November 11, 2025

If you have ever spent a late night trying to make sense of an omics dataset, you know the feeling: peaks, pathways, p-values, numbers, and more numbers… and somewhere in there, the answers to critical scientific questions. But turning data into insight requires more than numbers; it requires clarity, transparency, and trust. That’s why Afekta is proud to set a new benchmark in metabolomics reporting by adopting the Organisation for Economic Co-operation and Development (OECD) Omics Reporting Framework, ensuring our reports meet internationally recognized standards for quality and reproducibility.

Why we need it?

Omics technologies, be it metabolomics, transcriptomics, or proteomics, have become indispensable across research and regulatory domains – from toxicology and nutrition to disease mechanisms and environmental studies. Yet, despite their promise, inconsistency in reporting has long been a barrier to scientific progress. One study reports every step of data processing in detail. Another simply says, “we normalized it.” Normalized how? With what? This lack of transparency makes it difficult for others to evaluate, reproduce, or compare results.

To address this, OECD developed the Omics Reporting Framework (OORF): Guidance on reporting elements for the regulatory use of omics data from laboratory-based toxicology studies.  It provides a structured, harmonized approach for reporting omics data generated in laboratory-based studies, particularly those intended for regulatory or comparative scientific use. The OORF emphasizes traceability, transparency, and quality assurance, covering every stage of the workflow: study design, sample preparation, data acquisition, quality control, and bioinformatics analysis. By standardizing what should be reported and how, the framework builds confidence in omics data across academia, industry, and regulators worldwide.

What we report?

At Afekta, we have structured our reports to be compliant with the Study Summary Reporting Module (SSRM), Data Acquisition and Processing Reporting Module (DAPRM) for mass spectrometry–based metabolomics, and Data Analysis Reporting Modules (DARM) for differentially abundant molecules and multivariate statistical analysis, in line with the OECD Omics Reporting Framework standards. Our metabolomics reports now make every step of the analysis fully traceable: from raw LC–MS data to identification. Our reports include clear descriptions of sample preparation, analytical methods, instrument parameters, quality controls, data analysis tools and software, metabolite identification, as well as links to relevant publications, leaving no room for ambiguity. All procedures are performed in accordance with established SOPs and Good Laboratory Practices (GLP), whenever applicable. Results are carefully structured so that our customers can review, interpret, and reuse the data with confidence.

Data acquisition

Every project is supported by comprehensive documentation covering sample randomisation, preparation, and processing, including metabolite extraction procedures, quality control and blank sample handling, certificates of analysis, and storage conditions. Our LC–MS methods are reported in full detail: from chromatographic settings and ionization parameters to acquisition modes, providing complete methodological transparency.

Data analysis

The data analysis workflow is equally well-defined. We describe each step of peak picking, alignment, pre-processing, and statistical analysis, specifying all software tools, versions, parameters, and references used. Reports also include details of signal drift and batch correction methods, if any, with parameters for assessing and correcting intensity drift both within and across batches. All normalization, missing-value imputation, and feature selection criteria are presented clearly. We also report the relative standard deviations (RSDs) of feature intensities along with the detection rate and dispersion ratio. Moreover, we also apply robust versions of RSD and dispersion ratio, as they are less influenced by single outlying QCs and do not assume normal distribution (Broadhurst et al.). We also provide a comprehensive visual summary of data quality including PCA plots showing the clustering of QC samples in relation to biological samples, as well as histograms illustrating the distribution of the key quality metrics. We also provide a bar plot summarising the number of features classified as good, contaminant, low QC detection, or low quality.

Metabolite annotation

For metabolite annotation, we specify the methods used, indicate whether reference standards are from in-house, in-silico, or external spectral libraries, and name the libraries employed. For each annotated metabolite, our reports include m/z, retention time, MS/MS fragmentation data, ion form, molecular formula, common metabolite name, database identifiers, metabolite class, and MSI level of identification following the Metabolomics Standards Initiative (MSI) levels 1–4 with clear distinction between identified compounds, putative annotations, characterized classes, and unknowns.

The results

This level of reporting might add a few extra tables and documentation steps, but it transforms how metabolomics data can be shared and trusted. For academic customers, this means one less thing to worry about when preparing for peer review. Everything a journal might possibly ask for, be it QC or pre-processing details, is already in place. For commercial customers, it means confidence that the report format will satisfy authorities and help move projects forward without delays. And for everyone we work with, by aligning our reporting structure with the OECD Omics Reporting Framework, Afekta sets a new benchmark for reliability and transparency in metabolomics ensuring that every dataset we deliver are not only scientifically rigorous but also fully interpretable, comparable, and reusable.

Ambrin Farizah Babu