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Orbitrap_SciLib
Reputable Mentor II
Reputable Mentor II
Siegfried Gessulat1, Tobias Schmidt2, Michael Graber1, Florian Seefried1, Carmen Paschke3, Kai Fritzemeier3, Dave Horn4, Bernard Delanghe3, Daniel Zolg2, Mathias Wilhelm2, Bernhard Kuster2, Martin Frejno1
ASMS 2020
Purpose: Improve the separation of target and decoy identifications in proteomics data sets in order to boost the confidence in search results. Methods: Several datasets were analysedusing a beta version of Thermo Scientific™ Proteome Discoverer™ 2.5 software with SequestHTand the new Prosit-derived (1) Rescoring node by MSAID. Results: Deep-learning-based prediction of fragment ion intensities enables the addition of intensity-based scores to identification workflows with SequestHT, which increase the confidence in search results.


1msAid GmbH, Garching, Germany; 2Technical University of Munich, Freising, Germany; 3Thermo Fisher Scientific (Bremen) GmbH, Bremen, Germany; 4Thermo Fisher, San Jose, CA
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‎10-15-2021 08:19 AM
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