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

The accurate processing of complex 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 pre-processing toolset, which allows highly accurate quantification of data-dependent acquisition (DDA) 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 frommultiple identification engines. PASTAQ offers straightforward parametrization and automatic generation of quality control plots for data and pre-processing assessment. This design results in smaller variance when analyzing replicates of proteomes mixed with known ratios, and allows the detection of peptides with 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.