IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v13y2022i1d10.1038_s41467-022-30094-0.html
   My bibliography  Save this article

Benchmarking of analysis strategies for data-independent acquisition proteomics using a large-scale dataset comprising inter-patient heterogeneity

Author

Listed:
  • Klemens Fröhlich

    (Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg
    University of Freiburg
    University of Freiburg)

  • Eva Brombacher

    (University of Freiburg
    University of Freiburg
    Faculty of Medicine and Medical Center – University of Freiburg
    University of Freiburg)

  • Matthias Fahrner

    (Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg
    University of Freiburg
    University of Freiburg)

  • Daniel Vogele

    (Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg
    University of Freiburg)

  • Lucas Kook

    (University of Zurich
    Zurich University of Applied Sciences)

  • Niko Pinter

    (Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg)

  • Peter Bronsert

    (Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg
    German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ)
    Tumorbank Comprehensive Cancer Center Freiburg, Medical Center University of Freiburg)

  • Sylvia Timme-Bronsert

    (Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg
    Tumorbank Comprehensive Cancer Center Freiburg, Medical Center University of Freiburg)

  • Alexander Schmidt

    (University of Basel)

  • Katja Bärenfaller

    (University of Zurich, and Swiss Institute of Bioinformatics (SIB))

  • Clemens Kreutz

    (Faculty of Medicine and Medical Center – University of Freiburg
    University of Freiburg)

  • Oliver Schilling

    (Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg
    German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ)
    BIOSS Centre for Biological Signaling Studies, University of Freiburg)

Abstract

Numerous software tools exist for data-independent acquisition (DIA) analysis of clinical samples, necessitating their comprehensive benchmarking. We present a benchmark dataset comprising real-world inter-patient heterogeneity, which we use for in-depth benchmarking of DIA data analysis workflows for clinical settings. Combining spectral libraries, DIA software, sparsity reduction, normalization, and statistical tests results in 1428 distinct data analysis workflows, which we evaluate based on their ability to correctly identify differentially abundant proteins. From our dataset, we derive bootstrap datasets of varying sample sizes and use the whole range of bootstrap datasets to robustly evaluate each workflow. We find that all DIA software suites benefit from using a gas-phase fractionated spectral library, irrespective of the library refinement used. Gas-phase fractionation-based libraries perform best against two out of three reference protein lists. Among all investigated statistical tests non-parametric permutation-based statistical tests consistently perform best.

Suggested Citation

  • 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.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-30094-0
    DOI: 10.1038/s41467-022-30094-0
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-022-30094-0
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-022-30094-0?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. Tomi Suomi & Fatemeh Seyednasrollah & Maria K Jaakkola & Thomas Faux & Laura L Elo, 2017. "ROTS: An R package for reproducibility-optimized statistical testing," PLOS Computational Biology, Public Library of Science, vol. 13(5), pages 1-10, May.
    2. 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.
    3. Amaratunga D. & Cabrera J., 2001. "Analysis of Data From Viral DNA Microchips," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1161-1170, December.
    4. 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.
    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.
    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. 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.

    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. 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. Yi Yang & Qun Fang, 2024. "Prediction of glycopeptide fragment mass spectra by deep learning," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    3. 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.
    4. 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.
    5. 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.
    6. Siyuan Kong & Pengyun Gong & Wen-Feng Zeng & Biyun Jiang & Xinhang Hou & Yang Zhang & Huanhuan Zhao & Mingqi Liu & Guoquan Yan & Xinwen Zhou & Xihua Qiao & Mengxi Wu & Pengyuan Yang & Chao Liu & Weiqi, 2022. "pGlycoQuant with a deep residual network for quantitative glycoproteomics at intact glycopeptide level," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    7. 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.
    8. 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.
    9. Kafadar, Karen & Phang, Tzulip, 2003. "Transformations, background estimation, and process effects in the statistical analysis of microarrays," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 313-338, October.
    10. 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.
    11. 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.
    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. James Okoh & Jacqunae Mays & Alexandre Bacq & Juan A. Oses-Prieto & Stefka Tyanova & Chien-Ju Chen & Khalel Imanbeyev & Marion Doladilhe & Hongyi Zhou & Paymaan Jafar-Nejad & Alma Burlingame & Jeffrey, 2023. "Targeted suppression of mTORC2 reduces seizures across models of epilepsy," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    15. 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.
    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. Gang Ren & Wai Lim Ku & Guangzhe Ge & Jackson A. Hoffman & Jee Youn Kang & Qingsong Tang & Kairong Cui & Yong He & Yukun Guan & Bin Gao & Chengyu Liu & Trevor K. Archer & Keji Zhao, 2024. "Acute depletion of BRG1 reveals its primary function as an activator of transcription," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    20. Tommi Välikangas & Tomi Suomi & Courtney E. Chandler & Alison J. Scott & Bao Q. Tran & Robert K. Ernst & David R. Goodlett & Laura L. Elo, 2022. "Benchmarking tools for detecting longitudinal differential expression in proteomics data allows establishing a robust reproducibility optimization regression approach," Nature Communications, Nature, vol. 13(1), pages 1-18, 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:13:y:2022:i:1:d:10.1038_s41467-022-30094-0. 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.