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Protocol to use TopNet for gene regulatory network modeling using gene expression data from perturbation experiments
Inference of gene regulatory networks from gene perturbation experiments is the most reliable approach for investigating interdependence between genes. Here, we describe the initial gene perturbations, expression measurements, and preparation steps, followed by network modeling using TopNet. Summari...
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/PMC9529586/ https://www.ncbi.nlm.nih.gov/pubmed/36181678 http://dx.doi.org/10.1016/j.xpro.2022.101737 |
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author | McMurray, Helene R. Stern, Harry A. Ambeskovic, Aslihan Land, Hartmut McCall, Matthew N. |
author_facet | McMurray, Helene R. Stern, Harry A. Ambeskovic, Aslihan Land, Hartmut McCall, Matthew N. |
author_sort | McMurray, Helene R. |
collection | PubMed |
description | Inference of gene regulatory networks from gene perturbation experiments is the most reliable approach for investigating interdependence between genes. Here, we describe the initial gene perturbations, expression measurements, and preparation steps, followed by network modeling using TopNet. Summarization and visualization of the estimated networks and optional genetic testing of dependencies revealed by the network model are demonstrated. While developed for gene perturbation experiments, TopNet models data in which nodes are both perturbed and measured. For complete details on the use and execution of this protocol, please refer to McMurray et al. (2021). |
format | Online Article Text |
id | pubmed-9529586 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-95295862022-10-05 Protocol to use TopNet for gene regulatory network modeling using gene expression data from perturbation experiments McMurray, Helene R. Stern, Harry A. Ambeskovic, Aslihan Land, Hartmut McCall, Matthew N. STAR Protoc Protocol Inference of gene regulatory networks from gene perturbation experiments is the most reliable approach for investigating interdependence between genes. Here, we describe the initial gene perturbations, expression measurements, and preparation steps, followed by network modeling using TopNet. Summarization and visualization of the estimated networks and optional genetic testing of dependencies revealed by the network model are demonstrated. While developed for gene perturbation experiments, TopNet models data in which nodes are both perturbed and measured. For complete details on the use and execution of this protocol, please refer to McMurray et al. (2021). Elsevier 2022-09-30 /pmc/articles/PMC9529586/ /pubmed/36181678 http://dx.doi.org/10.1016/j.xpro.2022.101737 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 McMurray, Helene R. Stern, Harry A. Ambeskovic, Aslihan Land, Hartmut McCall, Matthew N. Protocol to use TopNet for gene regulatory network modeling using gene expression data from perturbation experiments |
title | Protocol to use TopNet for gene regulatory network modeling using gene expression data from perturbation experiments |
title_full | Protocol to use TopNet for gene regulatory network modeling using gene expression data from perturbation experiments |
title_fullStr | Protocol to use TopNet for gene regulatory network modeling using gene expression data from perturbation experiments |
title_full_unstemmed | Protocol to use TopNet for gene regulatory network modeling using gene expression data from perturbation experiments |
title_short | Protocol to use TopNet for gene regulatory network modeling using gene expression data from perturbation experiments |
title_sort | protocol to use topnet for gene regulatory network modeling using gene expression data from perturbation experiments |
topic | Protocol |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9529586/ https://www.ncbi.nlm.nih.gov/pubmed/36181678 http://dx.doi.org/10.1016/j.xpro.2022.101737 |
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