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Analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform

Author

Listed:
  • Fengchao Yu

    (University of Michigan)

  • Guo Ci Teo

    (University of Michigan)

  • Andy T. Kong

    (University of Michigan
    University of Michigan)

  • Klemens Fröhlich

    (University of Basel)

  • Ginny Xiaohe Li

    (University of Michigan)

  • Vadim Demichev

    (Charité – Universitätsmedizin Berlin
    University of Cambridge)

  • Alexey I. Nesvizhskii

    (University of Michigan
    University of Michigan)

Abstract

Liquid chromatography (LC) coupled with data-independent acquisition (DIA) mass spectrometry (MS) has been increasingly used in quantitative proteomics studies. Here, we present a fast and sensitive approach for direct peptide identification from DIA data, MSFragger-DIA, which leverages the unmatched speed of the fragment ion indexing-based search engine MSFragger. Different from most existing methods, MSFragger-DIA conducts a database search of the DIA tandem mass (MS/MS) spectra prior to spectral feature detection and peak tracing across the LC dimension. To streamline the analysis of DIA data and enable easy reproducibility, we integrate MSFragger-DIA into the FragPipe computational platform for seamless support of peptide identification and spectral library building from DIA, data-dependent acquisition (DDA), or both data types combined. We compare MSFragger-DIA with other DIA tools, such as DIA-Umpire based workflow in FragPipe, Spectronaut, DIA-NN library-free, and MaxDIA. We demonstrate the fast, sensitive, and accurate performance of MSFragger-DIA across a variety of sample types and data acquisition schemes, including single-cell proteomics, phosphoproteomics, and large-scale tumor proteome profiling studies.

Suggested Citation

  • 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.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-39869-5
    DOI: 10.1038/s41467-023-39869-5
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    References listed on IDEAS

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    1. Brian C. Searle & Kristian E. Swearingen & Christopher A. Barnes & Tobias Schmidt & Siegfried Gessulat & Bernhard Küster & Mathias Wilhelm, 2020. "Generating high quality libraries for DIA MS with empirically corrected peptide predictions," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
    2. Brian C. Searle & Lindsay K. Pino & Jarrett D. Egertson & Ying S. Ting & Robert T. Lawrence & Brendan X. MacLean & Judit Villén & Michael J. MacCoss, 2018. "Chromatogram libraries improve peptide detection and quantification by data independent acquisition mass spectrometry," Nature Communications, Nature, vol. 9(1), pages 1-12, December.
    3. 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.
    4. Sofani Tafesse Gebreyesus & Asad Ali Siyal & Reta Birhanu Kitata & Eric Sheng-Wen Chen & Bayarmaa Enkhbayar & Takashi Angata & Kuo-I Lin & Yu-Ju Chen & Hsiung-Lin Tu, 2022. "Streamlined single-cell proteomics by an integrated microfluidic chip and data-independent acquisition mass spectrometry," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    5. Vadim Demichev & Lukasz Szyrwiel & Fengchao Yu & Guo Ci Teo & George Rosenberger & Agathe Niewienda & Daniela Ludwig & Jens Decker & Stephanie Kaspar-Schoenefeld & Kathryn S. Lilley & Michael Mülleder, 2022. "dia-PASEF data analysis using FragPipe and DIA-NN for deep proteomics of low sample amounts," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    6. Yi Yang & Xiaohui Liu & Chengpin Shen & Yu Lin & Pengyuan Yang & Liang Qiao, 2020. "In silico spectral libraries by deep learning facilitate data-independent acquisition proteomics," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    7. Klemens Fröhlich & Eva Brombacher & Matthias Fahrner & Daniel Vogele & Lucas Kook & Niko Pinter & Peter Bronsert & Sylvia Timme-Bronsert & Alexander Schmidt & Katja Bärenfaller & Clemens Kreutz & Oliv, 2022. "Benchmarking of analysis strategies for data-independent acquisition proteomics using a large-scale dataset comprising inter-patient heterogeneity," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
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    1. Humberto J. Ferreira & Brian J. Stevenson & HuiSong Pak & Fengchao Yu & Jessica Almeida Oliveira & Florian Huber & Marie Taillandier-Coindard & Justine Michaux & Emma Ricart-Altimiras & Anne I. Kraeme, 2024. "Immunopeptidomics-based identification of naturally presented non-canonical circRNA-derived peptides," Nature Communications, Nature, vol. 15(1), pages 1-18, 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.
    3. Kevin L. Yang & Fengchao Yu & Guo Ci Teo & Kai Li & Vadim Demichev & Markus Ralser & Alexey I. Nesvizhskii, 2023. "MSBooster: improving peptide identification rates using deep learning-based features," Nature Communications, Nature, vol. 14(1), pages 1-14, December.

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