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

Abstract

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.\n\nGRAPHIC ABSTRACT\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=127 SRC=\"FIGDIR/small/802231v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (16K):\norg.highwire.dtl.DTLVardef@a631c6org.highwire.dtl.DTLVardef@10dc3a0org.highwire.dtl.DTLVardef@a65061org.highwire.dtl.DTLVardef@e72627_HPS_FORMAT_FIGEXP M_FIG C_FIG