COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms
Archive ouverte
Ostaszewski, Marek | Niarakis, Anna | Mazein, Alexander | Kuperstein, Inna | Phair, Robert | Orta-Resendiz, Aurelio | Singh, Vidisha | Aghamiri, Sara, Sadat | Acencio, Marcio, Luis | Glaab, Enrico | Ruepp, Andreas | Fobo, Gisela | Montrone, Corinna | Brauner, Barbara | Frishman, Goar | Monraz Gómez, Luis Cristóbal | Somers, Julia | Hoch, Matti | Kumar Gupta, Shailendra | Scheel, Julia | Borlinghaus, Hanna | Czauderna, Tobias | Schreiber, Falk | Montagud, Arnau | Ponce de Leon, Miguel | Funahashi, Akira | Hiki, Yusuke | Hiroi, Noriko | Yamada, Takahiro, G | Dräger, Andreas | Renz, Alina | Naveez, Muhammad | Bocskei, Zsolt | Messina, Francesco | Börnigen, Daniela | Fergusson, Liam | Conti, Marta | Rameil, Marius | Nakonecnij, Vanessa | Vanhoefer, Jakob | Schmiester, Leonard | Wang, Muying | Ackerman, Emily, E | Shoemaker, Jason, E | Zucker, Jeremy | Oxford, Kristie | Teuton, Jeremy | Kocakaya, Ebru | Ya Gmur Summak, Gökçe | Hanspers, Kristina | Kutmon, Martina | Coort, Susan | Eijssen, Lars | Ehrhart, Friederike | Arokia, Devasahayam | Rex, Balaya | Slenter, Denise | Martens, Marvin | Pham, Nhung | Haw, Robin | Jassal, Bijay | Matthews, Lisa | Orlic-Milacic, Marija | Ribeiro, Andrea, Senff | Rothfels, Karen | Shamovsky, Veronica | Stephan, Ralf | Sevilla, Cristoffer | Varusai, Thawfeek | Ravel, Jean-Marie | Fraser, Rupsha | Ortseifen, Vera | Marchesi, Silvia | Gawron, Piotr | Smula, Ewa | Heirendt, Laurent | Satagopam, Venkata | Wu, Guanming | Riutta, Anders | Golebiewski, Martin | Owen, Stuart | Goble, Carole | Hu, Xiaoming | Overall, Rupert, W | Maier, Dieter | Bauch, Angela | Gyori, Benjamin, M | Bachman, John, A | Vega, Carlos | Grouès, Valentin | Vazquez, Miguel | Porras, Pablo | Licata, Luana | Iannuccelli, Marta | Sacco, Francesca | Nesterova, Anastasia | Yuryev, Anton | de Waard, Anita | Turei, Denes | Luna, Augustin | Babur, Ozgun | Soliman, Sylvain | Valdeolivas, Alberto | Esteban-Medina, Marina | Peña-Chilet, Maria | Rian, Kinza | Helikar, Tomáš | Lal Puniya, Bhanwar | Modos, Dezso | Treveil, Agatha | Olbei, Marton | de Meulder, Bertrand | Ballereau, Stephane | Dugourd, Aurélien | Naldi, Aurélien | Noël, Vincent | Calzone, Laurence | Sander, Chris | Demir, Emek | Korcsmaros, Tamas | Freeman, Tom, C | Augé, Franck | Beckmann, Jacques, S | Hasenauer, Jan | Wolkenhauer, Olaf | Wilighagen, Egon, L | Pico, Alexander, R | Evelo, Chris, T | Gillespie, Marc, E | Stein, Lincoln, D | Hermjakob, Henning | d'Eustachio, Peter | Saez-Rodriguez, Julio | Dopazo, Joaquin | Valencia, Alfonso | Kitano, Hiroaki | Barillot, Emmanuel | Auffray, Charles | Balling, Rudi | Schneider, Reinhard | Disease Map Community, Covid-19
Edité par
CCSD ; EMBO Press -
International audience.
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.
