pGlycoQuant with a deep residual network for quantitative glycoproteomics at intact glycopeptide level

pGlycoQuant with a deep residual network for quantitative glycoproteomics at intact glycopeptide level

Software tools for larger-scale intact glycopeptide quantification lag far behind, which hinders exploring the differential sitespecific glycosylation. Here, the authors report pGlycoQuant, a generic tool with a deep learning model for quantitative glycoproteomics at intact glycopeptide level.

Bibliographic Details
Main Authors: Siyuan Kong, Pengyun Gong, Wen-Feng Zeng, Biyun Jiang, Xinhang Hou, Yang Zhang, Huanhuan Zhao, Mingqi Liu, Guoquan Yan, Xinwen Zhou, Xihua Qiao, Mengxi Wu, Pengyuan Yang, Chao Liu, Weiqian Cao
Format: Article
Language:English
Published: Nature Portfolio 2022-12-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-022-35172-x

Internet

https://doi.org/10.1038/s41467-022-35172-x

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