Combining Percolator with X!Tandem for Accurate and Sensitive Peptide Identification
Bibliographic record
Abstract
In this work, Percolator was successfully interfaced with X!Tandem using a PHP program to generate an improved search platform, X!Tandem Percolator. In order to achieve the best classification performance of peptide identifications in Percolator, a set of experimentally validated spectral identifications (34,993 MS/MS spectra) were used to guide the development of discriminatory features from X!Tandem search results. By comparing the features (e.g., Log(E) and mass error) of these experimentally validated peptide matches with those of false identifications, a comprehensive set of features can be chosen for Percolator in an objective and rational manner. The accuracy of X!Tandem Percolator was demonstrated by comparing the estimated q-value of the validated data set with the empirical q-value. By comparing the results from the X!Tandem Percolator and the original X!Tandem, superior sensitivity and specificity of the X!Tandem Percolator result was demonstrated on various shotgun proteomic data sets under different search conditions. In all of the cases studied in this work, X!Tandem Percolator could improve the number of peptide identifications at the same level of q-values.
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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 | 0.001 | 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".