SWATH‐ID: An instrument method which combines identification and quantification in a single analysis
Bibliographic record
Abstract
-MS, are showing great potential to reliably quantify significant numbers of peptides and proteins in an unbiased manner. These developments have enhanced interest in developing a single DIA method that integrates qualitative and quantitative analysis, eliminating the need of a prebuilt library of peptide spectra, which are created through data-dependent acquisition methods or from public repositories. Here, we introduce a new DIA approach, referred to as "SWATH-ID," which was developed to allow peptide identification as well as quantitation. The SWATH-ID method is composed of small Q1 windows, achieving better selectivity and thus significantly improving high-confidence peptide extractions from data files. Furthermore, the SWATH-ID approach transmits precursor ions without fragmentation as well as their fragments within the same SWATH acquisition period. This provides a single scan that includes all precursor ions within the isolation window as well as a record of all of their fragment ions, substantially negating the need for a survey scan. In this way all precursors present in a small Q1 window are associated with their fragment ions, improving the identification specificity and providing a more comprehensive and in-depth view of protein and peptide species in complex samples.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".