Program

 

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)

 

Tools: DADA2, VSEARCH

 

 

 

 

 

 

 

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