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Author Correction: Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics

Author

Listed:
  • Mathias Wilhelm

    (Technical University of Munich (TUM)
    Technical University of Munich (TUM))

  • Daniel P. Zolg

    (Technical University of Munich (TUM))

  • Michael Graber

    (Technical University of Munich (TUM))

  • Siegfried Gessulat

    (Technical University of Munich (TUM))

  • Tobias Schmidt

    (Technical University of Munich (TUM))

  • Karsten Schnatbaum

    (JPT Peptide Technologies GmbH)

  • Celina Schwencke-Westphal

    (Technical University of Munich (TUM)
    German Cancer Consortium (DKTK), partner site Munich; and German Cancer Research Center (DKFZ)
    Technical University of Munich (TUM))

  • Philipp Seifert

    (Technical University of Munich (TUM)
    Technical University of Munich (TUM))

  • Niklas Andrade Krätzig

    (Technical University of Munich (TUM)
    Technical University of Munich (TUM)
    Technical University of Munich (TUM))

  • Johannes Zerweck

    (JPT Peptide Technologies GmbH)

  • Tobias Knaute

    (JPT Peptide Technologies GmbH)

  • Eva Bräunlein

    (Technical University of Munich (TUM)
    Technical University of Munich (TUM))

  • Patroklos Samaras

    (Technical University of Munich (TUM))

  • Ludwig Lautenbacher

    (Technical University of Munich (TUM))

  • Susan Klaeger

    (Broad Institute of MIT and Harvard)

  • Holger Wenschuh

    (JPT Peptide Technologies GmbH)

  • Roland Rad

    (Technical University of Munich (TUM)
    Technical University of Munich (TUM)
    Technical University of Munich (TUM))

  • Bernard Delanghe

    (Thermo Fisher Scientific)

  • Andreas Huhmer

    (Thermo Fisher Scientific)

  • Steven A. Carr

    (Broad Institute of MIT and Harvard)

  • Karl R. Clauser

    (Broad Institute of MIT and Harvard)

  • Angela M. Krackhardt

    (Technical University of Munich (TUM)
    German Cancer Consortium (DKTK), partner site Munich; and German Cancer Research Center (DKFZ)
    Technical University of Munich (TUM))

  • Ulf Reimer

    (JPT Peptide Technologies GmbH)

  • Bernhard Kuster

    (Technical University of Munich (TUM)
    Technical University of Munich (TUM))

Abstract

No abstract is available for this item.

Suggested Citation

  • Mathias Wilhelm & Daniel P. Zolg & Michael Graber & Siegfried Gessulat & Tobias Schmidt & Karsten Schnatbaum & Celina Schwencke-Westphal & Philipp Seifert & Niklas Andrade Krätzig & Johannes Zerweck &, 2021. "Author Correction: Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics," Nature Communications, Nature, vol. 12(1), pages 1-1, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-24263-w
    DOI: 10.1038/s41467-021-24263-w
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    Citations

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

    1. Hanqing Liao & Carolina Barra & Zhicheng Zhou & Xu Peng & Isaac Woodhouse & Arun Tailor & Robert Parker & Alexia Carré & Persephone Borrow & Michael J. Hogan & Wayne Paes & Laurence C. Eisenlohr & Rob, 2024. "MARS an improved de novo peptide candidate selection method for non-canonical antigen target discovery in cancer," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    2. Celina Tretter & Niklas Andrade Krätzig & Matteo Pecoraro & Sebastian Lange & Philipp Seifert & Clara Frankenberg & Johannes Untch & Gabriela Zuleger & Mathias Wilhelm & Daniel P. Zolg & Florian S. Dr, 2023. "Proteogenomic analysis reveals RNA as a source for tumor-agnostic neoantigen identification," Nature Communications, Nature, vol. 14(1), pages 1-22, December.
    3. Yi Yang & Qun Fang, 2024. "Prediction of glycopeptide fragment mass spectra by deep learning," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    4. David Gomez-Zepeda & Danielle Arnold-Schild & Julian Beyrle & Arthur Declercq & Ralf Gabriels & Elena Kumm & Annica Preikschat & Mateusz Krzysztof Łącki & Aurélie Hirschler & Jeewan Babu Rijal & Chris, 2024. "Thunder-DDA-PASEF enables high-coverage immunopeptidomics and is boosted by MS2Rescore with MS2PIP timsTOF fragmentation prediction model," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    5. Lei Xin & Rui Qiao & Xin Chen & Hieu Tran & Shengying Pan & Sahar Rabinoviz & Haibo Bian & Xianliang He & Brenton Morse & Baozhen Shan & Ming Li, 2022. "A streamlined platform for analyzing tera-scale DDA and DIA mass spectrometry data enables highly sensitive immunopeptidomics," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    6. Wen-Feng Zeng & Xie-Xuan Zhou & Sander Willems & Constantin Ammar & Maria Wahle & Isabell Bludau & Eugenia Voytik & Maximillian T. Strauss & Matthias Mann, 2022. "AlphaPeptDeep: a modular deep learning framework to predict peptide properties for proteomics," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    7. 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.
    8. Daniela Klaproth-Andrade & Johannes Hingerl & Yanik Bruns & Nicholas H. Smith & Jakob Träuble & Mathias Wilhelm & Julien Gagneur, 2024. "Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    9. Weiping Sun & Qianqiu Zhang & Xiyue Zhang & Ngoc Hieu Tran & M. Ziaur Rahman & Zheng Chen & Chao Peng & Jun Ma & Ming Li & Lei Xin & Baozhen Shan, 2023. "Glycopeptide database search and de novo sequencing with PEAKS GlycanFinder enable highly sensitive glycoproteomics," Nature Communications, Nature, vol. 14(1), pages 1-15, December.

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