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 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

 

 

 

Friday from 09:30 to 17:30

 

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

 

    DESeq2

 

   Indicator Analysis

 

    ANCOM