Deep learning enables de novo peptide sequencing from data-independent-acquisition mass spectrometry.

Journal: Nature methods
Published Date: Dec 20, 2018

Abstract

We present DeepNovo-DIA, a de novo peptide-sequencing method for data-independent acquisition (DIA) mass spectrometry data. We use neural networks to capture precursor and fragment ions across m/z, retention-time, and intensity dimensions. They are then further integrated with peptide sequence patterns to address the problem of highly multiplexed spectra. DIA coupled with de novo sequencing allowed us to identify novel peptides in human antibodies and antigens.

Authors

  • Ngoc Hieu Tran
    David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, Canada.
  • Rui Qiao
    Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada.
  • Lei Xin
    Bioinformatics Solutions Inc., Waterloo, ON, Canada.
  • Xin Chen
    University of Nottingham, Nottingham, United Kingdom.
  • Chuyi Liu
    Department of Electrical & Computer Engineering, University of Waterloo, Waterloo, ON, Canada.
  • Xianglilan Zhang
    State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China.
  • Baozhen Shan
    Bioinformatics Solutions Inc., Waterloo, ON, Canada.
  • Ali Ghodsi
    Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada.
  • Ming Li
    Radiology Department, Huadong Hospital, Affiliated with Fudan University, Shanghai, China.

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