De Novo sequencing-assisted homology search for DIA data analysis enables low abundance peptide variants discovery
Bibliographic record
Abstract
Abstract Data-independent acquisition mass spectrometry (DIA-MS) has emerged as a powerful approach for comprehensive proteome profiling. Spectral library search and library-free search are the two major approaches for DIA data analysis. The spectral library search requires high-quality spectral libraries derived from the search results of data-dependent acquisition (DDA) experiments, while library-free approaches rely on prediction models to generate in silico libraries. Both methodologies constrain the search space to the peptide list in the database, limiting the discovery of variant peptides arising from genetic variations or mutations. We present a novel computational method DIAVariant designed to identify peptide sequence variants directly and solely from complex DIA spectra while rigorously controlling the false discovery rate. Our experimental results demonstrate that DIAVariant successfully identifies sequence variants previously detected through proteogenomic approaches, while maintaining high specificity across multiple datasets. When integrated with existing DIA database search solutions, our approach constitutes a comprehensive analytical workflow capable of identifying peptides both represented within reference protein databases and those arising from sequence variations not captured in standard databases.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".