Generating high-quality libraries for DIA-MS with empirically-corrected peptide predictions
Brian C. Searle, Kristian E. Swearingen, Christopher A. Barnes, Tobias Schmidt, Siegfried Gessulat, Bernhard Kuster and Mathias Wilhelm
Received Date: 21st August 19
Data-independent acquisition approaches typically rely on sample-specific spectrum libraries requiring offline fractionation and tens to hundreds of injections. We demonstrate a new 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 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.
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This is an abstract of a preprint hosted on an independent third party site. It has not been peer reviewed but is currently under consideration at Nature Communications.