Mitigating the missing-fragmentation problem in de novo peptide sequencing with a two-stage graph-based deep learning model

peer-reviewed · Nature Machine Intelligence · 2023

peer-reviewed · Nature Machine Intelligence · 2023. Zeping Mao et al.
Date 2023-10-19
Type peer-reviewed
Venue Nature Machine Intelligence
Publisher Springer Science and Business Media LLC
Contribution algorithm
DOI 10.1038/s42256-023-00738-x
Citations (OpenAlex) 41
Venue 2-year citedness 23.00

Authors

  1. Zeping Mao · Bioinformatics Solutions Inc., University of Waterloo
  2. Ruixue Zhang · University of Waterloo
  3. Lei Xin · Bioinformatics Solutions (Canada), Bioinformatics Solutions Inc., Western University
  4. Ming Li · Bioinformatics Solutions Inc., Central China Institute of Artificial Intelligence, Peng Cheng Laboratory, University of Waterloo, University of Western Ontario

Methods and tools

Data used

  • BoxCar acquisition method enables single shot proteomics at a depth of 10,000 proteins in 100 minutes (as deposited) · PXD006109
  • GraphNovo dataset and checkpoint (as deposited) · 10.5281/zenodo.8000316
  • NFYB-1 regulates mitochondrial function and longevity via lysosomal prosaposin (as deposited) · PXD013233
  • Predictive signatures of 19 antibiotics-induced Escherichia coli proteomes (as deposited) · PXD016001
  • The beta subunit of nascent polypeptide associated complex plays a role in flowers and siliques development of Arabidops (as deposited) · PXD016315

Cites (24)

Cited by (32)

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