Course objectives
By the end of the course participants should be able to:
- Describe the importance of the microbiome and why it should be studied
- Understand how to design 16S rRNA microbiome studies
- Be able to apply basic syntax and operations in R
- Understand the different NGS data types (e.g. MiSeq reads) produced for a 16S rRNA microbiome study
- Evaluate the quality of NGS sequence reads and samples
- Understand the various bioinformatics tools used for 16S microbiome studies
- Understand the various 16S rRNA bioinformatics pipelines being used to study the microbiome.
- Apply the H3ABioNet’s 16S rRNA pipeline and understand how to execute this
- Understand the use of workflow languages (Nextflow) and containerized images (Singularity) to automate analyses
- Analyze 16S rRNA microbiome data and interpret results
Course modules
Module 1: Introduction to the Linux command line / intro to R
Learning outcomes and content covered:
- Define the following terms as they relate to R: object, assign, function, arguments, options.
- Assign values to objects in R
- Use comments to inform script
- Call functions and use arguments to change their default options
- Create vectors, subset and extract values from vectors
➔ Total number of hours: ~ 7 hours
➔ Tools/software: latest versions of R and RStudio
Module 2: Introduction to the microbiome and study design – why 16S?
Learning outcomes and content covered:
- Understand what is meant by the term “microbiome”.
- Understand how it originates (current hypotheses), how it may change due to lifetime exposures (factors influencing microbiota profiles) and that it is unique to every individual.
- Know that different body sites have unique microbiomes.
- Appreciate the importance of studying the microbiome (what are the clinical impacts).
➔ Total number of hours: ~ 4 hours
Module 3: Sample collection, extraction and library prep for 16S NGS analyses
Learning outcomes and content covered:
- Understand what is meant by the term “16S rRNA gene” and why we are interested in this gene.
- Understand the concepts behind sequencing and high-throughput sequencing
- Know why it is important to clearly plan an experiment and how different components to the study and experimental design may influence data generated and downstream analyses.
- Know that techniques other than 16S rRNA sequencing are also available to study microbial profiles.
➔ Total number of hours: ~ 4 hours
Module 4: 16S rRNA sequencing bioinformatics pipeline: the theory
Learning outcomes and content covered:
- Understand various free 16S rRNA bioinformatics processing pipelines
- Understand the concept of paired-end and single-end sequencing
- To know why merging of paired end 16S rRNA sequence reads is necessary
➔ Total number of hours: ~ 4 hours
Module 5: 16S analysis pipeline - QC, ASV picking, taxonomic classification and alignment
Learning outcomes and content covered:
- Understand each steps of 16S analysis pipeline
- Run 16S analysis pipeline from raw reads
- Know how to edit files and use command line
- Choose the best approach and tools to analyze your data
➔ Total number of hours: ~ 5 hours➔ Tools/software: FASTQC, QIIME, usearch, UPARSE, DADA2
Module 6: Downstream analysis in R - using the packages phyloseq, NMF, vegan, metagenomeSeq (among others)
Learning outcomes and content covered:
- Understand the basic terminology related to 16S (OTU table, alpha diversity, beta diversity)
- Import OTU table into R for downstream analysis
- Load R packages relevant to downstream analysis, set working directories
- Perform downstream analysis (alpha diversity, visual data exploration (bar plots, heat maps, beta diversity), differential abundance testing)
➔ Total number of hours: ~ 8 hours➔ Tools/software: latest versions of R and RStudio, R packages: phyloseq, metagenomeSeq, NMF, vegan, ggplot2, randomForest, matrixStats, fifer, dplyr, ROCR, gridExtra
