The ProteomeXchange consortium at 10 years: 2023 update

Abstract

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.

Figure 1.

Number of submitted datasets per month to PX resources, ranging from May 2012 to June 2022. Publicly released datasets are colored in green, unreleased datasets are colored in yellow.

Figure 2.

Overview figure including the current PX resources and the main efforts devoted to data re-use of public proteomics datasets. Different data types are listed, including protein quantitative expression, integration of genomics and proteomics data (proteogenomics), including metagenomics and metaproteomics, peptide and protein sequences, and PTMs. For each data type, the corresponding data resources where these data can be accessed are highlighted. Additional data re-use activities are also indicated, e.g. the efforts in the context of the Human Proteome Project, software benchmarking, machine learning approaches and the creation of spectral libraries. Finally, other bioinformatics resources re-using proteomics data are also indicated (ProteomeHD, MatrisomeDB and GPMDB).