Overview
Welcome to MMIP Server.
This server is designed for prediction of the metabolites that could be produced by the microbial community during different health condition by using their metagenomics data (16S-rRNA). Furthermore, it also helps you to compare your real-time metabolomic (untargeted) data with predicted metabolite and helps to see what could be the most probable source of the metabolites.
- Helps in predicting metabolites from the metagenomic data.
- Helps in finding the most probable source of the metabolite (microbe responsible for the production).
- Predict important feature at OTU, Compound, Enzyme level by machine learning approaches.
- Helps in interlinking important feature.
Publication: Anupam Gautam, Debaleena Bhowmik, Sayantani Basu, Wenhuan Zeng, Abhishake Lahiri, Daniel H Huson, Sandip Paul, Microbiome Metabolome Integration Platform (MMIP): a web-based platform for microbiome and metabolome data integration and feature identification, Briefings in Bioinformatics, Volume 24, Issue 6, November 2023, bbad325, https://doi.org/10.1093/bib/bbad325
You can access preprint here: Anupam Gautam, Debaleena Bhowmik, Sayantani Basu, Wenhuan Zeng, Abhishake Lahiri, Daniel H. Huson, Sandip Paul. Microbiome Metabolome Integration Platform (MMIP): a web-based platform for microbiome and metabolome data integration and feature identification. bioRxiv , 2023; doi: https://doi.org/10.1101/2023.04.04.535534
Services
Please Select any one of the two module to start your analysis.
Analysis result
Feature Prediction
Feature prediction by machine learning approaches at OTU, Compound, Enzyme level.
Alpha Diversiy
α-diversity analysis through phyloseq R-package and visulatization through plotly.
Beta Diversiy
β-diversity analysis through phyloseq R-package and visulatization through plotly.
Taxonomic profiling
Taxonomic profile at different level through phyloseq R-package and visulatization through plotly