LooMS: a novel peptide identification tools for data independent acquisition

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. HighlightsO_LILooMS 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. C_LIO_LILooMS 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. C_LIO_LIA 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. C_LI