Data Independent Acquisition offers the following advantages over classic Data Dependent Acquisition:
- Theoretical coverage of the whole sample proteome (because all peptides are fragmented). It thus offers potentially deeper coverage of the data, reducing the need for offline fractionation.
- Reliance on a library for identifications means DIA does not suffer from the stochastic identifications of peptides that DDA suffers from. Cross-sample comparisons are thus made much easier.
- DIA data can be re-probed indefinitely, as peptide libraries are expanded, to improve the quality of the data. If the first search did not output as many peptides as you hoped it would, but you know the sample is complex, you may be able to get much better data after improving your library.
- DIA does not require labelling for good cross-sample comparability. Thus, where SILAC, iTRAQ or TMT labelling really work well within the boundaries of their maximum multiplicity (currently up to 3, 8 and 11, respectively), there is no limit to the number of samples that can be compared in an unbiased way with DIA. (In addition, DIA is theoretically incompatible with labelling methods which require single precursor isolation, such as TMT and iTRAQ).
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