Advanced

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:
author
organization
publishing date
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
external identifiers
  • wos:000349276400009
  • scopus:84922599807
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
2015-03-25 09:39:12
date last changed
2017-01-08 03:06:54
@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},
  keyword      = {label-free,quantification,proteomics,SRM,shotgun,targeted},
  language     = {eng},
  number       = {2},
  pages        = {676--687},
  publisher    = {The American Chemical Society},
  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},
  volume       = {14},
  year         = {2015},
}