This workshop is open to all applicants. However, CSAFS ICT participants receive acceptance priority and a discounted registration rate.
Microbes are everywhere and the study of microbiomes has become a multidisciplinary science involving microbiology, statistics, computer science, and molecular biology to elucidate the impact of microbes in various ecosystems, including the human body. Microbiomics, the study of microbes without first culturing and isolating the organisms, has become the principal approach to exploring the diversity, function and ecology of microbial communities. This 3-day course provides an introduction to marker-gene analysis, metagenomic and metatranscriptomic data analysis followed by hands-on practical tutorials for each session to demonstrate the use of relevant bioinformatics and statistical tools. Modules will consist of lectures covering both theoretical and practical components followed by hands-on bioinformatic tutorials guided by instructors and teaching assistants.
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 microbiome sequence data (marker-gene, shotgun metagenomic, and metatranscriptomic data)
- Apply appropriate statistics to undertake rigorous data analysis
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.
Experience with UNIX and R/Python programming languages. Included with this workshop is a virtual pre-session (August 26-28) that covers this prerequisite.
You will require your own laptop computer. Minimum requirements: 1024×768 screen resolution, 2.4GHz CPU, 8GB RAM, 100GB free disk space, recent versions of Windows, Mac OS X or Linux (Most computers purchased in the past 3-4 years likely meet these requirements).
This workshop requires participants to complete pre-workshop tasks and readings.
Module 1: Marker gene profiling
- Overview of amplicon sequencing: pros & cons
- Popular amplicon targets (e.g. 16S, 18S, and ITS)
- Outline of key steps in a 16S/18S/ITS bioinformatic analysis
- Short vs long read amplicon sequencing
Lab practical
- Your choice of 16S/18S/ITS analysis workflow using QIIME2 in a Unix command line: initial importing of reads, denoising and ASV generation, taxonomy assignment, filtering of potential contaminants
Module 2: Microbiome ecology statistics and visualizations
- Overview of microbial ecology metrics (alpha + beta diversity)
- Challenges with different differential abundance methods
- Challenges with microbiome data and compositionality
- Relative vs absolute abundance
Lab practical
- Calculating and visualization of alpha and beta diversity metrics
- Calculation of statistically significant differences
Module 3: Introduction to metagenomics and read-based profiling
- Challenges and benefits of metagenomic data
- Comparison of major approaches (read based vs assembly based)
- Importance of sequencing depth and host contamination
- Long read vs short read
- Approaches for assigning taxonomy to shotgun metagenomic data
Lab practical
- Quality control of reads and removal of host contamination using Kneaddata in the Unix command line
- Taxonomic profiling of reads using Kraken in the Unix command line
- Analysis of taxonomic assignment results using Phyloseq in R
Module 4: Metagenomic Assembly and Binning
- Overview of binning theory/approaches, advantages/disadvantages
- Quality metrics for assembly and binning
- Genome-resolved metagenomics using the Anv’io ecosystem
Lab practical
- Assembly of reads into contigs, binning of contigs and refinement of bins into Metagenome Assembled Genomes (MAGs) using Anv’io in the Unix command line
- Metrics for assessing MAG quality using Anv’io in the Unix command line
Module 5: Assigning Functions
- Overview of functional assignments
- Comprehensive gene annotation and pathways
- Specialized functions of interest (e.g. AMR, methanogenesis, etc.)
- Linking functions to taxonomy
- Extending to metatranscriptomics
Lab practical
- Prediction of gene models and annotations from MAGs using Bakta
- Read-based functional annotation to general functional categories (e.g. UniRef, KEGG, EC numbers)
- Functional annotation with specialized databases
Module 6: Visualization and Finding Functional Significance
- How to incorporate confounding variables into statistical analyses of microbiome data
- Using machine learning (e.g. Random Forests) for diagnostics or identification of important microbial features
- Incorporating metatranscriptomic data using MaAsLin3
Lab practical
- Confounding variables and metatranscriptomics within Maaslin 3
- Random Foerst modelling of microbiome data in R
Duration: 3 days
Start: Sep 15, 2026
End: Sep 17, 2026
Status: Application Open
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