Microbiome Data Analysis: 16S, Metagenomics, AI, and Ecological Modeling

Catalog description

This course provides a hands-on, quantitative introduction to analyzing microbiome data, with an emphasis on modern experimental, computational, and AI-driven approaches used in current research. Students will work with real datasets to learn how to process and interpret 16S rRNA gene sequencing and metagenomic data, including quality control, taxonomic profiling, and functional inference. The course introduces core ecological concepts such as alpha and beta diversity, community structure, and compositional data analysis, alongside practical methods for visualization and statistical testing. Students will develop end-to-end roadmaps for microbiome analysis, from experimental design through interpretation, with attention to common pitfalls and sources of bias. A central focus is connecting data to mechanism and prediction. Students will be introduced to consumer-resource models and related quantitative frameworks to understand how microbial communities assemble and respond to perturbations. In parallel, the course explores how machine learning and AI methods can be used to model high-dimensional microbiome data, identify predictive signatures, and generate testable hypotheses. Emphasis is placed on integrating AI with mechanistic models rather than treating them as black boxes. By the end of the course, students will be able to design, execute, and critically evaluate microbiome data analyses, and to bridge sequencing data, ecological theory, and AI-based approaches to enable predictive microbiome science.