Organisation and utilisation of hologenomic datasets course notes


The full programme can be seen on the course website.

Day 1

An overview of a holomic approach — Morten Limborg — Lecture slides

HoloFood sampling and experimental design — Morten Limborg — Lecture slides

HoloFood in Public Archives (practical) — Sandy Rogers — Instructions

Metagenomics data — Germana Baldi / Varsha Kale — Lecture slides

Metagenomics data: MAG generation (practical) — Varsha Kale / Germana Baldi — Instructions

Metagenomics data: continued (practical) — Varsha Kale / Germana Baldi / Sandy Rogers — Instructions

Day 2

From population genomics to hologenomes; Host variation: host genome recovery from gut metagenomics samples in chicken — Morten Limborg / Melanie Pajero / Sofia Marcos — Lecture slides

mGWAS on salmon (practical) — Jaelle Brealey — Instructions

Metabolomics data — Martin Hansen — Lecture slides

Metabolomics data (practical) — Jacob Rasmussen — Instructions

A multi-focal point of view: Integrated analyses of multi-omics data — Rob Finn — Lecture slides

About the course

This course will cover 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.

There is an increasing recognition that organisms do not exist in isolation, but are actually holobionts, composed of the host and the many microorganisms found on or in the individual. The HoloFood project has developed significant multi-omics datasets for both chicken and salmon, with a view to understanding how different feeds impact the gut microbiota, and in turn animal productivity. This course covers how to access and utilise both raw and derived data products, the workflow to achieve genome-resolved metagenomics, analysis of host variation, generation and interpretation of metabolomic data, and approaches to multi-omic integration to understand links between traits and genomic information.

The HoloFood project represents a cornerstone of hologenomic research, providing a blueprint for how data from such projects should be archived, analysed and interlinked. As such, the motivation for this course is to highlight the availability and usability of the HoloFood data in further holo-omic analyses, either as reference sets to compare against, or as source data for subsequent novel analysis.



To follow the practical sessions, various software and data are needed.

See full instructions.

For the practical sessions, familiarity with Unix command line use and scripting with R and/or Python will be needed. These tutorials will be very useful if you are not familiar:

For the lectures, the recommended pre-reading list is:

Indices and tables