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      PECAN: Library Free Peptide Detection for Data-Independent Acquisition Tandem Mass Spectrometry Data

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          Abstract

          In mass spectrometry-based shogun proteomics, data-independent acquisition (DIA) is an emerging technique for unbiased and reproducible measurement of protein mixtures. Without targeting a specific precursor ion, DIA MS/MS spectra are often highly multiplexed, containing product ions from multiple co-fragmenting precursors. Thus, detecting peptides directly from DIA data is challenging; most DIA data analyses require spectral libraries. Here we present a new library-free, peptide-centric tool PECAN that detects peptides directly from DIA data. PECAN reports evidence of detection based on product ion scoring, enabling detection of low abundance analytes with poor precursor ion signal. We benchmarked PECAN with chromatographic peak picking accuracy and peptide detection capability. We further validated PECAN detection with data-dependent acquisition and targeted analyses. Last, we used PECAN to build a library from DIA data and to query sequence variants. Together, these results show that PECAN detects peptides robustly and accurately from DIA data without using a library.

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          Most cited references18

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          A probability-based approach for high-throughput protein phosphorylation analysis and site localization.

          Data analysis and interpretation remain major logistical challenges when attempting to identify large numbers of protein phosphorylation sites by nanoscale reverse-phase liquid chromatography/tandem mass spectrometry (LC-MS/MS) (Supplementary Figure 1 online). In this report we address challenges that are often only addressable by laborious manual validation, including data set error, data set sensitivity and phosphorylation site localization. We provide a large-scale phosphorylation data set with a measured error rate as determined by the target-decoy approach, we demonstrate an approach to maximize data set sensitivity by efficiently distracting incorrect peptide spectral matches (PSMs), and we present a probability-based score, the Ascore, that measures the probability of correct phosphorylation site localization based on the presence and intensity of site-determining ions in MS/MS spectra. We applied our methods in a fully automated fashion to nocodazole-arrested HeLa cell lysate where we identified 1,761 nonredundant phosphorylation sites from 491 proteins with a peptide false-positive rate of 1.3%.
            • Record: found
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            OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data.

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

              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.

                Author and article information

                Journal
                101215604
                32338
                Nat Methods
                Nat. Methods
                Nature methods
                1548-7091
                1548-7105
                22 July 2017
                07 August 2017
                September 2017
                07 February 2018
                : 14
                : 9
                : 903-908
                Affiliations
                [1 ]Department of Genome Sciences, University of Washington, Seattle, Washington, USA
                [2 ]Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, USA
                [3 ]Department of Computer Science and Engineering, University of Washington, Seattle, Washington, USA
                Author notes
                Correspondence should be addressed to M. J. M. ( maccoss@ 123456uw.edu )
                Article
                NIHMS893716
                10.1038/nmeth.4390
                5578911
                28783153
                34bdd989-b67d-42bd-a345-2d310a745d2e

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                Life sciences
                Life sciences

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