MetagenoNets

MetagenoNets infers microbial association networks from multi-environment microbial abundance data and inter-omic functional profiles to characterize community dynamics.

Key Features:

• Data Management and Integration: Handles continuous and categorical metadata and inter-omic functional profiles to construct categorical, integrated (inter-omic), and bi-partite networks.
• Modular Analytical Framework: Implements a modular analysis flow encompassing inference, integration, exploration, and comparison stages.
• Dynamic Algorithm Selection: Supports selection of filtration, normalization, data transformation, and correlation algorithms for tailored network inference.
• Visualization and Reporting: Generates visualizations to report and compare microbial associations and network structures.

Scientific Applications:

• Microbiome community dynamics: Enables analysis of microbial interactions and community structure across multi-environment datasets and multi-group metadata.
• Ecological studies: Supports investigation of species associations and network-level ecology using abundance and functional profile data.
• Health and disease research: Facilitates exploration of microbial association patterns relevant to host-associated microbiomes.
• Environmental monitoring: Allows comparison of microbial networks across environmental samples and conditions.

Methodology:

Performs segregation of metadata types, integrates multi-environment microbial abundance data with inter-omic functional profiles, applies filtration, normalization, data transformation, and correlation/network inference algorithms, constructs categorical, integrated (inter-omic), and bi-partite networks, and generates visualizations for comparison.