Introduction to metagenomic

Coordinators

Eugénie Pfeifer; Mathieu Jossier; Eoghan King

Goals

By the end of the course, participants will have the confidence to approach metagenomic datasets and understand their relevance to scientific challenges in microbiology, ecology, and beyond. Participants will acquire following skills:

• Comprehensive knowledge and hands-on experience using high computing clusters (job submission, resource allocation, big data management)

• Familiarity with metagenomic sequencing data and analysis tools

• Format sequencing data for statistical analysis

• Interpret graphical representations of microbial diversity and its analysis

• Differentiate between the specific characteristics of animal and plant microbiota

Skills

• Ability to define metagenomics, explain its necessity for understanding microbiome-host (holobiont) interactions, and articulate its relevance across diverse fields (e.g., human health, plant-microbe interactions, and the environment, role of viruses). Explaining how metagenomics advances our knowledge of microbial diversity and ecosystem dynamics.

• Understanding and clearly articulating the complete metagenomic workflow - from sample collection and sequencing strategy (e.g., short-read vs. long-read) to final computational analysis, including the distinction between data types (e.g., 16S amplicon vs. shotgun metagenomics).

• Competence in applying methods for functional annotation and gene prediction, demonstrating how to use bioinformatics to link gene data to functional potential.

• Ease in using R (or RStudio) for data visualization (e.g., bar/box plots, heatmaps) with the goal of clearly communicating complex findings

Content

This course provides a basic and comprehensive introduction to the dynamic field of metagenomics, which is essential for understanding microbiome-host interactions. Participants will receive introductory classes of diverse topics such as the human gut microbiome, plant-microbe interactions, and the roles of viruses (virome) within microbiomes. Metagenomics is key in advancing our understanding of microbial diversity, ecosystem dynamics, and their implications for health, agriculture, and the environment.

Lectures will be given by experts in the field, and participants will analyze real-world datasets by gaining hands-on experience with R (RStudio), computing clusters, and command-line tools. The approaches will include data pre-processing, community analysis, taxonomic profiling, functional annotation, and data visualization.

Format

The course will take place over a period of 5 days. Lectures will be given in the morning, including tutorials and introduction to exercises. In these tasks (done in afternoon), students will learn how to work with computing clusters, and analyze metagenomic datasets. Assistance and support will be given by lectures, and experienced researchers.

Special teaching methods

Project based and bioinformatics.

Language : English

Elective TU

ECTS : 5

Lectures : 10 ;  Directed Study : 15