Integrating multi-omics technologies to decipher microbiome functions
Abstract
Multi-omics approaches have revolutionized our understanding of microbial communities by enabling simultaneous interrogation of genomic, transcriptomic, proteomic, and metabolomic data. The systematic integration and analysis of these deep datasets help decipher the functional roles of microbiomes, providing critical insights into microbial activities, interactions, and dynamics across diverse environments. Biological complexity makes multi-omics analysis of a single, isolated organism demanding but highly informative, yet this complexity increases further when samples comprise hundreds to thousands of individual species. As microbiome research continues to expand into clinical, environmental, and engineered systems, standardized workflows, benchmarked datasets, and community-driven initiatives are essential to ensure reproducibility, standardization and interpretability. Establishing and disseminating best practices for experimental design, data processing, and integrative analyses will be critical for maximizing comparability and scientific rigor across studies. This perspective highlights recent advances in multi-omics microbiome research, outlines key obstacles in data integration and metadata harmonization, and proposes a collaborative roadmap for scalable, FAIR-compliant multi-omics investigations and potentially disruptive Artificial Intelligence (AI) advances comparable to those of AlphaFold in the field of microbiome science.
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BibTeX
@article{bossche2026integrating,
title = {{Integrating multi-omics technologies to decipher microbiome functions}},
author = {Tim van den Bossche and Eunice Adeline Lazau and Velma Aho and J. Alfredo Blakeley-Ruiz and Maximilian Wolf and Benoit Josef Kunath and Luis Valentin-Alvarado and Patrick Hellwig and Pieter Verschaffelt and Andrew Rajczewski and Jannie. Henderickx and Tomi Suomi and Feng Xian and Shruti Shah and Lennart Martens and Dirk Benndorf and Samir Damare and Bastiaan Willem Haak and Sven-Bastiaan Haange and Paul Piehowski and Anne Kupczok and Daniel Figeys and Bart Mesuere and Magnus Palmblad and Robert Hettich and Laura Elo and Juan Antonio Vizcaíno and Neha Garg and Zhong Wang and Muzaffer Arıkan and Lee Ann Mccue and Timothy Griffin and Laure-Alix Clerbaux and Robert Heyer and Marnix Medema and Sabine Matallana-Surget and David Gomez-Varela and Thilo Muth and Bree Tillett and Jean Armengaud and Robert Finn and Paul Wilmes and J. Gregory Caporaso and Lucia Grenga and Pratik Dilip Jagtap},
journal = {Nature Communications},
year = {2026},
month = sep,
doi = {10.1038/s41467-026-77539-4},
url = {https://hal.science/hal-05768814v1},
note = {CC BY},
}
RIS
TY - JOUR
TI - Integrating multi-omics technologies to decipher microbiome functions
AU - Tim van den Bossche
AU - Eunice Adeline Lazau
AU - Velma Aho
AU - J. Alfredo Blakeley-Ruiz
AU - Maximilian Wolf
AU - Benoit Josef Kunath
AU - Luis Valentin-Alvarado
AU - Patrick Hellwig
AU - Pieter Verschaffelt
AU - Andrew Rajczewski
AU - Jannie. Henderickx
AU - Tomi Suomi
AU - Feng Xian
AU - Shruti Shah
AU - Lennart Martens
AU - Dirk Benndorf
AU - Samir Damare
AU - Bastiaan Willem Haak
AU - Sven-Bastiaan Haange
AU - Paul Piehowski
AU - Anne Kupczok
AU - Daniel Figeys
AU - Bart Mesuere
AU - Magnus Palmblad
AU - Robert Hettich
AU - Laura Elo
AU - Juan Antonio Vizcaíno
AU - Neha Garg
AU - Zhong Wang
AU - Muzaffer Arıkan
AU - Lee Ann Mccue
AU - Timothy Griffin
AU - Laure-Alix Clerbaux
AU - Robert Heyer
AU - Marnix Medema
AU - Sabine Matallana-Surget
AU - David Gomez-Varela
AU - Thilo Muth
AU - Bree Tillett
AU - Jean Armengaud
AU - Robert Finn
AU - Paul Wilmes
AU - J. Gregory Caporaso
AU - Lucia Grenga
AU - Pratik Dilip Jagtap
PY - 2026
DA - 2026/09/08
JO - Nature Communications
DO - 10.1038/s41467-026-77539-4
UR - https://hal.science/hal-05768814v1
AB - Multi-omics approaches have revolutionized our understanding of microbial communities by enabling simultaneous interrogation of genomic, transcriptomic, proteomic, and metabolomic data. The systematic integration and analysis of these deep datasets help decipher the functional roles of microbiomes, providing critical insights into microbial activities, interactions, and dynamics across diverse environments. Biological complexity makes multi-omics analysis of a single, isolated organism demanding but highly informative, yet this complexity increases further when samples comprise hundreds to thousands of individual species. As microbiome research continues to expand into clinical, environmental, and engineered systems, standardized workflows, benchmarked datasets, and community-driven initiatives are essential to ensure reproducibility, standardization and interpretability. Establishing and disseminating best practices for experimental design, data processing, and integrative analyses will be critical for maximizing comparability and scientific rigor across studies. This perspective highlights recent advances in multi-omics microbiome research, outlines key obstacles in data integration and metadata harmonization, and proposes a collaborative roadmap for scalable, FAIR-compliant multi-omics investigations and potentially disruptive Artificial Intelligence (AI) advances comparable to those of AlphaFold in the field of microbiome science.
SN - 2041-1723
SN - 2041-1723
ER -