MiNAA-WebApp: A Web-Based Tool for the Visualization and Analysis of Microbiome Networks

TL;DR

MiNAA-WebApp is a user-friendly web tool that visualizes and analyzes microbiome networks, making complex microbial community data accessible to researchers without programming skills.

Contribution

It introduces a web-based interface for the MiNAA algorithm, enhancing usability and visualization capabilities for microbiome network analysis.

Findings

Enables alignment of microbial networks via a web interface.

Facilitates exploration of ecological relationships without programming.

Applicable to various biological networks beyond microbiomes.

Abstract

Microbial networks, representing microbes as nodes and their interactions as edges, are crucial for understanding community dynamics in various environments. Analyzing microbiome networks is crucial for identifying keystone taxa that play central roles in maintaining microbial community structure and function, assessing how environmental changes such as pollution, climate shifts, or land use affect microbial dynamics, tracking disease progression by revealing alterations in microbial interactions over time, and predicting microbial community responses to interventions such as antibiotics, probiotics, or changes in diet and habitat. The complexity of microbial interactions necessitates the use of computational tools such as the MiNAA-WebApp, available at https://minaa.wid.wisc.edu, which enhances the accessibility of the Microbiome Network Alignment Algorithm MiNAA. This tool allows researchers to align microbial networks and explore ecological relationships and community dynamics without extensive computational skills. Originally, MiNAA's command-line interface limited its usability for those without programming backgrounds. The web-based MiNAA-WebApp addresses this shortcoming by offering an intuitive interface with visualization tools, allowing easy exploration and analysis of microbial networks. The web app is designed for microbiome networks but also applicable to other biological networks, broadening its use in computational biology and making network-based research accessible to a wider audience.