MetaboMiNR: An Online Tool to Analyze Label-Free Proteomic Experiments With a Focus on Metabolism and Nuclear Receptors
Bibliographic record
Abstract
Label-free quantification (LFQ) proteomics is growing in popularity and becoming increasingly more accessible to researchers, empowering them to compare proteome-wide changes between different treatment conditions. However, it remains difficult to leverage the full potential of LFQ data when the researcher has limited experience in proteomics and/or bioinformatics due to the complexity of data analysis. Here, we present MetaboMiNR, an easy-to-use web application for the analysis of LFQ data with a focus on metabolism and nuclear receptors (NRs). MetaboMiNR guides users through an intuitive process with clear instructions and minimal user input to conduct statistical analysis and produce publication ready plots. Users may input a MaxQuant-generated output file and perform standard global analysis and data quality control with the click of a button. The application offers 3 additional unique features: 1) Metabolism Miner extracts a user-selected Reactome pathway from the dataset, 2) Nuclear Receptor Miner extracts the target genes of a user-selected NR, and 3) Individual Plotter produces publication-ready bar plots for a selected protein. The utility of this application was demonstrated by analyzing a previously published dataset from mice treated with LDT409, a synthetic peroxisome proliferator-activated receptor agonist. MetaboMiNR can be accessed freely at https://cumminslab.shinyapps.io/MetaboMiNR/.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.057 | 0.036 |
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".