Comparative metagenomics

Normalization methods, alpha & beta diversity, and differentially abundant taxa

In this practical session we aim to demonstrate how the MGnifyR tool can be used to fetch data and metadata of a MGnify metagenomic analyisis. With this data, we show how to generate diversity metrics for comparative metagenomics. Finally, we will run an analisys to detect differentially abundant taxa. The dataset is the TARA ocean metagenomic study (WMS) corresponding to size fractions for prokaryotes MGYS00002008. Find more information about the TARA Ocean Project.

The data we are using are abundance tables of taxonomic profiles gerated through the MGnify pipeline version 5.0.

workflow

MGnifyR is a library that provides a set of tools for easily accessing and processing MGnify data in R, making queries to MGnify databases through the MGnify API. The benefit of MGnifyR is that data can either be fetched in tsv format or be directly combined in a phyloseq object to run an analysis in a custom workflow.

The excercises are organized in 4 main sections:

  1. Fetching data and preprocessing (MGnifyR)

  2. Normalization, alpha diversity indices and taxonomic profiles visualization (phyloseq)

  3. Comparative metagenomics at community-level: Beta diversity (vegan)

  4. Detection of differentially abundant taxa (SIAMCAT)

Comparative metagenomics hands-on exercises

The practice has been prepared in a Jupyter Notebook available in the gihub repo.

Opening the Jupyter notebook

action Open a new Terminal and type these commands to clone and enter the repository:

cd /home/training/ && mkdir comparative && cd comparative

git clone https://github.com/EBI-Metagenomics/genome-resolved-metagenomics-2022.git

cd genome-resolved-metagenomics-2022

conda activate metagenomics

jupyter-lab course.jupyterlab-workspace

action Find and open Comparative metagenomics notebook in the ‘notebooks’ directory

info The notebook has colored boxes to indicate relevant steps in the analysis or to introduce material for discussion.

Yellow boxes:

  • Up to you: To re-run the analysis changing parametra

  • Questions: For open discussion

Blue boxes:

  • Notes: Informative boxes about the running command

  • Tips: Information useful for results interpretation

info To leave the notebook, you can save the changes and close the window. Type CTRL+C to terminate the process at the terminal.