MRMAssayDB: a comprehensive integrated resource for targeted proteomics assays
Bibliographic record
Abstract
INTRODUCTION: Targeted quantitative proteomics is vital for accurate protein measurement in biological samples. Techniques like Multiple Reaction Monitoring (MRM or SRM) and Parallel Reaction Monitoring (PRM), often used with isotopically labeled internal standards, provide absolute quantification, and represent the current gold standard. However, developing and validating assays for individual proteins remains labor-intensive. Several repositories, such as CPTAC, SRMAtlas, PanoramaWeb, and PeptideTracker host targeted assay data with varying levels of detail. MRMAssayDB is an integrated platform that hosts and annotates the curated targeted proteomics assays from these resources. AREAS COVERED: First launched in 2018 and updated in 2021, the latest release of MRMAssayDB includes over 1.1 million assays for 939,000 peptides, enabling quantification of 61,000 proteins from 146 organisms. The database also maps proteins to 19,000 Gene Ontology terms and 4,000 biological pathways. A newly integrated visualization module projects peptide assays onto Alphafold-predicted 3D protein structures, allowing users to examine peptide locations, post-translational modifications, and disease mutations while also supporting mapping to structures in the Protein Data Bank (PDB). EXPERT OPINION: MRMAssayDB significantly improves access to validated proteotypic peptides and transition data, facilitating efficient assay selection and quantitative panel building for researchers in targeted proteomics. Availability: http://mrmassaydb2.proteomicscentre.com.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.090 | 0.140 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".