Pipelines and Systems for Threshold-Avoiding Quantification of LC-MS/MS Data
(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.
(Less)
- author
- organization
- publishing date
- 2021-08-17
- 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
- 2024-08-24 22:53:56
@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}}, }