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. Useful information for interrogation of DIA scans was obtained from peptides identified by the conventional and robust DDA identification workflow. Deep learning based LC-MS/MS property prediction tools previously developed were repeatedly used 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 was developed. A key advantage of the DDIA method is that it requires minimal information for processing its data.