Multiomic analysis reveals cell-type-specific molecular determinants of COVID-19 severity

Zhang, S., Cooper-Knock, J. orcid.org/0000-0002-0873-8689, Weimer, A.K. et al. (17 more authors) (2022) Multiomic analysis reveals cell-type-specific molecular determinants of COVID-19 severity. Cell Systems, 13 (8). pp. 598-614. ISSN 2405-4712

Abstract

Metadata

Authors/Creators:
  • Zhang, S.
  • Cooper-Knock, J. ORCID logo https://orcid.org/0000-0002-0873-8689
  • Weimer, A.K.
  • Shi, M.
  • Kozhaya, L.
  • Unutmaz, D.
  • Harvey, C.
  • Julian, T.H.
  • Furini, S.
  • Frullanti, E.
  • Fava, F.
  • Renieri, A.
  • Gao, P.
  • Shen, X.
  • Timpanaro, I.S.
  • Kenna, K.P.
  • Baillie, J.K.
  • Davis, M.M.
  • Tsao, P.S.
  • Snyder, M.P.
Copyright, Publisher and Additional Information: © 2022 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Keywords: COVID-19; gene discovery; genome-wide association study; GWAS; machine learning; Mendelian randomization; network analysis; NK cell; rare variant analysis; single-cell multiomic profiling
Dates:
  • Accepted: 18 May 2022
  • Published (online): 2 June 2022
  • Published: 17 August 2022
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Sheffield Teaching Hospitals
Depositing User: Symplectic Sheffield
Date Deposited: 19 Jul 2022 11:42
Last Modified: 24 Feb 2023 10:49
Status: Published
Publisher: Elsevier BV
Refereed: Yes
Identification Number: https://doi.org/10.1016/j.cels.2022.05.007
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