Gene regulatory network inference resources: A practical overview.

Area of Science:

• Systems Biology
• Molecular Biology
• Bioinformatics

Background:

• Transcriptional regulation governs essential life processes, including homeostasis, development, metabolism, and disease.
• Gene Regulatory Networks (GRNs) offer an interpretable framework for understanding complex transcriptional interactions.
• Inferring comprehensive GRNs across all species and contexts remains a significant biological challenge.

Purpose of the Study:

• To review and categorize existing computational and experimental methods for inferring Gene Regulatory Networks (GRNs).
• To provide a comprehensive overview of tools and approaches used in systems biology for GRN analysis.
• To highlight the complementary nature of different GRN inference methods.

Main Methods:

• Categorization of GRN inference methods into six main areas: Coexpression, Sequence Motifs, Chromatin Immunoprecipitation (ChIP), Orthology, Literature, and Protein-Protein Interaction (PPI).
• Description of methods ranging from user-friendly web tools to advanced computational algorithms.
• Focus on methods leveraging specific types of biological data to infer regulatory relationships.

Main Results:

• A structured catalog of diverse GRN inference methodologies is presented.
• The review covers a spectrum of tools, catering to varying levels of computational expertise.
• Each method elucidates distinct aspects of transcriptional relationships, often complementing other approaches.

Conclusions:

• Integrating multiple GRN inference methods offers a more complete understanding of transcriptional relationships.
• This review serves as a valuable resource for both experimental and computational biologists.
• Addressing the challenge of holistic GRN inference requires combining various data-driven and computational strategies.