Data-independent acquisition approaches typically rely on experiment-specific spectrum libraries, requiring offline fractionation and tens to hundreds of injections. We demonstrate a library generation workflow that leverages fragmentation and retention time prediction to build libraries containing every peptide in a proteome, and then refines those libraries with empirical data. Our method specifically enables rapid, experiment-specific library generation for non-model organisms, which we demonstrate using the malaria parasite Plasmodium falciparum, and non-canonical databases, which we show by detecting missense variants in HeLa.
Institute for Systems Biology
Searle, Brian C; Swearingen, Kristian E; Barnes, Christopher A; Schmidt, Tobias; Gessulat, Siegfried; Küster, Bernhard; and Wilhelm, Mathias, "Generating high quality libraries for DIA MS with empirically corrected peptide predictions." (2020). Articles, Abstracts, and Reports. 2915.