Protocol to use TopNet for gene regulatory network modeling using gene expression data from perturbation experiments

Helene R McMurray; Harry A Stern; Aslihan Ambeskovic; Hartmut Land; Matthew N McCall

doi:10.1016/j.xpro.2022.101737

Protocol to use TopNet for gene regulatory network modeling using gene expression data from perturbation experiments

STAR Protoc. 2022 Dec 16;3(4):101737. doi: 10.1016/j.xpro.2022.101737. Epub 2022 Sep 30.

Authors

Helene R McMurray¹, Harry A Stern², Aslihan Ambeskovic³, Hartmut Land³, Matthew N McCall⁴

Affiliations

¹ Department of Biomedical Genetics, University of Rochester Medical Center, 601 Elmwood Avenue, Rochester, NY 14642, USA; Department of Pathology and Laboratory Medicine, University of Rochester Medical Center, 601 Elmwood Avenue, Rochester, NY 14642, USA.
² Center for Integrated Research Computing, University of Rochester Medical Center, 601 Elmwood Avenue, Rochester, NY 14642, USA.
³ Department of Biomedical Genetics, University of Rochester Medical Center, 601 Elmwood Avenue, Rochester, NY 14642, USA; Wilmot Cancer Institute, University of Rochester Medical Center, 601 Elmwood Avenue, Rochester, NY 14642, USA.
⁴ Department of Biomedical Genetics, University of Rochester Medical Center, 601 Elmwood Avenue, Rochester, NY 14642, USA; Wilmot Cancer Institute, University of Rochester Medical Center, 601 Elmwood Avenue, Rochester, NY 14642, USA; Department of Biostatistics and Computational Biology, University of Rochester Medical Center, 601 Elmwood Avenue, Rochester, NY 14642, USA. Electronic address: [email protected].

Abstract

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).

Keywords: Bioinformatics; Cancer; Cell Biology; Cell culture; Gene Expression; Genetics; Molecular Biology; Systems biology.

Protocol to use TopNet for gene regulatory network modeling using gene expression data from perturbation experiments

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