Classes from 2 to 8 pm Berlin time




Lecture 1 – Introduction to NGS in microbial ecology




    Key concepts (metabarcoding, metagenomics, single-cell sequencing)


    Sequencing platforms (core concepts, read length, read numbers, error rates)


    In-depth example of sequencing with Illumina platforms (over-and under-loading, sequencing process)


    Genetic markers for metabarcoding (markers, primer selection & evaluation)


    Experimental design (library preparation, replication, multiplexing, coverage, costs)


    Understanding data formats (FASTQ, FASTA, others)


    Core concept of computational pipeline for amplicons


    Introduction of the QIIME2 suite




Lab 1 – Introduction to compute lab



    Introduction to the BASH command line (e.g. basic UNIX commands, batch processing)


    Check functionality of computational environment with demo data


    Checking basic characteristics of datasets (number of reads, read length, read quality)





Classes from 2 to 8 pm Berlin time



Lecture 2 – Quality control of NGS reads



    Pre-PCR noise (under-sampling, DNA extraction bias, sample storage, contamination, metadata collection)


    PCR-dependent noise (single nucleotide mis-incorporations, PCR chimeras, primer dimers, unspecific amplification, preferential amplification, template concentrations)


    Sequencing-dependent noise (filtering/trimming poor base calls, dealing with substitution, insertion/deletion errors, index cross-talk, amplicon carry-over)




Lecture 3 – Binning into operational taxonomic units (OTUs) vs Exact Sequence Variants (exact sequence variants)


    Core concept of OTUs and ESV


    OTU binning strategies (de-novo vs. reference-based, impact of alignment strategies, hierarchical clustering algorithms, seed-based clustering algorithms, model-based clustering algorithms)


    OTUs versus ESVs




Lab 2 – Sequence quality control and clustering into operational taxonomic units


    Denoising, OTU binning, and ESV calling (e.g. paired-end merging, sequence filtering, dereplication, OTU clustering, chimera removal, target verification)







Classes from 2 to 8 pm Berlin time



Lecture 4 – Taxonomic Classification



    Core concepts of taxonomic classification


    Reference databases (INSDCs, SILVA, RDP, GREENGENES, UNITE)


    Classification algorithms (similarity-based, composition-based, phylogeny-based)


    Popular assignment approaches (Naïve Bayesian Classifier, BLAST)




Lab 3 – Taxonomic classification



    Finishing Lab 2 if required


    Taxonomic classification using Naïve Bayesian Classifiers and VSEARCH taxonomy implemented in QIIME2


    Dealing with the preparation of custom databases for any genetic marker from NCBI







Classes from 2 to 8 pm Berlin time




Lecture 5 – Multivariate analysis of ecological communitie


    Traits of Alpha and Beta Diversity (richness, evenness, dispersion)


    Ordination Techniques (Constrained vs Unconstrained)


    Multivariate Tests for differences in microbial community composition






Lab  4 - Multivariate Statistics



     Data import & preparation (normalisations, transformations, metadata)



    Alpha Diversity (indices of diversity, rarefaction curves)



    Heatmaps to visualise microbial community differences



    Unconstrained and Constrained Ordination (PCoA, NMDS, CCA, DCA)


    Multivariate tests for differences in community composition (PERMANOVA, PERMDISP)


   •    Taxon-level responses (ANCOM, DESeq2)


   •    Core concept of alpha and beta diversity (indices, distance and dissimilarity metrics)


   •    Unconstrained and constrained ordination techniques


   •   Multivariate tests to infer structural differences


       Statistical tests to assess taxon-level responses





Classes from 2 to 8 pm Berlin time


Lecture 6 –GLMs and Mixed Models for Microbiome Data



     Using Traits of Microbiome structure in GLMs and Mixed Models


    Model selection for GLMs and (G)LMMs


    Combining Microbiome data and life history data



Lab 5 – Mixed Models


    Fitting GLMs and (G)LMMs in R


   Model Selection and presentation of results


    Plotting effects



Lecture 7 –Quantifying Taxon-level Changes in Abundance



     Absolute vs Relative Abundance


     Indicator Species and Community composition


     Differential Abundance testing



Lab 6 – taxon-level statistics




   Indicator Analysis