Marker Data Profiling

Analyze marker gene counts data

Shotgun Data Profiling

Analyze shotgun metagenomics data

Taxon Set Analysis

Discover enriched microbial signatures

Microbiome Metabolomics

Co-analyze microbiome & metabolomics data

Statistical Meta-analysis

Integrate multiple marker gene data

Raw Data Processing

Convert raw 16S reads to ASV table

News & Updates

  • Releasing MicrobiomeAnalyst 2.0 with three new modules coupled with many enhanced features in the existing modules!
  • Added support for both 2012 version and 2013 version of Greengene database for PiCRUST (01/26/2023); ;
  • Updated KEGG global network for improved visualization and coverage (01/23/2023); ;
  • Added support for Sparse Estimation of Correlations among Microbiomes (SECOM) method in taxa correlation analysis (01/12/2023); ;
  • Minor bug fixes based on user feedback (12/27/2022); ;
  • Enhanced error messages when users upload normalized data instead of raw count table (12/22/2022); ;
  • Expanded our taxon set libraries to include >5000 taxon sets covering seven themes (12/18/2022); ;
  • Users can perform multi-factor association analysis based on MaAsLin2 (12/15/2022); ;
  • Added Metadata Editor (in Data Integrity Check page) to better support continuous metadata and covariate adjustment (12/05/2022); ;
  • Added support for interactive stacked bar/area plot - click-and-drag to zoom and mouse-over for details (11/25/2022);
  • Added support for Tax4Fun 2.0 for predicting functional profiles directly from 16S rRNA sequence data (11/15/2022);
Read more ......

Publications

  • Chong, J., Liu, P., Zhou, G., and Xia. J. (2020) "Using MicrobiomeAnalyst for comprehensive statistical, functional, and meta-analysis of microbiome data" Nature Protocols 15, 799–821 (DOI: 10.1038/s41596-019-0264-1)
  • Dhariwal, A., Chong, J., Habib, S., King, I., Agellon, LB., and Xia. J. (2017) "MicrobiomeAnalyst - a web-based tool for comprehensive statistical, visual and meta-analysis of microbiome data" Nucleic Acids Research 45, W180-188 (DOI: 10.1093/nar/gkx295)

Acknowledgements

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