NovoPair: De novo peptide sequencing for tandem mass spectra pair
Bibliographic record
Abstract
With tandem mass spectrometry (MS/MS), spectra can be generated by various methods including collision-induced dissociation (CID), higher-energy collisional dissociation (HCD), electron capture dissociation (ECD) and electron transfer dissociation (ETD). At the same time, de novo sequencing using multiple spectra from the same peptide is becoming popular in proteomics studies. The focus of this work is using a pair of spectra from CID (or HCD) and ECD (or ETD) fragmentation because of the complementarity between them. We present a new de novo sequencing method for such paired spectra named NovoPair, and compare its performance to another successful method named pNovo+. NovoPair extends our previously proposed graph model to suit paired spectra, and considers different ion types in the two spectra to extract more information. The results show that NovoPair outperforms pNovo+ in terms of full length peptide sequencing accuracy on three pairs of experimental datasets, with the accuracy increasing up to 13.6% compared to pNovo+.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".