COVID19 Disease Map, a computational knowledge repository of virus–host interaction mechanisms. Issue 10 (19th October 2021)

• Record Type: Journal Article
• Authors: 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; 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;
• Is Part Of: Molecular systems biology. Volume 17:Issue 10(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-10-19
• Subjects: computable knowledge repository -- large‐scale biocuration -- omics data analysis -- open access community effort -- systems biomedicine; Molecular biology -- Periodicals; Systems biology -- Periodicals; 572.8
• DOI: 10.15252/msb.202110387
• Languages: English
• ISSNs: 1744-4292

Abstract

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. SYNOPSIS: COVID‐19 Disease Map is a large‐scale collection of curated computational models and diagrams of molecular mechanisms involved in SARS‐CoV‐2 infection. The map supports the computational exploration of pathways affected by the virus. COVID‐19 Disease Map was built by over 20 independent biocuration teams and harmonised using systems biology standards. Biocuration efforts were assisted by the systematic use of text‐ and AI‐assisted mining of relevant bioinformatic databases and platforms. Case studies illustrate the applications of the map for visual exploration and computational analysis of SARS‐CoV‐2 pathways in combination with omic data. The map is an open‐access effort, with all content and code shared in public repositories. Abstract : COVID‐19 Disease Map is a large‐scale collection of curated computational models and diagrams of molecular mechanisms involved in SARS‐CoV‐2 infection. The map supports the computational exploration of pathways affected by the virus.