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Removing the hidden data dependency of DIA with predicted spectral libraries

Bart Van Puyvelde (UGent) , Sander Willems (UGent) , Ralf Gabriels (UGent) , Simon Daled (UGent) , Laura De Clerck (UGent) , Sofie Vande Casteele (UGent) , An Staes (UGent) , Francis Impens (UGent) , Dieter Deforce (UGent) , Lennart Martens (UGent) , et al.
(2020) PROTEOMICS. 20(3-4).
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Abstract
Data‐independent acquisition (DIA) generates comprehensive yet complex mass spectrometric data, which imposes the use of data‐dependent acquisition (DDA) libraries for deep peptide‐centric detection. Here, it is shown that DIA can be redeemed from this dependency by combining predicted fragment intensities and retention times with narrow window DIA. This eliminates variation in library building and omits stochastic sampling, finally making the DIA workflow fully deterministic. Especially for clinical proteomics, this has the potential to facilitate inter‐laboratory comparison.
Keywords
bioinformatics, data-independent acquisition, label-free quantification, peptide-centric, PEPTIDE IDENTIFICATION, ACQUISITION

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Citation

Please use this url to cite or link to this publication:

MLA
Van Puyvelde, Bart, et al. “Removing the Hidden Data Dependency of DIA with Predicted Spectral Libraries.” PROTEOMICS, vol. 20, no. 3–4, 2020.
APA
Van Puyvelde, B., Willems, S., Gabriels, R., Daled, S., De Clerck, L., Vande Casteele, S., … Dhaenens, M. (2020). Removing the hidden data dependency of DIA with predicted spectral libraries. PROTEOMICS, 20(3–4).
Chicago author-date
Van Puyvelde, Bart, Sander Willems, Ralf Gabriels, Simon Daled, Laura De Clerck, Sofie Vande Casteele, An Staes, et al. 2020. “Removing the Hidden Data Dependency of DIA with Predicted Spectral Libraries.” PROTEOMICS 20 (3–4).
Chicago author-date (all authors)
Van Puyvelde, Bart, Sander Willems, Ralf Gabriels, Simon Daled, Laura De Clerck, Sofie Vande Casteele, An Staes, Francis Impens, Dieter Deforce, Lennart Martens, Sven Degroeve, and Maarten Dhaenens. 2020. “Removing the Hidden Data Dependency of DIA with Predicted Spectral Libraries.” PROTEOMICS 20 (3–4).
Vancouver
1.
Van Puyvelde B, Willems S, Gabriels R, Daled S, De Clerck L, Vande Casteele S, et al. Removing the hidden data dependency of DIA with predicted spectral libraries. PROTEOMICS. 2020;20(3–4).
IEEE
[1]
B. Van Puyvelde et al., “Removing the hidden data dependency of DIA with predicted spectral libraries,” PROTEOMICS, vol. 20, no. 3–4, 2020.
@article{8647926,
  abstract     = {Data‐independent acquisition (DIA) generates comprehensive yet complex mass spectrometric data, which imposes the use of data‐dependent acquisition (DDA) libraries for deep peptide‐centric detection. Here, it is shown that DIA can be redeemed from this dependency by combining predicted fragment intensities and retention times with narrow window DIA. This eliminates variation in library building and omits stochastic sampling, finally making the DIA workflow fully deterministic. Especially for clinical proteomics, this has the potential to facilitate inter‐laboratory comparison.},
  articleno    = {1900306},
  author       = {Van Puyvelde, Bart and Willems, Sander and Gabriels, Ralf and Daled, Simon and De Clerck, Laura and Vande Casteele, Sofie and Staes, An and Impens, Francis and Deforce, Dieter and Martens, Lennart and Degroeve, Sven and Dhaenens, Maarten},
  issn         = {1615-9853},
  journal      = {PROTEOMICS},
  keywords     = {bioinformatics,data-independent acquisition,label-free quantification,peptide-centric,PEPTIDE IDENTIFICATION,ACQUISITION},
  language     = {eng},
  number       = {3-4},
  pages        = {4},
  title        = {Removing the hidden data dependency of DIA with predicted spectral libraries},
  url          = {http://dx.doi.org/10.1002/pmic.201900306},
  volume       = {20},
  year         = {2020},
}
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