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Automated Workflow for Large-Scale Selected Reaction Monitoring Experiments

Malmstrom, Lars; Malmström, Johan LU ; Selevsek, Nathalie; Rosenberger, George and Aebersold, Ruedi (2012) In Journal of Proteome Research 11(3). p.1644-1653
Abstract
Targeted proteomics allows researchers to study proteins of interest without being drowned in data from other, less interesting proteins or from redundant or uninformative peptides. While the technique is mostly used for smaller, focused studies, there are several reasons to conduct larger targeted experiments. Automated, highly robust software becomes more important in such experiments. In addition, larger experiments are carried out over longer periods of time, requiring strategies to handle the sometimes large shift in retention time often observed. We present a complete proof-of-principle software stack that automates most aspects of selected reaction monitoring workflows, a targeted proteomics technology. The soft-ware allows... (More)
Targeted proteomics allows researchers to study proteins of interest without being drowned in data from other, less interesting proteins or from redundant or uninformative peptides. While the technique is mostly used for smaller, focused studies, there are several reasons to conduct larger targeted experiments. Automated, highly robust software becomes more important in such experiments. In addition, larger experiments are carried out over longer periods of time, requiring strategies to handle the sometimes large shift in retention time often observed. We present a complete proof-of-principle software stack that automates most aspects of selected reaction monitoring workflows, a targeted proteomics technology. The soft-ware allows experiments to be easily designed and carried out. The steps automated are the generation of assays, generation of mass spectrometry driver files and methods files, and the import. and analysis of the data. All data are normalized to a common retention time scale, the data are then scored using a novel score model, and the error is subsequently estimated. We also show that selected reaction monitoring can be used for label-free quantification. All data generated are stored in a relational database, and the growing resource further facilitates the design of new experiments. We apply the technology to a large-scale experiment studying how Streptococcus pyogenes remodels its proteome under stimulation of human plasma. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
discrete wavelet transform, selected reaction monitoring, mass spectrometry, targeted proteomics
in
Journal of Proteome Research
volume
11
issue
3
pages
1644 - 1653
publisher
The American Chemical Society
external identifiers
  • wos:000300916200019
  • scopus:84857881072
ISSN
1535-3893
DOI
10.1021/pr200844d
language
English
LU publication?
yes
id
3baa085d-cb93-4c8b-a779-dc87507cf6f6 (old id 2517274)
date added to LUP
2012-05-09 09:44:23
date last changed
2017-04-09 03:02:54
@article{3baa085d-cb93-4c8b-a779-dc87507cf6f6,
  abstract     = {Targeted proteomics allows researchers to study proteins of interest without being drowned in data from other, less interesting proteins or from redundant or uninformative peptides. While the technique is mostly used for smaller, focused studies, there are several reasons to conduct larger targeted experiments. Automated, highly robust software becomes more important in such experiments. In addition, larger experiments are carried out over longer periods of time, requiring strategies to handle the sometimes large shift in retention time often observed. We present a complete proof-of-principle software stack that automates most aspects of selected reaction monitoring workflows, a targeted proteomics technology. The soft-ware allows experiments to be easily designed and carried out. The steps automated are the generation of assays, generation of mass spectrometry driver files and methods files, and the import. and analysis of the data. All data are normalized to a common retention time scale, the data are then scored using a novel score model, and the error is subsequently estimated. We also show that selected reaction monitoring can be used for label-free quantification. All data generated are stored in a relational database, and the growing resource further facilitates the design of new experiments. We apply the technology to a large-scale experiment studying how Streptococcus pyogenes remodels its proteome under stimulation of human plasma.},
  author       = {Malmstrom, Lars and Malmström, Johan and Selevsek, Nathalie and Rosenberger, George and Aebersold, Ruedi},
  issn         = {1535-3893},
  keyword      = {discrete wavelet transform,selected reaction monitoring,mass spectrometry,targeted proteomics},
  language     = {eng},
  number       = {3},
  pages        = {1644--1653},
  publisher    = {The American Chemical Society},
  series       = {Journal of Proteome Research},
  title        = {Automated Workflow for Large-Scale Selected Reaction Monitoring Experiments},
  url          = {http://dx.doi.org/10.1021/pr200844d},
  volume       = {11},
  year         = {2012},
}