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

Alejandro Sánchez Brotons, Jonatan O Eriksson, Marcel Kwiatkowski, Justina C Wolters, Ido P Kema, Andrei Barcaru, Folkert Kuipers, Stephan J L Bakker, Rainer Bischoff, Frank Suits, Péter Horvatovich*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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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.

Original languageEnglish
Pages (from-to)11215–11224
Number of pages9
JournalAnalytical Chemistry
Issue number32
Early online date6-Aug-2021
Publication statusPublished - 17-Aug-2021

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