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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...

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Detalles Bibliográficos
Autores principales: McMurray, Helene R., Stern, Harry A., Ambeskovic, Aslihan, Land, Hartmut, McCall, Matthew N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
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).
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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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