Description Target Audience Prerequisites Outline

Course Description

Metagenomics, the sequencing of DNA directly from a sample without first culturing and isolating the organisms, has become the principal tool of “meta-omic” analysis. It can be used to explore the diversity, function, and ecology of microbial communities. The CBW has developed a 3-day course providing an introduction to metagenomic data analysis followed by hands-on practical tutorials demonstrating the use of metagenome analysis tools. The tutorials are designed as self-contained units that include example data and detailed instructions for installation of all required bioinformatics tools.

Course Objectives

Participants will gain practical experience and skills to be able to:

Design appropriate microbiome-focused experiments
Understand the advantages and limitations of metagenomic data analysis
Devise an appropriate bioinformatics workflow for processing and analyzing metagenomic sequence data (marker-gene, shotgun metagenomic, and metatranscriptomic data)
Apply appropriate statistics to undertake rigorous data analysis
Visualize datasets to gain intuitive insights into the composition and/or activity of their data set

Target Audience

Graduates, postgraduates, staff bioinformaticians and PIs working with or about to embark on analysis of marker genes, metagenomic, and metatranscriptomic data from microbiome-focused experiments.

Course Outline

Module 1: Introduction to Metagenomics

A brief history of microbiome studies
Review of relevant terms (microbial communities, microbiome, species, metagenome, marker genes, metatranscriptomics)
Technologies used in meta’omics

Module 2: Taxonomic Analysis + Stats Module

What is 16S? Why is it useful? What are its limitations? Methods
16S data processing
Clustering and Classification – OTUs vs. ASVs ; Taxonomy
Diversity (and co-occurrence?)
Statistics and machine learning related to microbiome studies

Lab Practical

Module 3: Bridging Taxonomy and Function

Brief intro on gene annotations and different granularity
Inferring function from taxa
Different methods for functional prediction
Focus on PICRUSt2
Limitations of all prediction methods

Lab Practical

Module 4: Metagenomics Functional

Non-assembly (this section) versus assembly based approache
Filtering out host reads (especially in low microbial load samples)
Metagenomics taxa using Kraken & Braken
Metagenomics function using HUMAnN or DIY (mmseqs)
Correction for read length, genome size, etc. (microbecensus)
Stratified functional tables (e.g. linking functions from the taxa they originate from)
Driver versus passenger functional selections
Special functions (e.g. AMR genes)