UniSpec: Deep Learning for Predicting the Full Range of Peptide Fragment Ion Series to Enhance the Proteomics Data Analysis Workflow-Reference-Cited by-同舟云学术

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UniSpec: Deep Learning for Predicting the Full Range of Peptide Fragment Ion Series to Enhance the Proteomics Data Analysis Workflow

Published:2024-02-08 Issue: Volume: Page:
ISSN:0003-2700
Container-title:Analytical Chemistry
language:en
Short-container-title:Anal. Chem.

Author:

Lapin Joel¹²,Yan Xinjian³^ORCID,Dong Qian³^ORCID

Affiliation:

1. Department of Physics, Georgetown University, Washington, D.C. 20057, United States

2. Associate, Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, Maryland 20899, United States

3. Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, Maryland 20899, United States

Funder

National Institute of Standards and Technology

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.analchem.3c02321

Reference39 articles.

1. The biological impact of mass-spectrometry-based proteomics

2. More than 100,000 Detectable Peptide Species Elute in Single Shotgun Proteomics Runs but the Majority is Inaccessible to Data-Dependent LC−MS/MS

3. The Choice of Search Engine Affects Sequencing Depth and HLA Class I Allele-Specific Peptide Repertoires

4. Mass Spectrometry for Neurobiomarker Discovery: The Relevance of Post-Translational Modifications

5. Rapid and Accurate Peptide Identification from Tandem Mass Spectra

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