Peptide shifter: Enhancing separation reproducibility using correlated expression profiles
Bibliographic record
Abstract
Chromatographic protein and peptide separation technologies enable comprehensive proteomic analysis of plasma and other complex biological samples by mass spectrometry. However, as the number of separations and/or fractions increases, so does the number of peptides split across fraction boundaries. Irreproducibility of peptide chromatographic separation results in peptides on or near the boundary moving partially or entirely into adjacent fractions. Peptide shifting across fraction boundaries increases the variability of measured peptide abundance, and so there is a trade-off between proteomic comprehensiveness using separation technologies and accurate quantitative proteomic measurements. In this paper, a method for detecting and correcting split peptides, called Peptide Shifter, is introduced and evaluated. An essential component of Peptide Shifter is a global peptide expression profile analysis that allows the inference of the underlying peptide shift pattern without the use of peptide labeling or internal standards. A controlled proteomic analysis of plasma samples demonstrates a 34% decrease in peptide intensity variability after the application of Peptide Shifter.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".