Untargeted, spectral library-free analysis of data-independent acquisition proteomics data generated using Orbitrap mass spectrometers

• Published in: Proteomics (Weinheim) Vol. 16; no. 15-16; pp. 2257 - 2271
• Main Authors: Tsou, Chih-Chiang, Tsai, Chia-Feng, Teo, Guo Ci, Chen, Yu-Ju, Nesvizhskii, Alexey I.
• Format: Journal Article
• Language: English
• Published: Germany Blackwell Publishing Ltd 01.08.2016; Wiley Subscription Services, Inc
• Subjects: Bioinformatics; Computational Biology - methods; Data-independent acquisition; HEK293 Cells; HeLa Cells; Humans; Mass Spectrometry - methods; Peptides; Proteomics; Proteomics - methods; Spectrometers; Sports officiating; Bioinformatics; Data-independent acquisition
• ISSN: 1615-9853; 1615-9861
• DOI: 10.1002/pmic.201500526

We describe an improved version of the data‐independent acquisition (DIA) computational analysis tool DIA‐Umpire, and show that it enables highly sensitive, untargeted, and direct (spectral library‐free) analysis of DIA data obtained using the Orbitrap family of mass spectrometers. DIA‐Umpire v2 implements an improved feature detection algorithm with two additional filters based on the isotope pattern and fractional peptide mass analysis. The targeted re‐extraction step of DIA‐Umpire is updated with an improved scoring function and a more robust, semiparametric mixture modeling of the resulting scores for computing posterior probabilities of correct peptide identification in a targeted setting. Using two publicly available Q Exactive DIA datasets generated using HEK‐293 cells and human liver microtissues, we demonstrate that DIA‐Umpire can identify similar number of peptide ions, but with better identification reproducibility between replicates and samples, as with conventional data‐dependent acquisition. We further demonstrate the utility of DIA‐Umpire using a series of Orbitrap Fusion DIA experiments with HeLa cell lysates profiled using conventional data‐dependent acquisition and using DIA with different isolation window widths.