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Peer-reviewedCC BY 4.0Proteomics

How Reusable Are Public Proteomics Datasets in Practice?

  • Giuseppe G. F. Leite1
  • Victor Corasolla Carregari2
  • Alexandre Keiji Tashima3,4
  • Reinaldo Salomão1
4 affiliations
  1. 1Division of Infectious Diseases Department of Medicine Escola Paulista de Medicina Universidade Federal de Sao Paulo São Paulo Brazil
  2. 2Department of Laboratory and Hematological Sciences Unit of Chemistry Biochemistry and Molecular Biology “A. Gemelli” Hospital Foundation IRCCS Rome Italy
  3. 3Department of Biochemistry Escola Paulista de Medicina Universidade Federal de Sao Paulo São Paulo Brazil
  4. 4Laboratory of Mass Spectrometry Applied to Biomolecules Escola Paulista de Medicina Universidade Federal de Sao Paulo São Paulo Brazil
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Abstract

Public proteomics repositories have expanded rapidly, but large‐scale reuse still depends on whether deposited studies are computationally reusable. We provide an evidence‐based snapshot of this gap by evaluating 500 non‐redundant human ProteomeXchange datasets released in 2025. Experimental design annotation was classified as metadata‐based when a deposited table (e.g., SDRF, spreadsheet, or text table) explicitly linked samples, files, channels, or quantitative columns to biological groups; sample‐based when groups could only be inferred from sample, column or channel names; and no information when reliable group assignment was not possible. Sample‐based datasets represented the largest category, comprising 195 deposits (39.0%), followed by datasets lacking reliable design information (170; 34.0%) and metadata‐based datasets (135; 27.0%). Among metadata‐based deposits, 81 (60.0%) included additional contextual covariates. Processed quantitative outputs were fully available in 245 datasets (49.0%), partially available in 168 (33.6%), and absent in 87 (17.4%). Combining processed quantitative outputs with available biological design information, 189 datasets (37.8%) supported direct matrix‐level reuse. Under a stricter scenario requiring metadata‐based design, contextual covariates and available processed quantitative outputs, only 64 datasets (12.8%) fulfilled all criteria. These findings suggest that scalable reuse requires more consistent machine‐actionable sample‐to‐file mapping, interpretable design annotation, processing information and processed outputs, while preserving raw data for harmonized reanalysis.

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Cite

BibTeXRIS
BibTeX
@article{leite2026reusable,
  title = {{How Reusable Are Public Proteomics Datasets in Practice?}},
  author = {Giuseppe G. F. Leite and Victor Corasolla Carregari and Alexandre Keiji Tashima and Reinaldo Salomão},
  journal = {Proteomics},
  year = {2026},
  month = sep,
  doi = {10.1002/pmic.70175},
  url = {https://doi.org/10.1002/pmic.70175},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - How Reusable Are Public Proteomics Datasets in Practice?
AU  - Giuseppe G. F. Leite
AU  - Victor Corasolla Carregari
AU  - Alexandre Keiji Tashima
AU  - Reinaldo Salomão
PY  - 2026
DA  - 2026/09/01
JO  - Proteomics
DO  - 10.1002/pmic.70175
UR  - https://doi.org/10.1002/pmic.70175
AB  - Public proteomics repositories have expanded rapidly, but large‐scale reuse still depends on whether deposited studies are computationally reusable. We provide an evidence‐based snapshot of this gap by evaluating 500 non‐redundant human ProteomeXchange datasets released in 2025. Experimental design annotation was classified as metadata‐based when a deposited table (e.g., SDRF, spreadsheet, or text table) explicitly linked samples, files, channels, or quantitative columns to biological groups; sample‐based when groups could only be inferred from sample, column or channel names; and no information when reliable group assignment was not possible. Sample‐based datasets represented the largest category, comprising 195 deposits (39.0%), followed by datasets lacking reliable design information (170; 34.0%) and metadata‐based datasets (135; 27.0%). Among metadata‐based deposits, 81 (60.0%) included additional contextual covariates. Processed quantitative outputs were fully available in 245 datasets (49.0%), partially available in 168 (33.6%), and absent in 87 (17.4%). Combining processed quantitative outputs with available biological design information, 189 datasets (37.8%) supported direct matrix‐level reuse. Under a stricter scenario requiring metadata‐based design, contextual covariates and available processed quantitative outputs, only 64 datasets (12.8%) fulfilled all criteria. These findings suggest that scalable reuse requires more consistent machine‐actionable sample‐to‐file mapping, interpretable design annotation, processing information and processed outputs, while preserving raw data for harmonized reanalysis.
SN  - 1615-9853
SN  - 1615-9861
ER  -