Literature DB >> 30705124

DIAlignR Provides Precise Retention Time Alignment Across Distant Runs in DIA and Targeted Proteomics.

Shubham Gupta1,2, Sara Ahadi3, Wenyu Zhou3, Hannes Röst4,2.   

Abstract

Sequential Windowed Acquisition of All Theoretical Fragment Ion Mass Spectra (SWATH-MS) is widely used for proteomics analysis given its high throughput and reproducibility, but ensuring consistent quantification of analytes across large-scale studies of heterogeneous samples such as human plasma remains challenging. Heterogeneity in large-scale studies can be caused by large time intervals between data acquisition, acquisition by different operators or instruments, and intermittent repair or replacement of parts, such as the liquid chromatography column, all of which affect retention time (RT) reproducibility and, successively, performance of SWATH-MS data analysis. Here, we present a novel algorithm for RT alignment of SWATH-MS data based on direct alignment of raw MS2 chromatograms using a hybrid dynamic programming approach. The algorithm does not impose a chronological order of elution and allows for alignment of elution-order-swapped peaks. Furthermore, allowing RT mapping in a certain window around a coarse global fit makes it robust against noise. On a manually validated dataset, this strategy outperformed the current state-of-the-art approaches. In addition, on real-world clinical data, our approach outperformed global alignment methods by mapping 98% of peaks compared with 67% cumulatively. DIAlignR reduced alignment error up to 30-fold for extremely distant runs. The robustness of technical parameters used in this pairwise alignment strategy is also demonstrated. The source code is released under the BSD license at https://github.com/Roestlab/DIAlignR.
© 2019 Gupta et al.

Entities:  

Keywords:  Bioinformatics software; Chromatography; Clinical data; Data-independent acquisition; Plasma or serum analysis; Retention time alignment; Targeted mass spectrometry

Mesh:

Substances:

Year:  2019        PMID: 30705124      PMCID: PMC6442363          DOI: 10.1074/mcp.TIR118.001132

Source DB:  PubMed          Journal:  Mol Cell Proteomics        ISSN: 1535-9476            Impact factor:   5.911


  29 in total

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Journal:  J Proteome Res       Date:  2010-12-13       Impact factor: 4.466

3.  ChromAlign: A two-step algorithmic procedure for time alignment of three-dimensional LC-MS chromatographic surfaces.

Authors:  Rovshan G Sadygov; Fernando Martin Maroto; Andreas F R Hühmer
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4.  Quantitative proteomics: challenges and opportunities in basic and applied research.

Authors:  Olga T Schubert; Hannes L Röst; Ben C Collins; George Rosenberger; Ruedi Aebersold
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Authors:  Hannes L Röst; George Rosenberger; Pedro Navarro; Ludovic Gillet; Saša M Miladinović; Olga T Schubert; Witold Wolski; Ben C Collins; Johan Malmström; Lars Malmström; Ruedi Aebersold
Journal:  Nat Biotechnol       Date:  2014-03       Impact factor: 54.908

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

Authors:  Chih-Chiang Tsou; Dmitry Avtonomov; Brett Larsen; Monika Tucholska; Hyungwon Choi; Anne-Claude Gingras; Alexey I Nesvizhskii
Journal:  Nat Methods       Date:  2015-01-19       Impact factor: 28.547

7.  MathDAMP: a package for differential analysis of metabolite profiles.

Authors:  Richard Baran; Hayataro Kochi; Natsumi Saito; Makoto Suematsu; Tomoyoshi Soga; Takaaki Nishioka; Martin Robert; Masaru Tomita
Journal:  BMC Bioinformatics       Date:  2006-12-13       Impact factor: 3.169

8.  Statistical control of peptide and protein error rates in large-scale targeted data-independent acquisition analyses.

Authors:  George Rosenberger; Isabell Bludau; Uwe Schmitt; Moritz Heusel; Christie L Hunter; Yansheng Liu; Michael J MacCoss; Brendan X MacLean; Alexey I Nesvizhskii; Patrick G A Pedrioli; Lukas Reiter; Hannes L Röst; Stephen Tate; Ying S Ting; Ben C Collins; Ruedi Aebersold
Journal:  Nat Methods       Date:  2017-08-21       Impact factor: 28.547

9.  ChromA: signal-based retention time alignment for chromatography-mass spectrometry data.

Authors:  Nils Hoffmann; Jens Stoye
Journal:  Bioinformatics       Date:  2009-06-08       Impact factor: 6.937

10.  High-precision iRT prediction in the targeted analysis of data-independent acquisition and its impact on identification and quantitation.

Authors:  Roland Bruderer; Oliver M Bernhardt; Tejas Gandhi; Lukas Reiter
Journal:  Proteomics       Date:  2016-06-28       Impact factor: 3.984

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1.  Simultaneous Improvement in the Precision, Accuracy, and Robustness of Label-free Proteome Quantification by Optimizing Data Manipulation Chains.

Authors:  Jing Tang; Jianbo Fu; Yunxia Wang; Yongchao Luo; Qingxia Yang; Bo Li; Gao Tu; Jiajun Hong; Xuejiao Cui; Yuzong Chen; Lixia Yao; Weiwei Xue; Feng Zhu
Journal:  Mol Cell Proteomics       Date:  2019-05-16       Impact factor: 5.911

2.  Acquiring and Analyzing Data Independent Acquisition Proteomics Experiments without Spectrum Libraries.

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Journal:  Mol Cell Proteomics       Date:  2020-04-20       Impact factor: 5.911

3.  DEIMoS: An Open-Source Tool for Processing High-Dimensional Mass Spectrometry Data.

Authors:  Sean M Colby; Christine H Chang; Jessica L Bade; Jamie R Nunez; Madison R Blumer; Daniel J Orton; Kent J Bloodsworth; Ernesto S Nakayasu; Richard D Smith; Yehia M Ibrahim; Ryan S Renslow; Thomas O Metz
Journal:  Anal Chem       Date:  2022-04-17       Impact factor: 8.008

4.  Proceedings of the EuBIC Winter School 2019.

Authors:  Dominik Kopczynski; Wout Bittremieux; David Bouyssié; Viktoria Dorfer; Marie Locard-Paulet; Bart Van Puyvelde; Veit Schwämmle; Alessio Soggiu; Sander Willems; Julian Uszkoreit
Journal:  EuPA Open Proteom       Date:  2019-08-17
  4 in total

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