Inference of Dynamic Growth Regulatory Network in Cancer Using High-Throughput Transcriptomic Data

Area of Science:

• Transcriptomics
• Systems Biology
• Bioinformatics

Background:

• Gene expression variation regulates biological processes across development and disease.
• Dynamic growth regulatory networks (dGRNs) model stage-specific gene expression changes in cancer.
• Correlation-based methods using differentially expressed genes (DEGs) are common for dGRN reconstruction.

Purpose of the Study:

• To provide a comprehensive, step-by-step protocol for inferring dGRNs from RNA-Seq data.
• To guide early researchers in analyzing transcriptomics data for dGRN construction.
• To detail methods for integrating external interaction data into dGRNs.

Main Methods:

• RNA-Seq data acquisition and pre-processing.
• Mapping reads to a reference genome.
• Construction of correlation-based co-expression networks from DEGs.
• Integration of public interaction/regulation data.
• Topological analysis of inferred dGRNs.

Main Results:

• A detailed protocol for DEG analysis and dGRN inference is presented.
• The protocol covers the entire workflow from raw data to network analysis.
• Methodologies for incorporating external biological knowledge into dGRNs are included.

Conclusions:

• This protocol facilitates the inference of dGRNs using transcriptomics data.
• It offers a practical guide for researchers new to dGRN analysis.
• The outlined methods enable deeper understanding of gene regulation in biological systems.