IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v14y2023i1d10.1038_s41467-023-39869-5.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-023-39869-5
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-023-39869-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yi Yang & Qun Fang, 2024. "Prediction of glycopeptide fragment mass spectra by deep learning," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    2. 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.
    3. Valdemaras Petrosius & Pedro Aragon-Fernandez & Nil Üresin & Gergo Kovacs & Teeradon Phlairaharn & Benjamin Furtwängler & Jeff Op De Beeck & Sarah L. Skovbakke & Steffen Goletz & Simon Francis Thomsen, 2023. "Exploration of cell state heterogeneity using single-cell proteomics through sensitivity-tailored data-independent acquisition," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    4. Rafaela Muniz de Queiroz & Gizem Efe & Asja Guzman & Naoko Hashimoto & Yusuke Kawashima & Tomoaki Tanaka & Anil K. Rustgi & Carol Prives, 2024. "Mdm2 requires Sprouty4 to regulate focal adhesion formation and metastasis independent of p53," Nature Communications, Nature, vol. 15(1), pages 1-20, December.
    5. Ronghui Lou & Weizhen Liu & Rongjie Li & Shanshan Li & Xuming He & Wenqing Shui, 2021. "DeepPhospho accelerates DIA phosphoproteome profiling through in silico library generation," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
    6. Masaji Sakaguchi & Shota Okagawa & Yuma Okubo & Yuri Otsuka & Kazuki Fukuda & Motoyuki Igata & Tatsuya Kondo & Yoshifumi Sato & Tatsuya Yoshizawa & Takaichi Fukuda & Kazuya Yamagata & Weikang Cai & Yu, 2022. "Phosphatase protector alpha4 (α4) is involved in adipocyte maintenance and mitochondrial homeostasis through regulation of insulin signaling," Nature Communications, Nature, vol. 13(1), pages 1-19, December.
    7. Ray Sajulga & Caleb Easterly & Michael Riffle & Bart Mesuere & Thilo Muth & Subina Mehta & Praveen Kumar & James Johnson & Bjoern Andreas Gruening & Henning Schiebenhoefer & Carolin A Kolmeder & Steph, 2020. "Survey of metaproteomics software tools for functional microbiome analysis," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-20, November.
    8. Guilherme Reis-de-Oliveira & Victor Corasolla Carregari & Gabriel Rodrigues dos Reis de Sousa & Daniel Martins-de-Souza, 2024. "OmicScope unravels systems-level insights from quantitative proteomics data," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    9. Yu Zong & Yuxin Wang & Yi Yang & Dan Zhao & Xiaoqing Wang & Chengpin Shen & Liang Qiao, 2023. "DeepFLR facilitates false localization rate control in phosphoproteomics," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    10. Zhi Huang & Gennifer E. Merrihew & Eric B. Larson & Jea Park & Deanna Plubell & Edward J. Fox & Kathleen S. Montine & Caitlin S. Latimer & C. Dirk Keene & James Y. Zou & Michael J. MacCoss & Thomas J., 2023. "Brain proteomic analysis implicates actin filament processes and injury response in resilience to Alzheimer’s disease," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    11. Karsten Suhre & Guhan Ram Venkataraman & Harendra Guturu & Anna Halama & Nisha Stephan & Gaurav Thareja & Hina Sarwath & Khatereh Motamedchaboki & Margaret K. R. Donovan & Asim Siddiqui & Serafim Batz, 2024. "Nanoparticle enrichment mass-spectrometry proteomics identifies protein-altering variants for precise pQTL mapping," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    12. Digant Nayak & Dongwen Lv & Yaxia Yuan & Peiyi Zhang & Wanyi Hu & Anindita Nayak & Eliza A. Ruben & Zongyang Lv & Patrick Sung & Robert Hromas & Guangrong Zheng & Daohong Zhou & Shaun K. Olsen, 2024. "Development and crystal structures of a potent second-generation dual degrader of BCL-2 and BCL-xL," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    13. Marziah Hashimi & T. Andrew Sebrell & Jodi F. Hedges & Deann Snyder & Katrina N. Lyon & Stephanie D. Byrum & Samuel G. Mackintosh & Dan Crowley & Michelle D. Cherne & David Skwarchuk & Amanda Robison , 2023. "Antiviral responses in a Jamaican fruit bat intestinal organoid model of SARS-CoV-2 infection," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    14. Martin Rodriguez & Brady Trevisan & Ritu M. Ramamurthy & Sunil K. George & Jonathan Diaz & Jordan Alexander & Diane Meares & Denise J. Schwahn & David R. Quilici & Jorge Figueroa & Michael Gautreaux &, 2023. "Transplanting FVIII/ET3-secreting cells in fetal sheep increases FVIII levels long-term without inducing immunity or toxicity," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    15. Zhen Dong & Wenhao Jiang & Chunlong Wu & Ting Chen & Jiayi Chen & Xuan Ding & Shu Zheng & Kiryl D. Piatkevich & Yi Zhu & Tiannan Guo, 2024. "Spatial proteomics of single cells and organelles on tissue slides using filter-aided expansion proteomics," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    16. 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.
    17. Charlotte Adams & Wassim Gabriel & Kris Laukens & Mario Picciani & Mathias Wilhelm & Wout Bittremieux & Kurt Boonen, 2024. "Fragment ion intensity prediction improves the identification rate of non-tryptic peptides in timsTOF," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    18. Yi Yang & Guoquan Yan & Siyuan Kong & Mengxi Wu & Pengyuan Yang & Weiqian Cao & Liang Qiao, 2021. "GproDIA enables data-independent acquisition glycoproteomics with comprehensive statistical control," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
    19. Karl K. Krull & Syed Azmal Ali & Jeroen Krijgsveld, 2024. "Enhanced feature matching in single-cell proteomics characterizes IFN-γ response and co-existence of cell states," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    20. Shota Okagawa & Masaji Sakaguchi & Yuma Okubo & Yuri Takekuma & Motoyuki Igata & Tatsuya Kondo & Naoki Takeda & Kimi Araki & Bruna Brasil Brandao & Wei-Jun Qian & Yu-Hua Tseng & Rohit N. Kulkarni & Na, 2024. "Hepatic SerpinA1 improves energy and glucose metabolism through regulation of preadipocyte proliferation and UCP1 expression," Nature Communications, Nature, vol. 15(1), pages 1-19, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-39869-5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.