Clinical Scorecard: A Billion-Scale Dataset for Proteomics AI

At a Glance

Key Highlights

• MassNet combines 1.54 billion tandem mass spectra from 648 PRIDE and iProX projects and 27,643 LC-MS/MS runs.
• After reanalysis and deduplication, the dataset contained 548 million peptide-spectrum matches, 2.54 million unique peptides, and 133,422 proteins.
• MSDT stores spectra alongside identifications and metadata and was approximately 82% smaller than the combined source spectral and search-result files.
• In a 15-species benchmark, XuanjiNovo outperformed five other de novo sequencing models; performance varied across datasets and instrument platforms.
• The resource excludes unidentified spectra and does not systematically include several modifications, including phosphorylation, glycosylation, and ubiquitination.

Guideline-Based Recommendations

Diagnosis

• The study describes a research resource and de novo peptide-sequencing model; it does not provide clinical diagnostic criteria.

Management

• MassNet and XuanjiNovo are available for research use; the study did not assess clinical diagnostic performance.

Monitoring & Follow-up

• The study reports variation in model results across datasets and mass spectrometry platforms; some older-instrument data required additional model training.

Risks

• MassNet includes only spectra confidently identified by FragPipe or Sage, potentially introducing search-related bias and excluding unidentified spectra.
• The dataset covers only a limited set of common protein modifications; clinically relevant modifications were not systematically included.
• The experimental rescoring model identified fewer matches than established FragPipe and Sage workflows.

Patient & Prescribing Data

No patient treatment population or prescribing data are reported. The resource contains proteomic data from 35 species, with most samples from humans and mice.

No treatments or prescribing recommendations were evaluated. The study did not assess clinical diagnostic performance.

Clinical Best Practices

• Interpret model performance in the context of the dataset and mass spectrometry platform; the study reports variation across both.
• Account for the resource's search-based inclusion criteria and limited systematic coverage of protein modifications when using MassNet.
• Do not infer clinical diagnostic performance from the reported model benchmarks; the study did not evaluate it.

Related Resources & Content

• MassNet resource, PRIDE accession PXD071590