Automated Selected Reaction Monitoring Software for Accurate Label-Free Protein Quantification.
Teleman, Johan LU ; Karlsson, Christofer LU ; Waldemarson, Sofia LU ; Hansson, Karin M LU ; James, Peter LU
; Malmström, Johan
LU
and Levander, Fredrik
LU
(2012)
In Journal of Proteome Research
11(7).
p.3766-3773
- Abstract
- Selected reaction monitoring (SRM) is a mass spectrometry method with documented ability to quantify proteins accurately and reproducibly using labeled reference peptides. However, the use of labeled reference peptides becomes impractical if large numbers of peptides are targeted and when high flexibility is desired when selecting peptides. We have developed a label-free quantitative SRM workflow that relies on a new automated algorithm, Anubis, for accurate peak detection. Anubis efficiently removes interfering signals from contaminating peptides to estimate the true signal of the targeted peptides. We evaluated the algorithm on a published multisite data set and achieved results in line with manual data analysis. In complex peptide... (More)
- Selected reaction monitoring (SRM) is a mass spectrometry method with documented ability to quantify proteins accurately and reproducibly using labeled reference peptides. However, the use of labeled reference peptides becomes impractical if large numbers of peptides are targeted and when high flexibility is desired when selecting peptides. We have developed a label-free quantitative SRM workflow that relies on a new automated algorithm, Anubis, for accurate peak detection. Anubis efficiently removes interfering signals from contaminating peptides to estimate the true signal of the targeted peptides. We evaluated the algorithm on a published multisite data set and achieved results in line with manual data analysis. In complex peptide mixtures from whole proteome digests of Streptococcus pyogenes we achieved a technical variability across the entire proteome abundance range of 6.5-19.2%, which was considerably below the total variation across biological samples. Our results show that the label-free SRM workflow with automated data analysis is feasible for large-scale biological studies, opening up new possibilities for quantitative proteomics and systems biology. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/2859864
- author
- Teleman, Johan
LU
; Karlsson, Christofer
LU
; Waldemarson, Sofia
LU
; Hansson, Karin M
LU
; James, Peter
LU
; Malmström, Johan
LU
and Levander, Fredrik
LU
- organization
- publishing date
- 2012
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Journal of Proteome Research
- volume
- 11
- issue
- 7
- pages
- 3766 - 3773
- publisher
- The American Chemical Society (ACS)
- external identifiers
-
- pmid:22658081
- wos:000306049800021
- scopus:84863624355
- pmid:22658081
- ISSN
- 1535-3893
- DOI
- 10.1021/pr300256x
- language
- English
- LU publication?
- yes
- id
- 1d135957-d015-4c4a-8100-26dadc5396c5 (old id 2859864)
- date added to LUP
- 2016-04-01 11:14:24
- date last changed
- 2023-11-10 15:18:11
@article{1d135957-d015-4c4a-8100-26dadc5396c5,
abstract = {{Selected reaction monitoring (SRM) is a mass spectrometry method with documented ability to quantify proteins accurately and reproducibly using labeled reference peptides. However, the use of labeled reference peptides becomes impractical if large numbers of peptides are targeted and when high flexibility is desired when selecting peptides. We have developed a label-free quantitative SRM workflow that relies on a new automated algorithm, Anubis, for accurate peak detection. Anubis efficiently removes interfering signals from contaminating peptides to estimate the true signal of the targeted peptides. We evaluated the algorithm on a published multisite data set and achieved results in line with manual data analysis. In complex peptide mixtures from whole proteome digests of Streptococcus pyogenes we achieved a technical variability across the entire proteome abundance range of 6.5-19.2%, which was considerably below the total variation across biological samples. Our results show that the label-free SRM workflow with automated data analysis is feasible for large-scale biological studies, opening up new possibilities for quantitative proteomics and systems biology.}},
author = {{Teleman, Johan and Karlsson, Christofer and Waldemarson, Sofia and Hansson, Karin M and James, Peter and Malmström, Johan and Levander, Fredrik}},
issn = {{1535-3893}},
language = {{eng}},
number = {{7}},
pages = {{3766--3773}},
publisher = {{The American Chemical Society (ACS)}},
series = {{Journal of Proteome Research}},
title = {{Automated Selected Reaction Monitoring Software for Accurate Label-Free Protein Quantification.}},
url = {{http://dx.doi.org/10.1021/pr300256x}},
doi = {{10.1021/pr300256x}},
volume = {{11}},
year = {{2012}},
}