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Learn common analysis techniques to make sense of microbial sequencing data.
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Microorganisms play a major role in the biosphere and within our bodies, but only a tiny fraction has been cultured so far. Microbiome data, that is the genetic information of microorganisms, is therefore an important window into the hidden microbial world.
Microbiome data analysis elucidates the composition of microbial communities and how it changes in response to the environment. When analyzing sequencing data, we learn whether microbial diversity differs across conditions and identify links between microbes. In brief, microbiome data analysis gives us a first idea of how a microbial ecosystem works.
This course will illustrate with the help of real-world example data how to carry out typical analysis tasks, such as comparing microbial composition and diversity, clustering samples and computing associations. If you plan to work with microbiome data, this course will get you up to speed.
The instructors are experienced bioinformaticians who are internationally known for their analysis of large-scale microbiome data sets.
Module 0: Introduction
Study guide, R basics and a help forum for programming questions
Module 1: Introduction to microbiome data
Sequencing techniques, data types (16S, WGS, metadata), example applications
Module 2: From sequences to counts
Quality control of reads, taxonomic and functional assignment
Module 3: Comparing microbiomes
Relative versus absolute abundance, taxonomic and functional richness, evenness and diversity
Module 4: Ordination
Dimension reduction: arranging samples according to their taxonomic and functional composition in two-dimensional space
Module 5: Taxon/function associations
Network construction: computing and interpreting associations between taxa and functions
Module 6: Your favourite microbiome
Databases and journals for microbiome data and guidelines for doing your own analysis
Module 7: Final exam
Complete the course by passing the quiz or by completing a microbiome analysis
