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Spatiotemporal Gene Expression Profiling and Network Inference: A Roadmap for Analysis, Visualization, and Key Gene Identification

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Modeling Transcriptional Regulation

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2328))

Abstract

Gene expression data analysis and the prediction of causal relationships within gene regulatory networks (GRNs) have guided the identification of key regulatory factors and unraveled the dynamic properties of biological systems. However, drawing accurate and unbiased conclusions requires a comprehensive understanding of relevant tools, computational methods, and their workflows. The topics covered in this chapter encompass the entire workflow for GRN inference including: (1) experimental design; (2) RNA sequencing data processing; (3) differentially expressed gene (DEG) selection; (4) clustering prior to inference; (5) network inference techniques; and (6) network visualization and analysis. Moreover, this chapter aims to present a workflow feasible and accessible for plant biologists without a bioinformatics or computer science background. To address this need, TuxNet, a user-friendly graphical user interface that integrates RNA sequencing data analysis with GRN inference, is chosen for the purpose of providing a detailed tutorial.

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Acknowledgement

Support for this work was provided to R.S. by the National Science Foundation (NSF) (CAREER MCB-1453130).

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Author notes

  1. Ryan Spurney and Michael Schwartz contributed equally to this work.

Authors and Affiliations

  1. Electrical and Computer Engineering Department, North Carolina State University, Raleigh, NC, USA

    Ryan Spurney

  2. Plant and Microbial Biology Department, North Carolina State University, Raleigh, NC, USA

    Michael Schwartz, Mariah Gobble, Rosangela Sozzani & Lisa Van den Broeck

Authors
  1. Ryan Spurney
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  2. Michael Schwartz
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  3. Mariah Gobble
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  4. Rosangela Sozzani
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  5. Lisa Van den Broeck
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Corresponding authors

Correspondence to Rosangela Sozzani or Lisa Van den Broeck .

Editor information

Editors and Affiliations

  1. Department of Biology, University of Alabama, Birmingham, AL, USA

    SHAHID MUKHTAR

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Spurney, R., Schwartz, M., Gobble, M., Sozzani, R., Van den Broeck, L. (2021). Spatiotemporal Gene Expression Profiling and Network Inference: A Roadmap for Analysis, Visualization, and Key Gene Identification. In: MUKHTAR, S. (eds) Modeling Transcriptional Regulation. Methods in Molecular Biology, vol 2328. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1534-8_4

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  • DOI: https://doi.org/10.1007/978-1-0716-1534-8_4

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1533-1

  • Online ISBN: 978-1-0716-1534-8

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