Slides, course materials, hands-on tutorial

Organisation and utilisation of hologenomic datasets – course notes

This course covered the generation and application of large-scale holo-omic data sets, such as those produced within the HoloFood project. This course was run in September 2022, in-person in Bilbao, as part of the 1st Applied Hologenomics Conference. These course notes include the lecture slides that were presented, as well as the instructions for the practical sessions participants followed.

Course content

During this course you will learn about:

  • Omic data repositories: MGnify, European Nucleotide Archive, MetaboLights, BioSamples
  • Available HoloFood datasets: Genomic, Metagenomic, Metatranscriptomic, Metabolomic, Microbial Genome Catalogues
  • Analysis techniques: Metagenome assembly, recovery of genomes, functional annotation of genomes, environmental metabolomic analysis
  • Integration: Approaches and tools for further holo-omic analysis: e.g. integration of metabolomic data with (meta-)genomic data

Licence: Creative Commons Attribution Non Commercial Share Alike 4.0 International

Contact: holofood-help@ebi.ac.uk

Keywords: hologenomics, metagenomics, Metabolomics, MGnify, Workshop

Resource type: Slides, course materials, hands-on tutorial

Status: Active

Prerequisites:

Familiarity with unix and command line scripting.
Some familiarity with R and/or Python coding.
Understanding of multi-omic concepts.

Learning objectives:

After the course you should be able to:
- Describe the HoloFood data that is available and how it was generated
- Access a range of data types from leading public repositories
- Illustrate how the data sets relate to each other
- Use the data to support your own analysis

Date created: 2022-09-11

Date modified: 2022-09-20

Date published: 2022-09-21

Authors: Sandy Rogers, Varsha Kale, Germana Baldi, Jaelle Brealey, Jacob Rasmussen, Rob Finn, Morten Limborg, Martin Hansen, Melanie Pajero, Sofia Marcos

Contributors: Lorna Richardson, Anna Fotaki, Shyam Gopalakrishnan

Scientific topics: Metatranscriptomics, Sample collections, Metabolomics, Metagenomics


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