COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms.

Details

Serval ID
serval:BIB_1A41D5739AA2
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms.
Journal
Molecular systems biology
Author(s)
Ostaszewski M., Niarakis A., Mazein A., Kuperstein I., Phair R., Orta-Resendiz A., Singh V., Aghamiri S.S., Acencio M.L., Glaab E., Ruepp A., Fobo G., Montrone C., Brauner B., Frishman G., Monraz Gómez L.C., Somers J., Hoch M., Kumar Gupta S., Scheel J., Borlinghaus H., Czauderna T., Schreiber F., Montagud A., Ponce de Leon M., Funahashi A., Hiki Y., Hiroi N., Yamada T.G., Dräger A., Renz A., Naveez M., Bocskei Z., Messina F., Börnigen D., Fergusson L., Conti M., Rameil M., Nakonecnij V., Vanhoefer J., Schmiester L., Wang M., Ackerman E.E., Shoemaker J.E., Zucker J., Oxford K., Teuton J., Kocakaya E., Summak G.Y., Hanspers K., Kutmon M., Coort S., Eijssen L., Ehrhart F., Rex DAB, Slenter D., Martens M., Pham N., Haw R., Jassal B., Matthews L., Orlic-Milacic M., Senff Ribeiro A., Rothfels K., Shamovsky V., Stephan R., Sevilla C., Varusai T., Ravel J.M., Fraser R., Ortseifen V., Marchesi S., Gawron P., Smula E., Heirendt L., Satagopam V., Wu G., Riutta A., Golebiewski M., Owen S., Goble C., Hu X., Overall R.W., Maier D., Bauch A., Gyori B.M., Bachman J.A., Vega C., Grouès V., Vazquez M., Porras P., Licata L., Iannuccelli M., Sacco F., Nesterova A., Yuryev A., de Waard A., Turei D., Luna A., Babur O., Soliman S., Valdeolivas A., Esteban-Medina M., Peña-Chilet M., Rian K., Helikar T., Puniya B.L., Modos D., Treveil A., Olbei M., De Meulder B., Ballereau S., Dugourd A., Naldi A., Noël V., Calzone L., Sander C., Demir E., Korcsmaros T., Freeman T.C., Augé F., Beckmann J.S., Hasenauer J., Wolkenhauer O., Wilighagen E.L., Pico A.R., Evelo C.T., Gillespie M.E., Stein L.D., Hermjakob H., D'Eustachio P., Saez-Rodriguez J., Dopazo J., Valencia A., Kitano H., Barillot E., Auffray C., Balling R., Schneider R.
Working group(s)
COVID-19 Disease Map Community
ISSN
1744-4292 (Electronic)
ISSN-L
1744-4292
Publication state
Published
Issued date
10/2021
Peer-reviewed
Oui
Volume
17
Number
10
Pages
e10387
Language
english
Notes
Publication types: Journal Article
Publication Status: ppublish
Abstract
We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.
Keywords
computable knowledge repository, large-scale biocuration, omics data analysis, open access community effort, systems biomedicine
Pubmed
Open Access
Yes
Create date
25/10/2021 9:27
Last modification date
04/02/2022 7:35
Usage data