LooMS: a novel peptide identification tools for data independent acquisition
Bibliographic record
Abstract
Abstract Advancements in mass spectrometry (MS)-based proteomics have produced large-scale datasets, necessitating the development of effective tools for peptide identification. Here, we present LooMS, a novel tool specifically designed for identifying peptides in data-independent acquisition (DIA) datasets. LooMS employs an innovative approach, using an unbiased generation strategy for positive and negative samples, which reduces the risk of overfitting in peptide identification with deep learning models. Additionally, LooMS addresses various critical aspects of DIA mass spectra data analysis, constructing a comprehensive set of 43 features for training deep learning models, which cover different stages of DIA data analysis. Notably, we propose a false discovery rate (FDR) control strategy that integrates results from both LooMS and DiaNN, another leading peptide identification tool. Our results demonstrate significant improvements in peptide identification performance, with enhancements of 40.61% and 26.60% at the unique peptide level for human and mouse datasets, respectively. Highlights LooMS is a novel tool for identifying peptides in DIA datasets that adopts an innovative unbiased positive and negative sample generation strategy, which aim to avoid the overfilling in peptide identification with deep learning model. LooMS comprehensively considers various aspects of data analysis for DIA mass spectra and builds 43 useful features for training deep learning models, which involve different stages of DIA data analysis. A FDR control strategy for integration of results from both LooMS and DiaNN is proposed, which can significantly improve the identification of peptides due to the differences in the features involved in peptide detection during their respective design.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".