Scientific criteria and guidelines for identifying, reporting and interpreting omics data – transcriptomics, proteomics, metabolomics and lipidomics – in meat science research
Abstract
Omics approaches offer a rapidly expanding and highly valuable means to study meat science research questions associated with on-farm and post-farm activities. To help ensure the reliability, reproducibility, and transparency of these methodologies, this paper provides practical recommendations for identifying, reporting and interpreting transcriptomics, proteomics, metabolomics, and lipidomics studies in meat research with a focus on best practices regarding the experimental designs, data analysis, and interpretation of the results. Rather than restricting methodological innovation, these guidelines aim to establish recommended scientific criteria that enhance reproducibility, facilitate the integration of independently generated omics datasets through standardized reporting, compatible metadata and transparent analytical workflows among studies submitted to Meat Science journal. Although individual technologies continue to evolve, adherence to the key principles and criteria will substantially improve the scientific soundness and long-term impact of omics generated data applied to meat science research.
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BibTeX
@article{gagaoua2026scientific,
title = {{Scientific criteria and guidelines for identifying, reporting and interpreting omics data – transcriptomics, proteomics, metabolomics and lipidomics – in meat science research}},
author = {Mohammed Gagaoua and Thierry Astruc and Marjeta Čandek-Potokar and David Gerrard and Eric Ponnampalam and Peter Purslow and Wangang Zhang and Mónica Flores},
journal = {Meat Science},
year = {2026},
month = nov,
doi = {10.1016/j.meatsci.2026.110250},
url = {https://hal.inrae.fr/hal-05856185v1},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Scientific criteria and guidelines for identifying, reporting and interpreting omics data – transcriptomics, proteomics, metabolomics and lipidomics – in meat science research
AU - Mohammed Gagaoua
AU - Thierry Astruc
AU - Marjeta Čandek-Potokar
AU - David Gerrard
AU - Eric Ponnampalam
AU - Peter Purslow
AU - Wangang Zhang
AU - Mónica Flores
PY - 2026
DA - 2026/11/07
JO - Meat Science
DO - 10.1016/j.meatsci.2026.110250
UR - https://hal.inrae.fr/hal-05856185v1
AB - Omics approaches offer a rapidly expanding and highly valuable means to study meat science research questions associated with on-farm and post-farm activities. To help ensure the reliability, reproducibility, and transparency of these methodologies, this paper provides practical recommendations for identifying, reporting and interpreting transcriptomics, proteomics, metabolomics, and lipidomics studies in meat research with a focus on best practices regarding the experimental designs, data analysis, and interpretation of the results. Rather than restricting methodological innovation, these guidelines aim to establish recommended scientific criteria that enhance reproducibility, facilitate the integration of independently generated omics datasets through standardized reporting, compatible metadata and transparent analytical workflows among studies submitted to Meat Science journal. Although individual technologies continue to evolve, adherence to the key principles and criteria will substantially improve the scientific soundness and long-term impact of omics generated data applied to meat science research.
SN - 0309-1740
SN - 0309-1740
ER -