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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

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Published on: October 19, 2021

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

Ryan Spurney¹, Michael Schwartz², Mariah Gobble²

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

Methods in Molecular Biology (Clifton, N.J.)

|July 12, 2021

Summary

This study presents a user-friendly workflow for gene regulatory network (GRN) inference using RNA sequencing data. It guides plant biologists through analysis, from experimental design to network visualization, making complex bioinformatics accessible.

Keywords:

Bioinformatics Gene regulatory network inference Network visualization RNA sequencing

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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Area of Science:

Computational Biology
Genomics
Systems Biology

Background:

Gene expression data analysis is crucial for understanding biological systems and identifying regulatory factors.
Accurate conclusions from gene regulatory network (GRN) inference depend on understanding computational tools and workflows.
Plant biologists often lack the specialized bioinformatics background needed for complex GRN analysis.

Purpose of the Study:

To provide a comprehensive and accessible workflow for GRN inference tailored for plant biologists.
To detail the steps involved in GRN analysis, from experimental design to network visualization.
To introduce TuxNet, a user-friendly graphical interface for integrated RNA sequencing data analysis and GRN inference.

Main Methods:

The workflow covers experimental design, RNA sequencing data processing, and differentially expressed gene (DEG) selection.
Includes methods for clustering gene expression data prior to network inference.
Employs network inference techniques and network visualization for analysis.

Main Results:

Presents a complete GRN inference workflow.
Demonstrates the utility of TuxNet as an integrated platform for RNA sequencing data analysis and GRN inference.
Offers a practical tutorial for plant biologists.

Conclusions:

The proposed workflow and TuxNet tool significantly enhance accessibility to GRN inference for plant biologists.
Facilitates deeper understanding of gene regulatory dynamics in plants.
Empowers researchers without a bioinformatics background to perform complex network analyses.