Benchmarking commonly used software suites and analysis workflows for DIA proteomics and phosphoproteomics

• Published in: Nature communications Vol. 14; no. 1; pp. 94 - 17
• Main Authors: Lou, Ronghui, Cao, Ye, Li, Shanshan, Lang, Xiaoyu, Li, Yunxia, Zhang, Yaoyang, Shui, Wenqing
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 06.01.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/2784; 631/1647/2067; 631/1647/296; 631/45/475; 82/16; 82/58; Benchmarking - methods; Benchmarks; Complexity; Data analysis; Data processing; Datasets; Evaluation; Humanities and Social Sciences; Libraries; Mass Spectrometry - methods; multidisciplinary; Proteome - metabolism; Proteomes; Proteomics; Proteomics - methods; Science; Science (multidisciplinary); Software; Software packages; Tumor necrosis factor-α; Workflow
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-022-35740-1

A plethora of software suites and multiple classes of spectral libraries have been developed to enhance the depth and robustness of data-independent acquisition (DIA) data processing. However, how the combination of a DIA software tool and a spectral library impacts the outcome of DIA proteomics and phosphoproteomics data analysis has been rarely investigated using benchmark data that mimics biological complexity. In this study, we create DIA benchmark data sets simulating the regulation of thousands of proteins in a complex background, which are collected on both an Orbitrap and a timsTOF instruments. We evaluate four commonly used software suites (DIA-NN, Spectronaut, MaxDIA and Skyline) combined with seven different spectral libraries in global proteome analysis. Moreover, we assess their performances in analyzing phosphopeptide standards and TNF-α-induced phosphoproteome regulation. Our study provides a practical guidance on how to construct a robust data analysis pipeline for different proteomics studies implementing the DIA technique. Many software suites and spectral libraries have been developed for DIA proteomics data analysis. Here, the authors create benchmark data sets to evaluate four commonly used software tools combined with seven spectral libraries in both global proteomics and phosphoproteomics analysis.