DDIA: data dependent-independent acquisition proteomics - DDA and DIA in a single LC-MS/MS run

Data dependent acquisition (DDA) and data independent acquisition (DIA) are traditionally separate experimental paradigms in bottom-up proteomics. In this work, we developed a strategy combining the two experimental methods into a single LC-MS/MS run. We call the novel strategy, data dependent-independent acquisition proteomics, or DDIA for short. Peptides identified by conventional and robust DDA identification workflow provide useful information for interrogation of DIA scans. Deep learning based LC-MS/MS property prediction tools, developed previously can be used repeatedly to produce spectral libraries facilitating DIA scan extraction. A complete DDIA data processing pipeline, including modules for iRT vs RT calibration curve generation, DIA extraction classifier training, FDR control has been developed. A key advantage of the DDIA method is that it requires minimal information for processing its data. ![Figure][1] * DDA : data dependent acquisition DDIA : data dependent-independent acquisition DIA : data independent acquisition FDR : false discovery rate iRT : indexed retention time RT : retention time QDA : quadratic discriminant analysis ROC : receiver operating characteristic [1]: pending:yes