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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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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. |
format | Online Article Text |
id | pubmed-9853317 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
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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