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

SUMMARY

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 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 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 peptide sequencing tool, PGPointNovo, accelerates 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 online.