Computational Inference of Gene Regulatory Network Using Genome-wide ChIP-X Data

Area of Science:

• Computational Biology
• Bioinformatics
• Systems Biology

Background:

• Gene regulatory networks (GRNs) govern gene expression crucial for cellular responses to environmental stimuli.
• Inferring GRNs from high-throughput data requires robust algorithmic and statistical methods.
• Databases like JASPAR and SwissRegulon provide TF-target interaction data, essential for GRN construction.

Purpose of the Study:

• To describe a step-wise procedure for extracting TF-target interaction data from the JASPAR database.
• To demonstrate the inference of GRNs using extracted data and the igraph library.
• To highlight key parameters for analyzing GRN properties.

Main Methods:

• Data extraction from the JASPAR database for specific TF-gene pairs.
• Network inference using the igraph R library.
• Analysis of network properties using relevant parameters.

Main Results:

• A reproducible methodology for GRN inference was established.
• The procedure facilitates the extraction of TF-target interactions for network construction.
• Key parameters for network analysis were identified.

Conclusions:

• The described method provides a framework for discerning GRNs based on TF-gene pair interactions.
• This approach aids in understanding gene regulation mechanisms.
• The study contributes to the computational analysis of gene regulatory architectures.