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The COVID-19 Gene and Drug Set Library.
Kuleshov, Maxim V; Clarke, Daniel J B; Kropiwnicki, Eryk; Jagodnik, Kathleen M; Bartal, Alon; Evangelista, John E; Zhou, Abigail; Ferguson, Laura B; Lachmann, Alexander; Ma'ayan, Avi.
Afiliación
  • Kuleshov MV; Department of Pharmacological Sciences; Mount Sinai Center for Bioinformatics; Big Data to Knowledge, Library of Integrated Network-based Cellular Signatures, Data Coordination and Integration Center (BD2K-LINCS DCIC); Knowledge Management Center for Illuminating the Druggable Genome (KMC-IDG); Icah
  • Clarke DJB; Department of Pharmacological Sciences; Mount Sinai Center for Bioinformatics; Big Data to Knowledge, Library of Integrated Network-based Cellular Signatures, Data Coordination and Integration Center (BD2K-LINCS DCIC); Knowledge Management Center for Illuminating the Druggable Genome (KMC-IDG); Icah
  • Kropiwnicki E; Department of Pharmacological Sciences; Mount Sinai Center for Bioinformatics; Big Data to Knowledge, Library of Integrated Network-based Cellular Signatures, Data Coordination and Integration Center (BD2K-LINCS DCIC); Knowledge Management Center for Illuminating the Druggable Genome (KMC-IDG); Icah
  • Jagodnik KM; Department of Pharmacological Sciences; Mount Sinai Center for Bioinformatics; Big Data to Knowledge, Library of Integrated Network-based Cellular Signatures, Data Coordination and Integration Center (BD2K-LINCS DCIC); Knowledge Management Center for Illuminating the Druggable Genome (KMC-IDG); Icah
  • Bartal A; Department of Pharmacological Sciences; Mount Sinai Center for Bioinformatics; Big Data to Knowledge, Library of Integrated Network-based Cellular Signatures, Data Coordination and Integration Center (BD2K-LINCS DCIC); Knowledge Management Center for Illuminating the Druggable Genome (KMC-IDG); Icah
  • Evangelista JE; Department of Pharmacological Sciences; Mount Sinai Center for Bioinformatics; Big Data to Knowledge, Library of Integrated Network-based Cellular Signatures, Data Coordination and Integration Center (BD2K-LINCS DCIC); Knowledge Management Center for Illuminating the Druggable Genome (KMC-IDG); Icah
  • Zhou A; Department of Pharmacological Sciences; Mount Sinai Center for Bioinformatics; Big Data to Knowledge, Library of Integrated Network-based Cellular Signatures, Data Coordination and Integration Center (BD2K-LINCS DCIC); Knowledge Management Center for Illuminating the Druggable Genome (KMC-IDG); Icah
  • Ferguson LB; Department of Neurology; Dell Medical School; University of Texas at Austin; 1601 Trinity Street, Bldg B, Austin, TX 78712, USA.
  • Lachmann A; Department of Pharmacological Sciences; Mount Sinai Center for Bioinformatics; Big Data to Knowledge, Library of Integrated Network-based Cellular Signatures, Data Coordination and Integration Center (BD2K-LINCS DCIC); Knowledge Management Center for Illuminating the Druggable Genome (KMC-IDG); Icah
  • Ma'ayan A; Department of Pharmacological Sciences; Mount Sinai Center for Bioinformatics; Big Data to Knowledge, Library of Integrated Network-based Cellular Signatures, Data Coordination and Integration Center (BD2K-LINCS DCIC); Knowledge Management Center for Illuminating the Druggable Genome (KMC-IDG); Icah
Res Sq ; 2020 May 13.
Article en En | MEDLINE | ID: mdl-32702729
The coronavirus (CoV) severe acute respiratory syndrome (SARS)-CoV-2 (COVID-19) pandemic has received rapid response by the research community to offer suggestions for repurposing of approved drugs as well as to improve our understanding of the COVID-19 viral life cycle molecular mechanisms. In a short period, tens of thousands of research preprints and other publications have emerged including those that report lists of experimentally validated drugs and compounds as potential COVID-19 therapies. In addition, gene sets from interacting COVID-19 virus-host proteins and differentially expressed genes when comparing infected to uninfected cells are being published at a fast rate. To organize this rapidly accumulating knowledge, we developed the COVID-19 Gene and Drug Set Library (https://amp.pharm.mssm.edu/covid19/), a collection of gene and drug sets related to COVID-19 research from multiple sources. The COVID-19 Gene and Drug Set Library is delivered as a web-based interface that enables users to view, download, analyze, visualize, and contribute gene and drug sets related to COVID-19 research. To evaluate the content of the library, we performed several analyses including comparing the results from 6 in-vitro drug screens for COVID-19 repurposing candidates. Surprisingly, we observe little overlap across these initial screens. The most common and unique hit across these screen is mefloquine, a malaria drug that should receive more attention as a potential therapeutic for COVID-19. Overall, the library of gene and drug sets can be used to identify community consensus, make researchers and clinicians aware of the development of new potential therapies, as well as allow the research community to work together towards a cure for COVID-19.

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Res Sq Año: 2020 Tipo del documento: Article Pais de publicación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Res Sq Año: 2020 Tipo del documento: Article Pais de publicación: Estados Unidos