Mouse Quantitative Proteomics Knowledgebase: reference protein concentration ranges in 20 mouse tissues using 5000 quantitative proteomics assays
Bibliographic record
Abstract
MotivationLaboratory mouse is the most used animal model in biological research, largely due to its high conserved synteny with human. Researchers use mice to answer various questions ranging from determining a pathological effect of knocked out/in gene to understanding drug metabolism. Our group developed >5000 quantitative targeted proteomics assays for 20 mouse tissues and determined the concentration ranges of a total of >1600 proteins using heavy labeled internal standards. We describe here MouseQuaPro; a knowledgebase that hosts this collection of carefully curated experimental data. ResultsThe web-based application includes protein concentrations from >700 mouse tissue samples from three common research strains, corresponding to >200k experimentally determined concentrations. The knowledgebase integrates the assay and protein concentration information with their human orthologs, functional and molecular annotations, biological pathways, related human diseases and known gene expressions. At its core are the protein concentration ranges, which provide insights into (dis)similarities between tissues, strains and sexes. MouseQuaPro implements advanced search as well as filtering functionalities with a simple interface and interactive visualization. This information-rich resource provides an initial map of protein absolute concentration in mouse tissues and allows guided design of proteomics phenotyping experiments. The knowledgebase is available on mousequapro.proteincentre.com. AVAILABILITY AND IMPLEMENTATION: The knowledgebase is available free of charge on http://mousequapro.proteincentre.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.041 | 0.041 |
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".