Repeat-Preserving Decoy Database for False Discovery Rate Estimation in Peptide Identification
Bibliographic record
Abstract
The sequence database searching method is widely used in proteomics for peptide identification. To control the false discovery rate (FDR) of the searching results, the target-decoy method generates and searches a decoy database together with the target database. A known problem is that the target protein sequence database may contain numerous repeated peptides. The structures of these repeats are not preserved by most existing decoy generation algorithms. Previous studies suggest that such discrepancy between the target and decoy databases may lead to an inaccurate FDR estimation. Based on the de Bruijn graph model, we propose a new repeat-preserving algorithm to generate decoy databases. We prove that this algorithm preserves the structures of the repeats in the target database to a great extent. The de Bruijn method has been compared with a few other commonly used methods and demonstrated superior FDR estimation accuracy and an improved number of peptide identification.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".