Data Dependent–Independent
Acquisition (DDIA)
Proteomics
Posted on 2020-06-15 - 19:39
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 from DDA scans by a 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 the modules for iRT vs RT
calibration curve generation, DIA extraction classifier training,
and false discovery rate control, has been developed. Compared to
another spectral library-free method, DIA-Umpire, the DDIA method
produced a similar number of peptide identifications, but nearly twice
as many protein group identifications. The primary advantage of the
DDIA method is that it requires minimal information for processing
its data.
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Guan, Shenheng; Taylor, Paul P.; Han, Ziwei; Moran, Michael F.; Ma, Bin (2020). Data Dependent–Independent
Acquisition (DDIA)
Proteomics. ACS Publications. Collection. https://doi.org/10.1021/acs.jproteome.0c00186
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AUTHORS (5)
SG
Shenheng Guan
PT
Paul P. Taylor
ZH
Ziwei Han
MM
Michael F. Moran
BM
Bin Ma