A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä

HoloFoodR: a statistical programming framework for holo-omics data integration workflows




TekijätBorman, Tuomas; Sannikov, Artur; Finn, Robert D; Limborg, Morten Tønsberg; Rogers, Alexander B; Kale, Varsha; Hanhineva, Kati; Lahti, Leo

ToimittajaKendziorski Christina

KustantajaOxford University Press (OUP)

Julkaisuvuosi2025

Lehti: Bioinformatics

Artikkelin numerobtaf605

ISSN1367-4803

eISSN1367-4811

DOIhttps://doi.org/10.1093/bioinformatics/btaf605

Julkaisun avoimuus kirjaamishetkelläAvoimesti saatavilla

Julkaisukanavan avoimuus Kokonaan avoin julkaisukanava

Verkko-osoitehttps://doi.org/10.1093/bioinformatics/btaf605

Rinnakkaistallenteen osoitehttps://research.utu.fi/converis/portal/detail/Publication/505305696


Tiivistelmä

Summary: Holo-omics is an emerging research area that integrates multi-omic datasets from the host organism and its microbiome to study their interactions. Recently, curated and openly accessible holo-omic databases have been developed. The HoloFood database, for instance, provides nearly 10,000 holo-omic profiles for salmon and chicken under controlled treatments. However, bridging the gap between holo-omic data resources and algorithmic frameworks remains a challenge. Combining the latest advances in statistical programming with curated holo-omic data sets can facilitate the design of open and reproducible research workflows in the emerging field of holo-omics.

Availability and implementation: HoloFoodR R/Bioconductor package and the source code are available under the open-source Artistic License 2.0 at the package homepage https://doi.org/10.18129/B9.bioc.HoloFoodR.

Supplementary information: Available in the package vignette https://ebi-metagenomics.github.io/HoloFoodR/articles/case_study.html.

Keywords: bioconductor; data integration; holo-omics; metabolomics; metagenomics; multi-omics.


Ladattava julkaisu

This is an electronic reprint of the original article.
This reprint may differ from the original in pagination and typographic detail. Please cite the original version.




Julkaisussa olevat rahoitustiedot
This work was supported by the European Commission in the framework of the Horizon2020 Project FindingPheno [GA 952914] and HoloFood [GA 817729]. L.L. was supported by Research Council of Finland [grant number 330887]. A.S. and K.H. were supported by Jane and Aatos Erkko Foundation and the Research Council of Finland [grant numbers 321716, 334814]. M.T.L. was supported by the Danish National Research Foundation [grant DNRF143].


Last updated on 2025-11-11 at 08:01