COMPLEXITY AND SCORING FUNCTION OF MS/MS PEPTIDE DE NOVO SEQUENCING
Bibliographic record
Abstract
Tandem mass spectrometry (MS/MS) has become a standard way for identifying peptides and proteins. A scoring function plays an important role in the MS/MS data analysis. De novo sequencing is the computational step to derive a peptide sequence from an MS/MS spectrum, normally by constructing the peptide that maximizes the scoring function. A number of polynomial time algorithms have been developed based on scoring functions that consider only either the N-terminal or C-terminal fragment ions of the peptide. It remains unknown whether the consideration of the internal fragment ions will still be polynomial time solvable. In this paper, we prove that the internal fragment ions make the de novo sequencing problem NP-complete. We also propose a regression model based scoring method to incorporate correlations between the fragment ions. Our scoring function is combined with PEAKS de novo sequencing algorithm and tested on ion trap data. The experimental results show that the regression model based scoring method can remarkably improve the de novo sequencing accuracy.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".