mirConnX: condition-specific mRNA-microRNA network integrator

Area of Science:

• Bioinformatics
• Systems Biology
• Computational Biology

Background:

• Gene regulatory networks (GRNs) are crucial for understanding cellular processes.
• MicroRNA (miRNA) and transcription factor (TF) interactions play key roles in gene regulation.
• Existing tools often lack integration of static and dynamic data for disease-specific network construction.

Purpose of the Study:

• To develop a user-friendly web interface, mirConnX, for inferring, displaying, and parsing mRNA and miRNA gene regulatory networks.
• To integrate sequence information and gene expression data for creating disease-specific, genome-wide regulatory networks.
• To provide a tool for clinical scientists to generate hypotheses and explore gene interactions.

Main Methods:

• Construction of a prior static network including predicted TF-gene and miRNA-target associations, supplemented with literature data.
• Inference of dynamic TF- and miRNA-gene associations from user-provided expression data.
• Combination of static and dynamic networks using an integration function with user-specified weights.
• Visualization and analysis via a responsive graphical user interface.

Main Results:

• mirConnX integrates prior knowledge with user-specific expression data to build comprehensive regulatory networks.
• The tool supports two model organisms: Homo sapiens and Mus musculus.
• A responsive graphical user interface facilitates network visualization and analysis.

Conclusions:

• mirConnX provides an intuitive platform for constructing and analyzing complex gene regulatory networks.
• The integration of static and dynamic data enables disease-specific network inference.
• The tool serves as a valuable resource for clinical scientists in hypothesis generation and biological exploration.