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The ProteomeXchange consortium at 10 years: 2023 update
Open accessAbstract
Mass spectrometry (MS) is by far the most used experimental approach in high-throughput proteomics. The ProteomeXchange (PX) consortium of proteomics resources (http://www.proteomexchange.org) was originally set up to standardize data submission and dissemination of public MS proteomics data. It is now 10 years since the initial data workflow was implemented. In this manuscript, we describe the main developments in PX since the previous update manuscript in Nucleic Acids Research was published in 2020. The six members of the Consortium are PRIDE, PeptideAtlas (including PASSEL), MassIVE, jPOST, iProX and Panorama Public. We report the current data submission statistics, showcasing that the number of datasets submitted to PX resources has continued to increase every year. As of June 2022, more than 34 233 datasets had been submitted to PX resources, and from those, 20 062 (58.6%) just in the last three years. We also report the development of the Universal Spectrum Identifiers and the improvements in capturing the experimental metadata annotations. In parallel, we highlight that data re-use activities of public datasets continue to increase, enabling connections between PX resources and other popular bioinformatics resources, novel research and also new data resources. Finally, we summarise the current state-of-the-art in data management practices for sensitive human (clinical) proteomics data.
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Cites
The FAIR Guiding Principles for scientific data management and stewardship
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., et al. (2016) · Scientific Data · doi:10.1038/sdata.2016.18 · Cited by 18,423 · cited 1×
“PX resources are committed to comply with the FAIR (Findable, Accessible, Interoperable, Re-usable) principles (4) for biological data, support reproducible research and represent the state-of-the-art in proteomics with regards to open data practices.”
MethodUniProt: the universal protein knowledgebase in 2021
The UniProt Consortium, Bateman, A., Martin, M.-J., Orchard, S., Magrane, M., Agivetova, R., Ahmad, S., Alpi, E., Bowler-Barnett, E. H., Britto, R., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa1100 · Cited by 6,372
The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences
Perez-Riverol, Y., Bai, J., Bandla, C., García-Seisdedos, D., Hewapathirana, S., Kamatchinathan, S., Kundu, D. J., Prakash, A., Frericks-Zipper, A., Eisenacher, M., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkab1038 · Cited by 6,125
Self-citationProteomeXchange provides globally coordinated proteomics data submission and dissemination
Vizcaíno, J. A., Deutsch, E. W., Wang, R., Csordas, A., Reisinger, F., Ríos, D., Dianes, J. A., Sun, Z., Farrah, T., Bandeira, N., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.2839 · Cited by 2,729
Self-citationCunningham, F., Allen, J. E., Allen, J., Alvarez-Jarreta, J., Amode, M. R., Armean, I. M., Austine-Orimoloye, O., Azov, A. G., Barnes, I., Bennett, R., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkab1049 · Cited by 1,923
iProX in 2021: connecting proteomics data sharing with big data
Chen, T., Ma, J., Liu, Y., Chen, Z., Xiao, N., Lu, Y., Fu, Y., Yang, C., Li, M., Wu, S., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkab1081 · Cited by 1,134
Self-citationDeutsch, E. W., Csordas, A., Sun, Z., Jarnuczak, A., Perez-Riverol, Y., Ternent, T., Campbell, D. S., Bernal-Llinares, M., Okuda, S., Kawano, S., et al. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw936 · Cited by 865
Self-citationThe Skyline ecosystem: Informatics for quantitative mass spectrometry proteomics
Pino, L. K., Searle, B. C., Bollinger, J. G., Nunn, B., MacLean, B., & MacCoss, M. J. (2020) · Mass Spectrometry Reviews · doi:10.1002/mas.21540 · Cited by 800
Self-citationmzML—a Community Standard for Mass Spectrometry Data
Martens, L., Chambers, M., Sturm, M., Kessner, D., Levander, F., Shofstahl, J., Tang, W. H., Römpp, A., Neumann, S., Pizarro, A. D., et al. (2011) · Molecular & Cellular Proteomics · doi:10.1074/mcp.r110.000133 · Cited by 745
Self-citationThe ProteomeXchange consortium in 2020: enabling ‘big data’ approaches in proteomics
Deutsch, E. W., Bandeira, N., Sharma, V., Perez-Riverol, Y., Carver, J. J., Kundu, D. J., García-Seisdedos, D., Jarnuczak, A. F., Hewapathirana, S., Pullman, B. S., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz984 · Cited by 697
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Cited by
UniProt: the Universal Protein Knowledgebase in 2025
The UniProt Consortium, Bateman, A., Martin, M.-J., Orchard, S., Magrane, M., Adesina, A., Ahmad, S., Bowler-Barnett, E. H., Bye-A-Jee, H., Carpentier, D., et al. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkae1010 · Cited by 2,537
Chen, X., Luo, Y., Zhu, Q., Zhang, J., Huang, H., Kan, Y., Li, D., Xu, M., Liu, S., Li, J., et al. (2024) · Nature Aging · doi:10.1038/s43587-024-00612-4 · Cited by 174
Phage display enables machine learning discovery of cancer antigen–specific TCRs
Croce, G., Lani, R., Tardivon, D., Bobisse, S., de Tiani, M., Bragina, M., Perez, M. A. S., Michaux, J., Pak, H. S., Michel, A., et al. (2025) · Science Advances · doi:10.1126/sciadv.ads5589 · Cited by 23
Diversification of molecular pattern recognition in bacterial NLR-like proteins
(2024) · doi:10.1038/s41467-024-54214-0 · Cited by 23
Woo, J., Loycano, M., Amanullah, M., Qian, J., Amend, S. R., Pienta, K. J., & Zhang, H. (2025) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2025.100949 · Cited by 17
Metadata
- Authors
- Deutsch, E. W., Bandeira, N., Perez-Riverol, Y., Sharma, V., Carver, J. J., Mendoza, L., Kundu, D. J., Wang, S., Bandla, C., Kamatchinathan, S., Hewapathirana, S., Pullman, B. S., Wertz, J., Sun, Z., Kawano, S., Okuda, S., Watanabe, Y., MacLean, B., MacCoss, M. J., Zhu, Y., Ishihama, Y., & Vizcaíno, J. A.
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2022-11-12
- Volume
- 51
- Issue
- D1
- Pages
- D1539-D1548
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- OpenAlex
- W4308834828
- Cited by
- 819
- References
- 61
- FWCI
- 55.93
- Type
- journal article
- Language
- en
- Open access
- gold
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- Deutsch, E. W., Bandeira, N., Perez-Riverol, Y., Sharma, V., Carver, J. J., Mendoza, L., Kundu, D. J., Wang, S., Bandla, C., Kamatchinathan, S., Hewapathirana, S., Pullman, B. S., Wertz, J., Sun, Z., Kawano, S., Okuda, S., Watanabe, Y., MacLean, B., MacCoss, M. J., . . . Vizcaíno, J. A. (2023). The ProteomeXchange consortium at 10 years: 2023 update. Nucleic Acids Research. https://doi.org/10.1093/nar/gkac1040
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