Starting from marker gene abundance data (OTU/ASV table, BIOM file, mothur output)
Visually exploring your 16S rRNA data with a public data in a 3D PCoA plot
Starting from gene list or gene abundance data annotated by KO, EC or COG
Starting with a list of taxa of interest (strains, species or higher level taxa)

MDP

Marker Data Profiling (MDP): Comprehensive composition & diversity analysis supporting various methods of data overview, alpha diversity and beta-diversity; comparative analysis supporting multiple differential abundance methods (metagenomeSeq, LEfSe, edgeR, DESeq2, etc.); as well as prediction of metabolic potentials.

SDP

Shotgun Data Profiling (SDP): Functional diversity profiling based on KEGG annotations (modules, pathways, metabolisms), EC numbers, or COG categories, direct functional association testing, as well as differential abundance analysis followed by functional enrichment analysis within a powerful metabolic network visualization system.

TSEA

Taxon Set Enrichment Analysis (TSEA): Enrichment analysis using ~2400 manually collected taxon sets organized into 11 different categories. They are defined based on their shared phenotypic traits or ecological niches, or their associations with host genetic variations, lifestyles, biochemistry, diseases, developmental stages, etc.

PPD

Projection with Public Data (PPD): Co-processing your data together with a suitable public 16S rRNA data of interest and explore the results within an interactive 3D PCoA visualization system to easily discover patterns of interest as well as to associate these patterns with underlying taxonomic variations.

News & Updates
  • Stacked area plot now supports grouping (faceting) based on two factors (10/24/2019);
  • Updated R from 3.5.1 to the latest version (3.6.1) (10/16/2019);
  • Fixed the issue with core microbiome analysis (09/18/2019);
  • Added support for group-wise core microbiome analysis (08/30/2019);
  • Added support for group-wise and sample-wise heat tree analysis (08/28/2019);
  • Minor bug fixes based on user feedback (08/26/2019);
  • Added support for SparCC correlation network analysis (08/15/2019);
  • Code refactoring and performance enhancements (08/01/2019);
  • ASV sequences are now mapped to self-defined ASV IDs for easy display and plotting (07/20/2019);
  • Added support for the viridis color palettes (07/10/2019);
  • Added support to label only significant nodes in heat tree graph (07/09/2019);
  • Code refactoring for improved performance(06/21/2019);
  • Updated the tutorial for marker gene profiling (MDP) module (06/15/2019);
  • Added support for heat tree visualization based on the Metacoder package (06/13/2019);
  • Added the R command history panel together with release of the companion R package MicrobiomeAnalystR (05/29/2019);
  • Upgraded the web framework to Primefaces 7.0 (05/19/2019);
Read more ......

Please Cite

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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