MOTIVATION: Network analysis has become a central strategy for dissecting complex biological and environmental systems, particularly as modern omics technologies generate increasingly large and heterogeneous datasets. However, current tools often lack the scalability, flexibility, and native multi-omics support required for high-dimensional data analysis. We developed MetaNet, a high-performance R package that unifies network construction, visualization, and analysis across diverse omics layers. RESULTS: MetaNet enables fast and scalable correlation-based network construction for datasets with more than 10 000 features, providing over 40 layout algorithms, rich annotation utilities, and visualization options compatible with both static and interactive platforms. It further offers comprehensive topological and stability metrics for in-depth network characterization. Benchmarking shows that MetaNet delivers up to a 100-fold improvement in computation time and a 50-fold reduction in memory usage compared to existing R packages. We demonstrate its utility through two representative applications: (1) longitudinal microbial co-occurrence networks revealing airborne microbiome dynamics, and (2) an integrative exposome-transcriptome network of over 40 000 features, uncovering distinct regulatory impacts of biological and chemical exposures. By offering a robust, reproducible, and biologically informed framework, MetaNet advances multi-omics network analysis across biological, ecological, and environmental domains. AVAILABILITY: MetaNet package is freely available at https://github.com/Asa12138/MetaNet.

Accurate and rapid taxonomic classifications are essential for systematically exploring organisms and metabolites in diverse environments. Many tools have been developed for biological taxonomic trees, but limitations apply, and a streamlined method for constructing chemical taxonomic trees is notably absent. We present the iPhylo suite (https://www.iphylo.net/), a comprehensive, automated, and interactive platform for biological and chemical taxonomic analysis. The iPhylo suite features web-based modules for the interactive construction and annotation of taxonomic trees and a stand-alone command-line interface (CLI) for local operation or deployment on high-performance computing (HPC) clusters. iPhylo supports National Center for Biotechnology Information (NCBI) taxonomy for biologicals and ChemOnt and NPClassifier for chemical classifications. The iPhylo visualization module, fully implemented in R, allows users to save progress locally and customize the underlying R code. Finally, the CLI module facilitates analysis across all hierarchical relational databases. We showcase the iPhylo suite's capabilities for visualizing environmental microbiomes, analyzing gut microbial metabolite synthesis preferences, and discovering novel correlations between microbiome and metabolome in humans and environment. Overall, the iPhylo suite is distinguished by its unified and interactive framework for in-depth taxonomic and integrative analyses of biological and chemical features and beyond.