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

Author

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  • Ronghui Lou

    (ShanghaiTech University
    ShanghaiTech University
    University of Chinese Academy of Sciences)

  • Ye Cao

    (University of Chinese Academy of Sciences
    Chinese Academy of Sciences)

  • Shanshan Li

    (ShanghaiTech University)

  • Xiaoyu Lang

    (ShanghaiTech University
    ShanghaiTech University
    University of Chinese Academy of Sciences)

  • Yunxia Li

    (Chinese Academy of Sciences)

  • Yaoyang Zhang

    (Chinese Academy of Sciences
    Shanghai Key Laboratory of Aging Studies)

  • Wenqing Shui

    (ShanghaiTech University
    ShanghaiTech University)

Abstract

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.

Suggested Citation

  • Ronghui Lou & Ye Cao & Shanshan Li & Xiaoyu Lang & Yunxia Li & Yaoyang Zhang & Wenqing Shui, 2023. "Benchmarking commonly used software suites and analysis workflows for DIA proteomics and phosphoproteomics," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-022-35740-1
    DOI: 10.1038/s41467-022-35740-1
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    Cited by:

    1. Fengchao Yu & Guo Ci Teo & Andy T. Kong & Klemens Fröhlich & Ginny Xiaohe Li & Vadim Demichev & Alexey I. Nesvizhskii, 2023. "Analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    2. Hui Peng & He Wang & Weijia Kong & Jinyan Li & Wilson Wen Bin Goh, 2024. "Optimizing differential expression analysis for proteomics data via high-performing rules and ensemble inference," Nature Communications, Nature, vol. 15(1), pages 1-18, December.

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