MetagenoNets: comprehensive inference and meta-insights for microbial correlation networks

Area of Science:

• Microbiology
• Bioinformatics
• Computational Biology

Background:

• Microbial association networks are crucial for microbiome research, but their analysis is complex.
• Existing workflows involve lengthy data management, method selection, and separate visualization pipelines.
• Multi-group metadata and inter-omic data add further complexity, requiring integrated and bipartite network approaches.

Purpose of the Study:

• To present MetagenoNets, a web-based application designed to simplify microbial network analysis.
• To provide a unified platform for inferring, visualizing, and comparing various types of microbial networks.
• To handle diverse microbiome data, including abundance, functional profiles, and metadata.

Main Methods:

• Developed MetagenoNets, a modular, web-based application.
• Incorporated intelligent segregation of continuous and categorical metadata.
• Enabled inference and visualization of categorical, integrated (inter-omic), and bipartite networks.
• Offered dynamic choices for data filtration, normalization, transformation, and correlation algorithms.

Main Results:

• MetagenoNets accepts multi-environment microbial abundance and functional profiles.
• The application supports categorical, integrated, and bipartite network construction and visualization.
• It provides an intuitive, interactive dashboard for a streamlined analysis workflow.
• Users can dynamically select analysis parameters for tailored network studies.

Conclusions:

• MetagenoNets offers a one-stop solution for comprehensive microbial network analysis.
• The application simplifies the process of gaining biological insights from complex microbiome data.
• It facilitates the exploration and comparison of microbial associations in diverse biological contexts.