%0 Journal Article
%A Ostaszewski, Marek
%A Niarakis, Anna
%A Mazein, Alexander
%A Kuperstein, Inna
%A Phair, Robert
%A Orta-Resendiz, Aurelio
%A Singh, Vidisha
%A Aghamiri, Sara Sadat
%A Acencio, Marcio Luis
%A Glaab, Enrico
%A Ruepp, Andreas
%A Fobo, Gisela
%A Montrone, Corinna
%A Brauner, Barbara
%A Frishman, Goar
%A Monraz Gómez, Luis Cristóbal
%A Somers, Julia
%A Hoch, Matti
%A Kumar Gupta, Shailendra
%A Scheel, Julia
%A Borlinghaus, Hanna
%A Czauderna, Tobias
%A Schreiber, Falk
%A Montagud, Arnau
%A Ponce de Leon, Miguel
%A Funahashi, Akira
%A Hiki, Yusuke
%A Hiroi, Noriko
%A Yamada, Takahiro G
%A Dräger, Andreas
%A Renz, Alina
%A Naveez, Muhammad
%A Bocskei, Zsolt
%A Messina, Francesco
%A Börnigen, Daniela
%A Fergusson, Liam
%A Conti, Marta
%A Rameil, Marius
%A Nakonecnij, Vanessa
%A Vanhoefer, Jakob
%A Schmiester, Leonard
%A Wang, Muying
%A Ackerman, Emily E
%A Shoemaker, Jason E
%A Zucker, Jeremy
%A Oxford, Kristie
%A Teuton, Jeremy
%A Kocakaya, Ebru
%A Summak, Gökçe Yağmur
%A Hanspers, Kristina
%A Kutmon, Martina
%A Coort, Susan
%A Eijssen, Lars
%A Ehrhart, Friederike
%A Rex, Devasahayam Arokia Balaya
%A Slenter, Denise
%A Martens, Marvin
%A Pham, Nhung
%A Haw, Robin
%A Jassal, Bijay
%A Matthews, Lisa
%A Orlic-Milacic, Marija
%A Senff Ribeiro, Andrea
%A Rothfels, Karen
%A Shamovsky, Veronica
%A Stephan, Ralf
%A Sevilla, Cristoffer
%A Varusai, Thawfeek
%A Ravel, Jean-Marie
%A Fraser, Rupsha
%A Ortseifen, Vera
%A Marchesi, Silvia
%A Gawron, Piotr
%A Smula, Ewa
%A Heirendt, Laurent
%A Satagopam, Venkata
%A Wu, Guanming
%A Riutta, Anders
%A Golebiewski, Martin
%A Owen, Stuart
%A Goble, Carole
%A Hu, Xiaoming
%A Overall, Rupert
%A Maier, Dieter
%A Bauch, Angela
%A Gyori, Benjamin M
%A Bachman, John A
%A Vega, Carlos
%A Grouès, Valentin
%A Vazquez, Miguel
%A Porras, Pablo
%A Licata, Luana
%A Iannuccelli, Marta
%A Sacco, Francesca
%A Nesterova, Anastasia
%A Yuryev, Anton
%A de Waard, Anita
%A Turei, Denes
%A Luna, Augustin
%A Babur, Ozgun
%A Soliman, Sylvain
%A Valdeolivas, Alberto
%A Esteban-Medina, Marina
%A Peña-Chilet, Maria
%A Rian, Kinza
%A Helikar, Tomáš
%A Puniya, Bhanwar Lal
%A Modos, Dezso
%A Treveil, Agatha
%A Olbei, Marton
%A De Meulder, Bertrand
%A Ballereau, Stephane
%A Dugourd, Aurélien
%A Naldi, Aurélien
%A Noël, Vincent
%A Calzone, Laurence
%A Sander, Chris
%A Demir, Emek
%A Korcsmaros, Tamas
%A Freeman, Tom C
%A Augé, Franck
%A Beckmann, Jacques S
%A Hasenauer, Jan
%A Wolkenhauer, Olaf
%A Wilighagen, Egon L
%A Pico, Alexander R
%A Evelo, Chris T
%A Gillespie, Marc E
%A Stein, Lincoln D
%A Hermjakob, Henning
%A D'Eustachio, Peter
%A Saez-Rodriguez, Julio
%A Dopazo, Joaquin
%A Valencia, Alfonso
%A Kitano, Hiroaki
%A Barillot, Emmanuel
%A Auffray, Charles
%A Balling, Rudi
%A Schneider, Reinhard
%T COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms.
%J Molecular systems biology
%V 17
%N 10
%@ 1744-4292
%C Heidelberg
%I EMBO Press
%M DZNE-2021-01564
%P e10387
%D 2021
%Z (CC BY)
%X 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.
%K COVID-19 Drug Treatment
%K Antiviral Agents: therapeutic use
%K COVID-19: drug therapy
%K COVID-19: genetics
%K COVID-19: immunology
%K COVID-19: virology
%K Computational Biology: methods
%K Computer Graphics
%K Cytokines: genetics
%K Cytokines: immunology
%K Data Mining: statistics & numerical data
%K Databases, Factual
%K Gene Expression Regulation
%K Host Microbial Interactions: genetics
%K Host Microbial Interactions: immunology
%K Humans
%K Immunity, Cellular: drug effects
%K Immunity, Humoral: drug effects
%K Immunity, Innate: drug effects
%K Lymphocytes: drug effects
%K Lymphocytes: immunology
%K Lymphocytes: virology
%K Metabolic Networks and Pathways: genetics
%K Metabolic Networks and Pathways: immunology
%K Myeloid Cells: drug effects
%K Myeloid Cells: immunology
%K Myeloid Cells: virology
%K Protein Interaction Mapping
%K SARS-CoV-2: drug effects
%K SARS-CoV-2: genetics
%K SARS-CoV-2: immunology
%K SARS-CoV-2: pathogenicity
%K Signal Transduction
%K Software
%K Transcription Factors: genetics
%K Transcription Factors: immunology
%K Viral Proteins: genetics
%K Viral Proteins: immunology
%K computable knowledge repository (Other)
%K large-scale biocuration (Other)
%K omics data analysis (Other)
%K open access community effort (Other)
%K systems biomedicine (Other)
%K Antiviral Agents (NLM Chemicals)
%K Cytokines (NLM Chemicals)
%K Transcription Factors (NLM Chemicals)
%K Viral Proteins (NLM Chemicals)
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:34664389
%2 pmc:PMC8524328
%R 10.15252/msb.202110387
%U https://pub.dzne.de/record/162909