ArticleAnalytical chemistry2021
Pipelines and Systems for Threshold-Avoiding Quantification of LC-MS/MS Data.
Article in Analytical chemistry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Navigating the glycomics landscape with CE-MS: advances in sample preparation and analytical strategies.The Analyst · 2026Review
- Untargeted Plasma Proteomic Signatures and Late Graft Failure in Kidney Transplant Recipients.Transplantation · 2026Article
- GlycoGenius: a streamlined high-throughput glycan composition identification tool.Nature communications · 2025Article
- Article
- Critical review on data processing algorithms in non-target screening: challenges and opportunities to improve result comparability.Analytical and bioanalytical chemistry · 2023Review
- Development of a scoring parameter to characterize data quality of centroids in high-resolution mass spectra.Analytical and bioanalytical chemistry · 2022Article
- Comparative Assessment of Quantification Methods for Tumor Tissue Phosphoproteomics.Analytical chemistry · 2022Article
- Combined Metabolic and Chemical (CoMetChem) Labeling Using Stable Isotopes-a Strategy to Reveal Site-Specific Histone Acetylation and Deacetylation Rates by LC-MS.Analytical chemistry · 2021Article
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Authors and funding
11 authors.
Funding
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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 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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