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DIAlignR Provides Precise Retention Time Alignment Across Distant Runs in DIA and Targeted Proteomics.
Gupta, Shubham; Ahadi, Sara; Zhou, Wenyu; Röst, Hannes.
Afiliação
  • Gupta S; From the ‡Department of Molecular Genetics, University of Toronto, Toronto, ON M5G 1A8, Canada;; The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1, Canada.
  • Ahadi S; ¶Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305.
  • Zhou W; ¶Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305.
  • Röst H; From the ‡Department of Molecular Genetics, University of Toronto, Toronto, ON M5G 1A8, Canada;; The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1, Canada;. Electronic address: hannes.rost@utoronto.ca.
Mol Cell Proteomics ; 18(4): 806-817, 2019 04.
Article em En | MEDLINE | ID: mdl-30705124
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.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Software / Alinhamento de Sequência / Proteômica Limite: Humans Idioma: En Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Software / Alinhamento de Sequência / Proteômica Limite: Humans Idioma: En Ano de publicação: 2019 Tipo de documento: Article