Data Processing Has Major Impact on the Outcome of Quantitative Label-Free LC-MS Analysis
Chawade, Aakash LU ; Sandin, Marianne LU ; Teleman, Johan LU ; Malmström, Johan LU
and Levander, Fredrik
LU
(2015)
In Journal of Proteome Research
14(2).
p.676-687
- Abstract
- High-throughput multiplexed protein quantification using mass spectrometry is steadily increasing in popularity, with the two major techniques being data-dependent acquisition (DDA) and targeted acquisition using selected reaction monitoring (SRM). However, both techniques involve extensive data processing, which can be performed by a multitude of different software solutions. Analysis of quantitative LC-MS/MS data is mainly performed in three major steps: processing of raw data, normalization, and statistical analysis. To evaluate the impact of data processing steps, we developed two new benchmark data sets, one each for DDA and SRM, with samples consisting of a long-range dilution series of synthetic peptides spiked in a total cell... (More)
- High-throughput multiplexed protein quantification using mass spectrometry is steadily increasing in popularity, with the two major techniques being data-dependent acquisition (DDA) and targeted acquisition using selected reaction monitoring (SRM). However, both techniques involve extensive data processing, which can be performed by a multitude of different software solutions. Analysis of quantitative LC-MS/MS data is mainly performed in three major steps: processing of raw data, normalization, and statistical analysis. To evaluate the impact of data processing steps, we developed two new benchmark data sets, one each for DDA and SRM, with samples consisting of a long-range dilution series of synthetic peptides spiked in a total cell protein digest. The generated data were processed by eight different software workflows and three postprocessing steps. The results show that the choice of the raw data processing software and the postprocessing steps play an important role in the final outcome. Also, the linear dynamic range of the DDA data could be extended by an order of magnitude through feature alignment and a charge state merging algorithm proposed here. Furthermore, the benchmark data sets are made publicly available for further benchmarking and software developments. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/5160272
- author
- Chawade, Aakash
LU
; Sandin, Marianne
LU
; Teleman, Johan
LU
; Malmström, Johan
LU
and Levander, Fredrik
LU
- organization
- publishing date
- 2015
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- label-free, quantification, proteomics, SRM, shotgun, targeted
- in
- Journal of Proteome Research
- volume
- 14
- issue
- 2
- pages
- 676 - 687
- publisher
- The American Chemical Society (ACS)
- external identifiers
-
- wos:000349276400009
- scopus:84922599807
- pmid:25407311
- ISSN
- 1535-3893
- DOI
- 10.1021/pr500665j
- language
- English
- LU publication?
- yes
- id
- fbd00b4c-4867-480d-965e-05292429ae04 (old id 5160272)
- date added to LUP
- 2016-04-01 10:00:52
- date last changed
- 2022-01-25 18:55:00
@article{fbd00b4c-4867-480d-965e-05292429ae04,
abstract = {{High-throughput multiplexed protein quantification using mass spectrometry is steadily increasing in popularity, with the two major techniques being data-dependent acquisition (DDA) and targeted acquisition using selected reaction monitoring (SRM). However, both techniques involve extensive data processing, which can be performed by a multitude of different software solutions. Analysis of quantitative LC-MS/MS data is mainly performed in three major steps: processing of raw data, normalization, and statistical analysis. To evaluate the impact of data processing steps, we developed two new benchmark data sets, one each for DDA and SRM, with samples consisting of a long-range dilution series of synthetic peptides spiked in a total cell protein digest. The generated data were processed by eight different software workflows and three postprocessing steps. The results show that the choice of the raw data processing software and the postprocessing steps play an important role in the final outcome. Also, the linear dynamic range of the DDA data could be extended by an order of magnitude through feature alignment and a charge state merging algorithm proposed here. Furthermore, the benchmark data sets are made publicly available for further benchmarking and software developments.}},
author = {{Chawade, Aakash and Sandin, Marianne and Teleman, Johan and Malmström, Johan and Levander, Fredrik}},
issn = {{1535-3893}},
keywords = {{label-free; quantification; proteomics; SRM; shotgun; targeted}},
language = {{eng}},
number = {{2}},
pages = {{676--687}},
publisher = {{The American Chemical Society (ACS)}},
series = {{Journal of Proteome Research}},
title = {{Data Processing Has Major Impact on the Outcome of Quantitative Label-Free LC-MS Analysis}},
url = {{http://dx.doi.org/10.1021/pr500665j}},
doi = {{10.1021/pr500665j}},
volume = {{14}},
year = {{2015}},
}