Microbes are everywhere and the study of microbiomes to elucidate the impact of microbes in various ecosystems has become a multidisciplinary science involving microbiology, statistics, computer science, and molecular biology. 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. The CBW has developed a 2-day course providing an introduction to metagenomic data analysis (both read-based and assembly-based) 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.
Note: this workshop can be taken with or without our Beginner Microbiome Analysis workshop. The workshops are in the same location and set with one day in between.
Participants will gain practical experience and skills to be able to:
- Understand the advantages and limitations of metagenomic data analysis
- Devise an appropriate bioinformatics workflow for processing and analyzing microbiome shotgun metagenomic sequence data
- Perform both read-based and assembly-based analyses of shotgun metagenomic sequence data
- Conduct various types of functional annotation and modelling
- Apply appropriate statistics to undertake rigorous data analysis
Graduates, postgraduates, staff bioinformaticians and PIs working with or about to embark on analysis of shotgun metagenomic sequence data from microbiome-focused experiments.
Participants should be comfortable with reading and writing basic R or Bash, or be enrolled in the Beginner Microbiome Analysis course. Participants should have some prior experience with microbiome sequencing data (for example, have completed a marker gene analysis of microbiome data).
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: 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 short reads and removal of host contamination using Kneaddata in the Unix command line
- Taxonomic profiling of short reads using Kraken and MetaPhlAn in the Unix command line
- Analysis of taxonomic assignment results using Phyloseq in R
Module 2: 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 short 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 3: Assigning Functions
- From MAGs to genes
- Overview of functional assignments
- Methods for assigning functions from global overviews of functions encoded in MAGs to focusing on specific systems with an emphasis on metabolism, but also using antimicrobial Resistance (AMR), cell wall biogenesis and biofilm machineries as example sub-systems.
- Stratifying functions by taxonomy
- Extending to metatranscriptomics
Lab practical
- Prediction of gene models and annotations using Prokka
- Enzyme annotation with deepRC (others are available)
- Metabolic reconstructions with GapSeq
- AMR annotation of reads/contigs using CARD RGI in the Unix command line
Module 4: Visualization and Finding Functional Significance
- How to incorporate confounding variables into statistical analyses of microbiome data (e.g. age and sex in human-based studies)
- Data normalization
- Differential abundance (metagenomics) / expression (metatranscriptomics) analysis
- Gene set enrichment analyses
- Visualizing functional and taxonomic relationships in Cytoscape
Lab practical
- Identifying differentially expressed genes using DESeq2
- From DE genes to pathway enrichment
- Pathway visualization with Cytoscape
Duration: 2 days
Start: May 29, 2025
End: May 30, 2025
Status: Application Open
Apply
Download the poster announcing this workshop
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