This workshop will give an overview of the theory and practice of using amplicon sequencing approaches to study the diversity of microbial communities. In the first part of the workshop, we will discuss current methods for microbiome quantification using amplicon sequencing approaches. In the second part of the workshop, we will discuss normalization and diversity analysis approaches that can be used to quantify the diversity of microbial communities.

This workshop was developed with support from the NSERC CREATE Computational Biodiversity Science and Services (BIOS²) training program and the Canada Research Chair in Plant Microbiomes.

Lecture - Microbiome quantification using amplicon sequencing approaches (PDF)

R practical - Microbiome sequence analysis workshop

Downloads

Sequence data and SILVA database files (~350MB download from figshare)

R workspace Microbiome-sequence-analysis-workspace.RData

R Markdown file used to generate the R practical document

Lecture - Data normalization and ecological analysis of microbiome data (PDF)

R practical - Microbiome ecological analysis workshop

Downloads

Sample metadata metadata-Qleaf_BACT.csv

DADA2 ASV sequence table seqtab.nochim.rds

DADA2 ASV taxonomic annotations taxa.sp.rds

R workspace Microbiome-ecological-analysis-workspace.RData

R Markdown file used to generate the R practical document

The workshop assumes basic familiarity with R/RStudio; practical exercises are based on the use of R scripts to analyse sequence data and resulting community data sets.

To be able to follow along with the practical exercises on your own computer, in addition to downloading the data files above, you will need to do the following:

Install the latest version of R for your operating system (version 4.2.0 as of May 2022): https://cran.r-project.org/

Install the latest version of RStudio for your operating system (version 2022.02.2+485 as of May 2022): https://www.rstudio.com/products/rstudio/download/

Install the packages that we will use during the workshop by executing the following code in R version 4.2.0:

```
# install packages from CRAN
install.packages(pkgs = c("Rcpp", "RcppArmadillo", "picante", "ggpubr", "pheatmap"), dependencies = TRUE)
# install packages from Bioconductor
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("dada2", version = "3.15", update = FALSE)
# if the dada2 install returns a warning for BiocParallel, install from binary using this command:
# BiocManager::install("BiocParallel", version = "3.15", type="binary", update = FALSE)
BiocManager::install("DESeq2")
BiocManager::install("phyloseq")
BiocManager::install("ANCOMBC")
```