EzMAP v2.0: an integrated platform for microbiome analysis with advanced functional and network modules
- 1Department of Biotechnology, College of Life and Applied Sciences, Yeungnam University
Abstract
Background Amplicon-based microbiome studies generate complex datasets that require streamlined processing and analysis. While EzMAP provided a user-friendly platform integrating QIIME2 with downstream statistical and functional analyses, its adoption was limited by installation issues, restricted user flexibility, and lack of advanced analytical modules. Results We present EzMAP v2.0, a major update to the Easy Microbiome Analysis Platform (EzMAP), featuring improved accessibility, enhanced usability, and expanded analytical capabilities. Installation has been simplified across operating systems, resolving dependency conflicts common in the previous version. EzMAP v2.0 introduces a dual-mode framework: Easy Mode with automated parameter selection (including a smart-trim algorithm for DADA2 truncation based on per-base quality profiles) suitable for novice users, and Expert Mode with full parameter control for advanced users. Functional prediction modules (Tax4Fun and FunGuild) have been optimized for stability and accuracy. EzMAP v2.0 incorporates machine learning through a Random Forest module for predictive modeling and feature selection at user-specified taxonomic levels, and a novel ANCOM-BC + Random Forest consensus module that combines compositional differential abundance with predictive feature importance to identify high-confidence biomarkers. Microbial co-occurrence network analysis using SparCC enables identification of correlated taxonomic associations, with hub analysis to identify potential keystone taxa. We demonstrate EzMAP v2.0 capabilities through application to a publicly available rhizosphere microbiome dataset, comparing Easy Mode and Expert Mode outputs across diversity, differential abundance, biomarker identification, consensus analysis, and co-occurrence network modules. Both modes detected significant treatment effects on community structure (PERMANOVA P < 0.001) and identified hundreds of differentially abundant ASVs per stress comparison, with the consensus module identifying high-confidence biomarkers supported by both ANCOM-BC and Random Forest analyses. Differences from previously published findings on this dataset are documented and attributed to known sources of pipeline-dependent variation in microbiome analyses. Conclusion EzMAP v2.0 consolidates upstream processing, downstream statistics, functional prediction, machine learning, and ecological network analysis within a single, user-friendly framework. By lowering technical barriers and broadening analytical scope, EzMAP v2.0 provides an integrated and methodologically transparent solution for microbiome research.
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BibTeX
@article{shanmugam2026ezmap,
title = {{EzMAP v2.0: an integrated platform for microbiome analysis with advanced functional and network modules}},
author = {Gnanendra Shanmugam and Junhyun Jeon},
journal = {BMC Bioinformatics},
year = {2026},
month = jul,
doi = {10.1186/s12859-026-06565-2},
url = {https://doi.org/10.1186/s12859-026-06565-2},
note = {CC BY},
}
RIS
TY - JOUR
TI - EzMAP v2.0: an integrated platform for microbiome analysis with advanced functional and network modules
AU - Gnanendra Shanmugam
AU - Junhyun Jeon
PY - 2026
DA - 2026/07/01
JO - BMC Bioinformatics
DO - 10.1186/s12859-026-06565-2
UR - https://doi.org/10.1186/s12859-026-06565-2
AB - Background Amplicon-based microbiome studies generate complex datasets that require streamlined processing and analysis. While EzMAP provided a user-friendly platform integrating QIIME2 with downstream statistical and functional analyses, its adoption was limited by installation issues, restricted user flexibility, and lack of advanced analytical modules. Results We present EzMAP v2.0, a major update to the Easy Microbiome Analysis Platform (EzMAP), featuring improved accessibility, enhanced usability, and expanded analytical capabilities. Installation has been simplified across operating systems, resolving dependency conflicts common in the previous version. EzMAP v2.0 introduces a dual-mode framework: Easy Mode with automated parameter selection (including a smart-trim algorithm for DADA2 truncation based on per-base quality profiles) suitable for novice users, and Expert Mode with full parameter control for advanced users. Functional prediction modules (Tax4Fun and FunGuild) have been optimized for stability and accuracy. EzMAP v2.0 incorporates machine learning through a Random Forest module for predictive modeling and feature selection at user-specified taxonomic levels, and a novel ANCOM-BC + Random Forest consensus module that combines compositional differential abundance with predictive feature importance to identify high-confidence biomarkers. Microbial co-occurrence network analysis using SparCC enables identification of correlated taxonomic associations, with hub analysis to identify potential keystone taxa. We demonstrate EzMAP v2.0 capabilities through application to a publicly available rhizosphere microbiome dataset, comparing Easy Mode and Expert Mode outputs across diversity, differential abundance, biomarker identification, consensus analysis, and co-occurrence network modules. Both modes detected significant treatment effects on community structure (PERMANOVA P < 0.001) and identified hundreds of differentially abundant ASVs per stress comparison, with the consensus module identifying high-confidence biomarkers supported by both ANCOM-BC and Random Forest analyses. Differences from previously published findings on this dataset are documented and attributed to known sources of pipeline-dependent variation in microbiome analyses. Conclusion EzMAP v2.0 consolidates upstream processing, downstream statistics, functional prediction, machine learning, and ecological network analysis within a single, user-friendly framework. By lowering technical barriers and broadening analytical scope, EzMAP v2.0 provides an integrated and methodologically transparent solution for microbiome research.
SN - 1471-2105
ER -