Computational and Quantitative Training in Microbiome Research

2- days course

● Lectures • Introduction to data harmonization & cloud computing for metagenomics. • Data processing & visualization for metagenomics incl. QC, metagenomic assembly, metagenomic binning, taxonomic assignment, and microbial diversity exploration. • Quantitative analysis (statistical methods appropriate for microbiome data; ANCOM, metagenomeSeq) and multi-omics analysis (integrative analysis of microbiome and metabolomics data using network methods). • The transformative power of ML tools across biotechnological and pharmaceutical sectors, including interpretable and explainable ML algorithms.

● Peer learning Mentees are divided in 4 groups to practice newly acquired skills by solving a data mining exercise.

Trainers: Kexin Li, Qilian Fan, Xiuqiang Chen, and Zhengyuan Zhou.