory networks controlling myofibroblast activation in human myocardial infarction. AVAILABILITY AND IMPLEMENTATION: scMEGA is implemented in R, released under the MIT license and available from https://github.com/CostaLab/scMEGA. Tutorials are available from https://costalab.github.io/scMEGA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics Advances online.
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spelling pubmed-98533172023-01-24 scMEGA: single-cell multi-omic enhancer-based gene regulatory network inference Li, Zhijian Nagai, James S Kuppe, Christoph Kramann, Rafael Costa, Ivan G Bioinform Adv Application Note SUMMARY: The increasing availability of single-cell multi-omics data allows to quantitatively characterize gene regulation. We here describe scMEGA (Single-cell Multiomic Enhancer-based Gene Regulatory Network Inference) that enables an end-to-end analysis of multi-omics data for gene regulatory network inference including modalities integration, trajectory analysis, enhancer-to-promoter association, network analysis and visualization. This enables to study the complex gene regulation mechanisms for dynamic biological processes, such as cellular differentiation and disease-driven cellular remodeling. We provide a case study on gene regulatory networks controlling myofibroblast activation in human myocardial infarction. AVAILABILITY AND IMPLEMENTATION: scMEGA is implemented in R, released under the MIT license and available from https://github.com/CostaLab/scMEGA. Tutorials are available from https://costalab.github.io/scMEGA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics Advances online. Oxford University Press 2023-01-12 /pmc/articles/PMC9853317/ /pubmed/36698768 http://dx.doi.org/10.1093/bioadv/vbad003 Text en © The Author(s) 2023. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Application Note
Li, Zhijian
Nagai, James S
Kuppe, Christoph
Kramann, Rafael
Costa, Ivan G
scMEGA: single-cell multi-omic enhancer-based gene regulatory network inference
title scMEGA: single-cell multi-omic enhancer-based gene regulatory network inference
title_full scMEGA: single-cell multi-omic enhancer-based gene regulatory network inference
title_fullStr scMEGA: single-cell multi-omic enhancer-based gene regulatory network inference
title_full_unstemmed scMEGA: single-cell multi-omic enhancer-based gene regulatory network inference
title_short scMEGA: single-cell multi-omic enhancer-based gene regulatory network inference
title_sort scmega: single-cell multi-omic enhancer-based gene regulatory network inference
topic Application Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9853317/
https://www.ncbi.nlm.nih.gov/pubmed/36698768
http://dx.doi.org/10.1093/bioadv/vbad003
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