Monday from 09:30 to 17:30




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)






Tuesday from 09:30 to 17:30




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)










Wednesday from 09:30 to 17:30




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






Thursday from 09:30 to 17:30




Lecture 5 – Multivariate analysis of ecological communities




    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




Lab 4 – Multivariate statistics




    Finishing Lab 3 if required


    Data import and preparation (normalizations, transformations, metadata)


    Alpha diversity analysis (diversity indices, rarefaction curves)


    Unconstrained and constrained ordinations (PCoA, NMDS, CAP, dbRDA, Procrustes, Mantel)


    Multivariate tests to infer structural differences (ANOSIM, PERMANOVA, PERMDISP, GLMs)


    Taxon level responses (ANCOM, permutational ANOVA)






Friday from 09:30 to 17:30




Lecture 6 –Supervised classification and regression of sample metadata, and other supervised machine learning methods.




    Introduction to machine learning


    Supervised learning classifier


    Microbial maturity index prediction.


    Supervised learning regressor.




Lab 5 – Supervised machine learning methods




    Finishing Lab 4 if required


   Supervised machine learning methods


Final round


    Group-based roundtables, short presentation


    Q&A and discussion