Comparative Analysis of Data Processing Tools for Mass Spectrometry Proteomics in Cardiovascular Research

Abstract

Mass spectrometry-based proteomics requires advanced data processing tools, yet most comparative studies have relied on noncardiovascular data sets or standard protein mixtures, limiting their relevance to cardiovascular research. We systematically compared three label-free data-dependent acquisition (DDA) tools (FragPipe, MaxQuant, and Proteome Discoverer) and five data-independent acquisition (DIA) tools (DIA-NN, DIA-Umpire, MSFragger-DIA, MaxDIA, and Spectronaut) using real-world cardiovascular tissue and blood-derived proteomics data sets. FragPipe and Spectronaut generally achieved the greatest quantitative proteome coverage among the evaluated DDA and DIA tools, respectively, particularly in cardiovascular tissue data sets, although tool rankings varied across sample types and proteome subsets. Despite differences in identification and quantification performance, peptide physicochemical characteristics were broadly similar across tools. Integration of complementary differential expression outputs increased differentially expressed protein detection in several data sets while maintaining agreement with the main results. These findings provide practical guidance for selecting data processing tools in cardiovascular proteomics and support the use of integrative approaches to maximize biological information from proteomics data sets.