Host-microbiome associations are shaped by host health, disease state and geography. We aim to integrate a large number of published metagenomic datasets from various cohorts worldwide, across diseases and geographies. We will process these large scale data with standardized methods, correcting for batch effects. Standard statistical approaches and more sophisticated machine learning techniques will be used to quantify associations between microbiome features and host traits.
The specific objectives of this project are:
1) Generating standardized, comparable taxonomic and functional profiles of human gut microbiomes from published studies
2) Performing association analyses of microbiome profiles and diversity with anthropometric, demographic, clinical and environmental variables
3) Sharing with the research community summary microbiome variables and feature profiles through an online repository. The original raw data and participant metadata will not be shared with third parties or the research community.
For this purpose, we are requesting access to all available gut microbiome data of the DMP (metagenomic sequencing data and metadata) to expand our dataset.
The data that we require for this purpose are:
1. Human gut metagenomic sequencing data
2. Participant demographic and clinical data (age, sex, BMI (or weight/height), country, Bristol stool scale, health status, disease)
The project will leverage the Institute of Clinical Molecular Biology's expertise in human microbiome research, particularly genomics and metagenomics. Standardized metagenomic data processing pipelines, such as TOFU-MAaPO, have been developed internally (https://github.com/ikmb/TOFU-MAaPO).
The project is funded by the DFG Research Unit miTarget 5042 and the VESICULOME ERC Consolidator grant (ID: 101126254).