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

Ryan Spurney; Michael Schwartz; Mariah Gobble; Rosangela Sozzani; Lisa Van den Broeck

doi:10.1007/978-1-0716-1534-8_4

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

Methods Mol Biol. 2021:2328:47-65. doi: 10.1007/978-1-0716-1534-8_4.

Authors

Ryan Spurney^#¹, Michael Schwartz^#², Mariah Gobble², Rosangela Sozzani³, Lisa Van den Broeck⁴

Affiliations

¹ Electrical and Computer Engineering Department, North Carolina State University, Raleigh, NC, USA.
² Plant and Microbial Biology Department, North Carolina State University, Raleigh, NC, USA.
³ Plant and Microbial Biology Department, North Carolina State University, Raleigh, NC, USA. ross_sozzani@ncsu.edu.
⁴ Plant and Microbial Biology Department, North Carolina State University, Raleigh, NC, USA. lfvanden@ncsu.edu.

^# Contributed equally.

PMID: 34251619
DOI: 10.1007/978-1-0716-1534-8_4

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.

Keywords: Bioinformatics; Gene regulatory network inference; Network visualization; RNA sequencing.

Publication types

Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Algorithms
Amino Acid Motifs / genetics
Cluster Analysis
Computational Biology / methods*
Gene Expression Profiling / methods*
Gene Expression Regulation / genetics*
Gene Regulatory Networks / genetics*
Multigene Family
RNA-Seq / methods
Software
Spatio-Temporal Analysis
Workflow