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Data Processing Has Major Impact on the Outcome of Quantitative Label-Free LC-MS Analysis

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Author

Summary, in English

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.

Publishing year

2015

Language

English

Pages

676-687

Publication/Series

Journal of Proteome Research

Volume

14

Issue

2

Document type

Journal article

Publisher

The American Chemical Society (ACS)

Topic

  • Immunology in the medical area

Keywords

  • label-free
  • quantification
  • proteomics
  • SRM
  • shotgun
  • targeted

Status

Published

Research group

  • Infection Medicine Proteomics

ISBN/ISSN/Other

  • ISSN: 1535-3893