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

Area of Science:

• Biochemistry
• Analytical Chemistry
• Bioinformatics

Background:

• Accurate processing of complex LC-MS/MS data is crucial but challenging for metabolomics and proteomics.
• Existing tools often struggle with high-dimensional biological datasets.

Purpose of the Study:

• To present the Pipelines and Systems for Threshold-Avoiding Quantification (PASTAQ) LC-MS/MS preprocessing toolset.
• To enable highly accurate quantification of data-dependent acquisition LC-MS/MS datasets.

Main Methods:

• PASTAQ utilizes single-stage (MS1) data for compound quantification.
• Novel algorithms are implemented for retention time alignment, feature detection, and linking annotations.
• The toolset offers straightforward parameterization and automatic quality control plot generation.

Main Results:

• PASTAQ demonstrates smaller variance in analyzing proteome replicates compared to existing tools.
• It enables detection of peptides over a larger dynamic concentration range.
• The pipeline successfully identified gender-related proteins in a human serum dataset.

Conclusions:

• PASTAQ is a high-performance toolset for accurate LC-MS/MS data preprocessing.
• It improves the analysis of complex biological samples in metabolomics and proteomics.
• The toolset facilitates more robust identification and quantification of biological molecules.