Integration of single-cell multi-omics for gene regulatory network inference

Area of Science:

• Genomics and Bioinformatics
• Systems Biology
• Molecular Biology

Background:

• Single-cell sequencing technologies have advanced significantly, enabling high-resolution analysis of cellular heterogeneity.
• Reconstructing gene regulatory networks (GRNs) is crucial for understanding cellular mechanisms in biological processes and diseases.
• Existing methods for GRN reconstruction from single-cell data are diverse and require comprehensive evaluation.

Purpose of the Study:

• To review current single-cell sequencing technologies for genome, transcriptome, and epigenome analysis.
• To provide an overview of existing strategies for reconstructing gene regulatory networks (GRNs) using single-cell data.
• To categorize and compare bioinformatics tools for GRN reconstruction based on data type and mathematical principles.

Main Methods:

• Review of single-cell sequencing technologies (genome, transcriptome, epigenome).
• Categorization of GRN reconstruction bioinformatics tools by input data and mathematical underpinnings.
• Comparative analysis of the adaptabilities and limitations of different GRN reconstruction methods.

Main Results:

• Comprehensive overview of technologies for generating single-cell sequencing data.
• Systematic classification of bioinformatics tools for GRN reconstruction.
• Discussion of the mathematical foundations, strengths, and weaknesses of various GRN inference approaches.

Conclusions:

• Single-cell sequencing data offers powerful insights into gene regulatory networks.
• A variety of computational tools exist for GRN reconstruction, each with specific applicability.
• This review aims to guide researchers in selecting appropriate tools for their specific needs in GRN analysis.