{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Xin L"],"funding":["Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada"],"pagination":["3108"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9174175"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["13(1)"],"pubmed_abstract":["Integrating data-dependent acquisition (DDA) and data-independent acquisition (DIA) approaches can enable highly sensitive mass spectrometry, especially for imunnopeptidomics applications. Here we report a streamlined platform for both DDA and DIA data analysis. The platform integrates deep learning-based solutions of spectral library search, database search, and de novo sequencing under a unified framework, which not only boosts the sensitivity but also accurately controls the specificity of peptide identification. Our platform identifies 5-30% more peptide precursors than other state-of-the-art systems on multiple benchmark datasets. When evaluated on immunopeptidomics datasets, we identify 1.7-4.1 and 1.4-2.2 times more peptides from DDA and DIA data, respectively, than previously repor"],"journal":["Nature communications"],"pubmed_title":["A streamlined platform for analyzing tera-scale DDA and DIA mass spectrometry data enables highly sensitive immunopeptidomics."],"pmcid":["PMC9174175"],"funding_grant_id":["OGP0046506"],"pubmed_authors":["Xin L","Morse B","Qiao R","Pan S","He X","Tran H","Rabinoviz S","Bian H","Chen X","Shan B","Li M"],"additional_accession":[]},"is_claimable":false,"name":"A streamlined platform for analyzing tera-scale DDA and DIA mass spectrometry data enables highly sensitive immunopeptidomics.","description":"Integrating data-dependent acquisition (DDA) and data-independent acquisition (DIA) approaches can enable highly sensitive mass spectrometry, especially for imunnopeptidomics applications. Here we report a streamlined platform for both DDA and DIA data analysis. The platform integrates deep learning-based solutions of spectral library search, database search, and de novo sequencing under a unified framework, which not only boosts the sensitivity but also accurately controls the specificity of peptide identification. Our platform identifies 5-30% more peptide precursors than other state-of-the-art systems on multiple benchmark datasets. When evaluated on immunopeptidomics datasets, we identify 1.7-4.1 and 1.4-2.2 times more peptides from DDA and DIA data, respectively, than previously repor","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Jun","modification":"2025-04-04T03:05:53.913Z","creation":"2025-02-19T05:06:21.279Z"},"accession":"S-EPMC9174175","cross_references":{"pubmed":["35672356"],"doi":["10.1038/s41467-022-30867-7"]}}