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Pipelines and Systems for Threshold-Avoiding Quantification of LC-MS/MS Data

Sánchez Brotons, Alejandro ; Eriksson, Jonatan O. LU ; Kwiatkowski, Marcel ; Wolters, Justina C. ; Kema, Ido P. ; Barcaru, Andrei ; Kuipers, Folkert ; Bakker, Stephan J.L. ; Bischoff, Rainer and Suits, Frank , et al. (2021) In Analytical Chemistry 93(32). p.11215-11224
Abstract

The accurate processing of complex liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) data from biological samples is a major challenge for metabolomics, proteomics, and related approaches. Here, we present the pipelines and systems for threshold-avoiding quantification (PASTAQ) LC-MS/MS preprocessing toolset, which allows highly accurate quantification of data-dependent acquisition LC-MS/MS datasets. PASTAQ performs compound quantification using single-stage (MS1) data and implements novel algorithms for high-performance and accurate quantification, retention time alignment, feature detection, and linking annotations from multiple identification engines. PASTAQ offers straightforward parameterization and automatic... (More)

The accurate processing of complex liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) data from biological samples is a major challenge for metabolomics, proteomics, and related approaches. Here, we present the pipelines and systems for threshold-avoiding quantification (PASTAQ) LC-MS/MS preprocessing toolset, which allows highly accurate quantification of data-dependent acquisition LC-MS/MS datasets. PASTAQ performs compound quantification using single-stage (MS1) data and implements novel algorithms for high-performance and accurate quantification, retention time alignment, feature detection, and linking annotations from multiple identification engines. PASTAQ offers straightforward parameterization and automatic generation of quality control plots for data and preprocessing assessment. This design results in smaller variance when analyzing replicates of proteomes mixed with known ratios and allows the detection of peptides over a larger dynamic concentration range compared to widely used proteomics preprocessing tools. The performance of the pipeline is also demonstrated in a biological human serum dataset for the identification of gender-related proteins.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Analytical Chemistry
volume
93
issue
32
pages
10 pages
publisher
The American Chemical Society (ACS)
external identifiers
  • pmid:34355890
  • scopus:85113594276
ISSN
0003-2700
DOI
10.1021/acs.analchem.1c01892
language
English
LU publication?
yes
id
20279476-bccc-40fe-a154-99ca0732f647
date added to LUP
2021-09-09 09:13:13
date last changed
2024-08-10 20:51:21
@article{20279476-bccc-40fe-a154-99ca0732f647,
  abstract     = {{<p>The accurate processing of complex liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) data from biological samples is a major challenge for metabolomics, proteomics, and related approaches. Here, we present the pipelines and systems for threshold-avoiding quantification (PASTAQ) LC-MS/MS preprocessing toolset, which allows highly accurate quantification of data-dependent acquisition LC-MS/MS datasets. PASTAQ performs compound quantification using single-stage (MS1) data and implements novel algorithms for high-performance and accurate quantification, retention time alignment, feature detection, and linking annotations from multiple identification engines. PASTAQ offers straightforward parameterization and automatic generation of quality control plots for data and preprocessing assessment. This design results in smaller variance when analyzing replicates of proteomes mixed with known ratios and allows the detection of peptides over a larger dynamic concentration range compared to widely used proteomics preprocessing tools. The performance of the pipeline is also demonstrated in a biological human serum dataset for the identification of gender-related proteins. </p>}},
  author       = {{Sánchez Brotons, Alejandro and Eriksson, Jonatan O. and Kwiatkowski, Marcel and Wolters, Justina C. and Kema, Ido P. and Barcaru, Andrei and Kuipers, Folkert and Bakker, Stephan J.L. and Bischoff, Rainer and Suits, Frank and Horvatovich, Péter}},
  issn         = {{0003-2700}},
  language     = {{eng}},
  month        = {{08}},
  number       = {{32}},
  pages        = {{11215--11224}},
  publisher    = {{The American Chemical Society (ACS)}},
  series       = {{Analytical Chemistry}},
  title        = {{Pipelines and Systems for Threshold-Avoiding Quantification of LC-MS/MS Data}},
  url          = {{http://dx.doi.org/10.1021/acs.analchem.1c01892}},
  doi          = {{10.1021/acs.analchem.1c01892}},
  volume       = {{93}},
  year         = {{2021}},
}