ReviewJournal of proteome research2026
Unlocking the Next Decade of Proteomics with Standardized, Structured Metadata.
Review in Journal of proteome research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Integrating multi-omics technologies to decipher microbiome functions.Nature communications · 2026Review
- usiGrabber: automating the curation of proteomics spectra data at scale, making large datasets ready for use in machine learning systems.Bioinformatics (Oxford, England) · 2026Article
- Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
The proteomics community has fully embraced data sharing, yet data set metadata provision remains limited, especially at the level of the biological samples and experimental design. This hampers large-scale data reuse, as comprehensive and structured sample context and study design information are often essential for confident, automatic reuse, and (re)interpretation. Although standards such as Sample and Data Relationship Format for Proteomics (SDRF-Proteomics) and supporting tools are already available, their adoption remains limited. Many researchers lack incentives, and enforcement by journals and repositories remains challenging in practice. Still, metadata defines a data set's long-term value. We propose a coordinated plan to dramatically improve metadata annotation of publicly disseminated proteomics data. Funders can drive progress by investing in a sustainable, scalable metadata infrastructure. HUPO-PSI plays a central role in setting community standards and enabling validation. ProteomeXchange repositories are key to implementing and supporting metadata adoption. Data producers must treat metadata as a part of their scientific output. Instrument vendors can contribute by enabling the automatic capture of technical metadata. Software developers should embed SDRF-Proteomics metadata into analysis workflows. Finally, journals and reviewers are well positioned to shape expectations and enforce compliance. By aligning efforts across these stakeholders, we can build the road to large-scale, context-aware reuse and unlock the full value of public proteomics data sets.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.