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A Comprehensive Evaluation of MS/MS Spectrum Prediction Tools for Shotgun Proteomics
Author(s) -
Xu Rui,
Sheng Jie,
Bai Mingze,
Shu Kunxian,
Zhu Yunping,
Chang Cheng
Publication year - 2020
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/pmic.201900345
Subject(s) - computer science , machine learning , artificial intelligence , shotgun proteomics , shotgun , deep learning , proteomics , data mining , chemistry , biochemistry , gene
Spectrum prediction using machine learning or deep learning models is an emerging method in computational proteomics. Several deep learning‐based MS/MS spectrum prediction tools have been developed and showed their potentials not only for increasing the sensitivity and accuracy of data‐dependent acquisition search engines, but also for building spectral libraries for data‐independent acquisition analysis. Different tools with their unique algorithms and implementations may result in different performances. Hence, it is necessary to systematically evaluate these tools to find out their preferences and intrinsic differences. In this study, multiple datasets with different collision energies, enzymes, instruments, and species, are used to evaluate the performances of the deep learning‐based MS/MS spectrum prediction tools, as well as, the machine learning‐based tool MS2PIP. The evaluations may provide helpful insights and guidelines of spectrum prediction tools for the corresponding researchers.