{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Zong Y"],"funding":["Ministry of Science and Technology of the People’s Republic of China","National Natural Science Foundation of China"],"pagination":["2269"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10119288"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["14(1)"],"pubmed_abstract":["Protein phosphorylation is a post-translational modification crucial for many cellular processes and protein functions. Accurate identification and quantification of protein phosphosites at the proteome-wide level are challenging, not least because efficient tools for protein phosphosite false localization rate (FLR) control are lacking. Here, we propose DeepFLR, a deep learning-based framework for controlling the FLR in phosphoproteomics. DeepFLR includes a phosphopeptide tandem mass spectrum (MS/MS) prediction module based on deep learning and an FLR assessment module based on a target-decoy approach. DeepFLR improves the accuracy of phosphopeptide MS/MS prediction compared to existing tools. Furthermore, DeepFLR estimates FLR accurately for both synthetic and biological datasets, and lo"],"journal":["Nature communications"],"pubmed_title":["DeepFLR facilitates false localization rate control in phosphoproteomics."],"pmcid":["PMC10119288"],"funding_grant_id":["21934001","22022401","22074022","2022YFC2704300"],"pubmed_authors":["Yang Y","Zong Y","Shen C","Zhao D","Wang X","Wang Y","Qiao L"],"additional_accession":[]},"is_claimable":false,"name":"DeepFLR facilitates false localization rate control in phosphoproteomics.","description":"Protein phosphorylation is a post-translational modification crucial for many cellular processes and protein functions. Accurate identification and quantification of protein phosphosites at the proteome-wide level are challenging, not least because efficient tools for protein phosphosite false localization rate (FLR) control are lacking. Here, we propose DeepFLR, a deep learning-based framework for controlling the FLR in phosphoproteomics. DeepFLR includes a phosphopeptide tandem mass spectrum (MS/MS) prediction module based on deep learning and an FLR assessment module based on a target-decoy approach. DeepFLR improves the accuracy of phosphopeptide MS/MS prediction compared to existing tools. Furthermore, DeepFLR estimates FLR accurately for both synthetic and biological datasets, and lo","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Apr","modification":"2026-05-28T16:50:18.973Z","creation":"2025-04-04T03:42:36.879Z"},"accession":"S-EPMC10119288","cross_references":{"pubmed":["37080984"],"doi":["10.1038/s41467-023-38035-1"]}}