A Workflow of Integrated Resources to Catalyze Network Pharmacology Driven COVID-19 Research.
Journal
bioRxiv : the preprint server for biology
Titre abrégé: bioRxiv
Pays: United States
ID NLM: 101680187
Informations de publication
Date de publication:
05 Nov 2020
05 Nov 2020
Historique:
entrez:
11
11
2020
pubmed:
12
11
2020
medline:
12
11
2020
Statut:
epublish
Résumé
In the event of an outbreak due to an emerging pathogen, time is of the essence to contain or to mitigate the spread of the disease. Drug repositioning is one of the strategies that has the potential to deliver therapeutics relatively quickly. The SARS-CoV-2 pandemic has shown that integrating critical data resources to drive drug-repositioning studies, involving host-host, hostpathogen and drug-target interactions, remains a time-consuming effort that translates to a delay in the development and delivery of a life-saving therapy. Here, we describe a workflow we designed for a semi-automated integration of rapidly emerging datasets that can be generally adopted in a broad network pharmacology research setting. The workflow was used to construct a COVID-19 focused multimodal network that integrates 487 host-pathogen, 74,805 host-host protein and 1,265 drug-target interactions. The resultant Neo4j graph database named "Neo4COVID19" is accessible via a web interface and via API calls based on the Bolt protocol. We believe that our Neo4COVID19 database will be a valuable asset to the research community and will catalyze the discovery of therapeutics to fight COVID-19. https://neo4covid19.ncats.io.
Identifiants
pubmed: 33173863
doi: 10.1101/2020.11.04.369041
pmc: PMC7654851
pii:
doi:
Types de publication
Preprint
Langues
eng
Commentaires et corrections
Type : UpdateIn
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