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DIAlignR provides precise retention time alignment across distant runs in DIA and targeted proteomics

View ORCID ProfileShubham Gupta, Sara Ahadi, Wenyu Zhou, View ORCID ProfileHannes Röst
doi: https://doi.org/10.1101/438309
Shubham Gupta
1Department of Molecular Genetics, University of Toronto, Toronto, ON M5G 1A8, Canada
2Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1, Canada
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Sara Ahadi
3Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
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Wenyu Zhou
3Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
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Hannes Röst
1Department of Molecular Genetics, University of Toronto, Toronto, ON M5G 1A8, Canada
2Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1, Canada
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  • For correspondence: hannes.rost@utoronto.ca
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Abstract

SWATH-MS has been 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, 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 retention time 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 coarse global fit makes it robust against noise. On a manually validated dataset, this strategy outperforms the current state-of-the-art approaches. In addition, on a real-world clinical data, our approach outperforms global alignment methods by mapping 98% of peaks compared to 67% cumulatively and DIAlignR can reduce alignment error up to 30-fold for extremely distant runs. The robustness of technical parameters used in this pairwise alignment strategy has also been demonstrated. The source code is released under the BSD license at https://github.com/Roestlab/DIAlignR.

AUC
Area Under the Curve
DIA
Data-independent acquisition
LC
Liquid chromatography
LOESS
Local weighted regression
RSE
Residual Standard Error
RT
Retention time
XIC
Extracted ion chromatograms

Data Availability: Raw chromatograms and features extracted by OpenSWATH are available on PeptideAtlas.

Servername: ftp.peptideatlas.org

Username: PASS01280

Password: KQ2592b

Footnotes

  • Main manuscript and Supplemental text are updated. Effect of the number of transitions on RT-alignment is included. Titles of Supplementary Figures and Tables are added. Supplementary Figure 1a and Supplementary Figure 10a are added. Figure 1c and Supplementary Figure 10b are corrected. Table 1, Supplemental Table 4 and Supplemental Table 15 are added. R-Package name is changed to DIAlignR.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted December 26, 2018.
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DIAlignR provides precise retention time alignment across distant runs in DIA and targeted proteomics
Shubham Gupta, Sara Ahadi, Wenyu Zhou, Hannes Röst
bioRxiv 438309; doi: https://doi.org/10.1101/438309
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DIAlignR provides precise retention time alignment across distant runs in DIA and targeted proteomics
Shubham Gupta, Sara Ahadi, Wenyu Zhou, Hannes Röst
bioRxiv 438309; doi: https://doi.org/10.1101/438309

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