DIA-Umpire: comprehensive computational framework for data independent acquisition proteomics

By Chih-Chiang Tsou, Dmitry Avtonomov, B. Larsen, M. Tucholska, Hyungwon Choi, A. Gingras, Alexey I. Nesvizhskii. Nature Methods, 2015

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As a result of recent improvements in mass spectrometry (MS), there is increased interest in data-independent acquisition (DIA) strategies in which all peptides are systematically fragmented using wide mass-isolation windows ('multiplex fragmentation'). DIA-Umpire (http://diaumpire.sourceforge.net/), a comprehensive computational workflow and open-source software for DIA data, detects precursor and fragment chromatographic features and assembles them into pseudo–tandem MS spectra. These spectra can be identified with conventional database-searching and protein-inference tools, allowing sensitive, untargeted analysis of DIA data without the need for a spectral library. Quantification is done with both precursor- and fragment-ion intensities. Furthermore, DIA-Umpire enables targeted extraction of quantitative information based on peptides initially identified in only a subset of the samples, resulting in more consistent quantification across multiple samples. We demonstrated the performance of the method with control samples of varying complexity and publicly available glycoproteomics and affinity purification–MS data.

Subfields: Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/a3db0ae40d2bbee920b5ca209be609bc62bab689

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Citations in the corpus

The Skyline Ecosystem: Informatics for Quantitative Mass Spectrometry Proteomics (2017, methodology): “…of spectral library retention time information for peptide spectrum matches (IDs) within the files being analyzed (usually for DDA, PRM or DIA - with initial processing by tools like DIA-Umpire (Tsou et al 2015)), Skyline takes only the 20 most intense peaks for each transition from CRAWDAD.”
IonQuant Enables Accurate and Sensitive Label-Free Quantification With FDR-Controlled Match-Between-Runs (2020, methodology): “We also implement a new protein abundance calculation module in IonQuant based on the MaxLFQ strategy (15), improving upon our previously described top-N approach (21, 22).”
MSFragger: ultrafast and comprehensive peptide identification in shotgun proteomics (2017, background): “Furthermore, we attempted to address the weakness of the open-search strategy for C-terminal modifications (as y-ions are the most abundant and commonly observed in collision-induced and higher-energy collisional dissociation) by inserting complementary ions 6,31 .”
Data‐independent acquisition‐based SWATH‐MS for quantitative proteomics: a tutorial (2018, unknown): “…et al , 2016) using a ground truth data set has demonstrated remarkable agreement for the qualitative and quantitative results produced by those peptide-centric scoring tools as well as by the spectrum-centric scoring algorithm DIA-Umpire (Tsou et al , 2015) that was also included in the study.”