An R package developed for streamlining the integration and analysis of EMBL-EBI HoloFood data. This utility package simplifies access to the resource, enabling direct loading of data into formats tailored for multiomics downstream analytics. With this tool, users can efficiently search and retrieve data from the EBI HoloFood resource.
The retrieved data is available in MultiAssayExperiment / TreeSummarizedExperiment format similarly to the data acquired with the MGnifyR package from the MGnify database. This compatibility ensures users can seamlessly combine and analyze datasets from both sources, leading to valuable insights into intricate biological systems.
This research has received funding from the Horizon 2020 Programme of the European Union within the FindingPheno project under grant agreement No 952914.
FindingPheno 
FindingPheno, an EU-funded project, is dedicated to developing computational tools and methodologies for the integration and analysis of multi-omics data. Its primary objective is to deepen our understanding of the interactions between hosts and their microbiomes.
HoloFood 
HoloFood, a project funded under EU’s Horizon 2020 programme, employed a holistic, “hologenomic”, approach to enhance the efficiency of food production systems. This involved exploring the biomolecular and physiological processes triggered by the incorporation of feed additives and novel sustainable feeds in farmed animals.
The HoloFood database, hosted by European Bioinformatics Institute (EMBL-EBI), houses data gathered during the project, encompassing multiple omics, including metabolomics and various other biomolecular measurements. Notably, it does not include data on metagenomic and untargeted metabolomic analyses. However, metagenomic data from the project can be accessed through the MGnify database, while untargeted metabolomic data is stored in the MetaboLights database. To explore available datasets in HoloFood, you can utilize the API browser.
MGnify 
EMBL-EBI’s MGnify serves as a repository for microbiome data, offering a wide array of analyses encompassing metabarcoding, metatranscriptomic, and metagenomic datasets from diverse environments. This platform provides comprehensive taxonomic assignments and functional annotations for these datasets. The data can be accessed through MGnifyR package.
MetaboLights 
MetaboLights, managed by EMBL-EBI, serves as a repository for metabolomic data. It can be accessed through HoloFoodR package.
miaverse 
miaverse is an R/Bioconductor framework specialized for microbiome downstream data analysis, leveraging the TreeSummarizedExperiment class. It offers a comprehensive suite of tools for microbiome bioinformatics. Additionally, miaverse includes the tutorial book Orchestrating Microbiome Analysis (OMA), which aims to guide users in conducting microbiome data analysis and sharing best practices in microbiome data science.
Installation
Bioc-release
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("HoloFoodR")Bioc-devel
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
# The following initializes usage of Bioc devel
BiocManager::install(version="devel")
BiocManager::install("HoloFoodR")GitHub
remotes::install_github("EBI-Metagenomics/HoloFoodR")Basic usage
For more detailed instructions read the associated function help, function reference page and vignette (vignette("HoloFoodR"))
library(HoloFoodR)
# Search samples
samples <- doQuery("samples")
# Search animals
animals <- doQuery("animal")
# Fetch data on certain sample
samples <- c("ACCESSION_ID")
sample_data <- getData(accession.type = "samples", accession = samples)
# Fetch data on genome catalogues
genome_catalogues <- getData(type = "genome-catalogues")
# Fetch data on genomes in certain genome catalogue
catalogues <- c("ACCESSION_ID")
genomes <- getData(
type = "genomes",
accession.type = "genome-catalogues",
accession = catalogues)
# Fetch data on untargeted metabolites
metabolites <- getMetaboLights(study_id)
# Fetch data as MultiAssayExperiment
samples <- c("ACCESSION_ID")
mae <- getResult(accession = samples)