PGPointNovo: an efficient neural network-based tool for parallel de novo peptide sequencing

peer-reviewed · Bioinformatics Advances · 2023

peer-reviewed · Bioinformatics Advances · 2023. Xiaofang Xu et al. Summary De novo peptide sequencing for tandem mass spectrometry data is not only a key technology for novel…
Date 2023-04-25
Type peer-reviewed
Venue Bioinformatics Advances
Publisher Bioinformatics Advances
Contribution algorithm
DOI 10.1093/bioadv/vbad057
Citations (OpenAlex) 9
Venue 2-year citedness 2.22

Abstract

Summary De novo peptide sequencing for tandem mass spectrometry data is not only a key technology for novel peptide identification, but also a precedent task for many downstream tasks, such as vaccine and antibody studies. In recent years, neural network models for de novo peptide sequencing have manifested a remarkable ability to accommodate various data sources and outperformed conventional peptide identification tools. However, the excellent model is computationally expensive, taking up to 1 week to process about 400 000 spectrums. This article presents PGPointNovo, a novel neural network-based tool for parallel de novo peptide sequencing. PGPointNovo uses data parallelization technology to accelerate training and inference and optimizes the training obstacles caused by large batch sizes. The results of extensive experiments conducted on multiple datasets of different sizes demonstrate that compared with PointNovo the excellent neural network-based de novo peptide sequencing tool, PGPointNovo, accelerates de novo peptide sequencing by up to 7.35× without precision or recall compromises. Availability and implementation The source code and the parameter settings are available at https://github.com/shallFun4Learning/PGPointNovo. Supplementary information Supplementary data are available at Bioinformatics Advances online.

Authors

  1. Xiaofang Xu · Chongqing University of Posts and Telecommunications
  2. Chunde Yang · Chongqing University of Posts and Telecommunications
  3. Qiang He · Swinburne University of Technology
  4. Kunxian Shu · Chongqing University of Posts and Telecommunications
  5. Yuan Xinpu · Chinese PLA General Hospital
  6. Zhiguang Chen · Sun Yat-sen University
  7. Yunping Zhu · Beijing Institute of Lifeomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing)
  8. Tao Chen · Beijing Institute of Lifeomics, Beijing Proteome Research Center

Methods and tools

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