학술논문
COVID19 Disease Map, a computational knowledge repository of virus–host interaction mechanisms
Document Type
Report
Author
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; Gómez, Luis Cristóbal Monraz; Somers, Julia; Hoch, Matti; Gupta, Shailendra Kumar; Scheel, Julia; Borlinghaus, Hanna; Czauderna, Tobias; Schreiber, Falk; Montagud, Arnau; De Leon, Miguel Ponce; 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; Summak, Gökçe Yağmur; Hanspers, Kristina; Kutmon, Martina; Coort, Susan; Eijssen, Lars; Ehrhart, Friederike; Rex, Devasahayam Arokia Balaya; Slenter, Denise; Martens, Marvin; Pham, Nhung; Haw, Robin; Jassal, Bijay; Matthews, Lisa; Orlic‐Milacic, Marija; Senff-Ribeiro, Andrea; 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; Puniya, Bhanwar Lal; 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; Willighagen, 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
Source
Molecular Systems Biology. October 2021, Vol. 17 Issue 10
Subject
Language
English
Abstract
Introduction The coronavirus disease 2019 (COVID‐19) pandemic due to severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) has already resulted in the infection of over 250 million people worldwide, of whom [...]
: 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.
: 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.