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RNA-GPS Predicts SARS-CoV-2 RNA Residency to Host Mitochondria and Nucleolus

Authors
  • Wu, Kevin E.1, 2, 3
  • Fazal, Furqan M.3
  • Parker, Kevin R.3
  • Zou, James1, 2
  • Chang, Howard Y.3, 4
  • 1 Department of Computer Science, Stanford University, Stanford, CA 94305, USA
  • 2 Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA 94305, USA
  • 3 Center for Personal and Dynamic Regulomes, Stanford University School of Medicine, Stanford, CA 94305, USA
  • 4 Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA 94305, USA
Type
Published Article
Journal
Cell Systems
Publisher
The Authors. Published by Elsevier Inc.
Publication Date
Jun 20, 2020
Volume
11
Issue
1
Pages
102–108
Identifiers
DOI: 10.1016/j.cels.2020.06.008
PMID: 32673562
PMCID: PMC7305881
Source
PubMed Central
Keywords
License
Unknown

Abstract

Where the SARS-CoV-2 genome localizes inside human cells remains understudied but may regulate viral replication and host response. We use a machine-learning model to predict subcellular residency of the SARS-CoV-2 genome and its encoded transcripts, as well as for other coronaviruses. Our predictions suggest new hypotheses for SARS-CoV-2 mechanisms.

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