Learn about the tools, processes, and analysis approaches used in the field of genome-resolved metagenomics.

This course will cover the use of publicly available resources to manage, share, analyse, and interpret metagenomics data, focussing primarily on assembly-based approaches.

The delivered content will involve participants learning via live lectures and presentations, followed by live Q&As with the trainers. Practical experience will be developed in group activities and in computational exercises run using containerised tools on our training infrastructure.

Who is this course for?

This course is for life scientists who are working in the field of metagenomics and are currently in the early stages of data analysis. Participants should have some prior experience of using bioinformatics in their research.

The practical sessions in the course require a basic understanding of the Unix command line and the R statistics package. If you are not already familiar with these then please ensure that you complete these free tutorials before you attend the course:

Introduction to the Unix environment – https://swcarpentry.github.io/shell-novice/index.html
Introduction to R – https://swcarpentry.github.io/r-novice-inflammation/
Unix skill level required

To participate in the course, attendees should have a basic understanding of Unix commands and the command-line interface. Below is a primer to help potential participants assess their suitability. Please see the 'Additional information' tab for a detailed description of the commands that you should be able to manage to navigate through the course practicals.

After the course you should be able to:

Conduct appropriate quality control and decontamination of metagenomic data and run simple assembly pipelines on short-read and long-read data
Utilise public datasets and resources to identify relevant data for analysis
Apply relevant tools in the analysis of metagenomic data
Submit metagenomics data to online repositories for sharing and future analysis
Apply knowledge in the areas of strain resolution and additional functional analysis

During this course you will learn about:

Different types of metagenomic data (short-read and long-read)
Assembly and metagenome assembled genomes (MAGs)
Data analysis: MGnify, HMMER, InterPro, GO, FASTQC, and pathway analyses
Data standards and submission:
European Nucleotide Archive (ENA)
Genomic Standards Consortium (GSC)
SRA
Webin
Metagenomic data analysis workflows