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Generating high-quality libraries for DIA-MS with empirically-corrected peptide predictions

By Brian C Searle, Kristian E. Swearingen, Christopher A Barnes, Tobias Schmidt, Siegfried Gessulat, Bernhard Kuster, Mathias Wilhelm

Posted 27 Jun 2019
bioRxiv DOI: 10.1101/682245 (published DOI: 10.1038/s41467-020-15346-1)

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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