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

• Published in: Molecular systems biology Vol. 17; no. 10; pp. e10387 - n/a
• Main Authors: Ostaszewski, Marek, Mazein, Alexander, Phair, Robert, Orta‐Resendiz, Aurelio, Singh, Vidisha, Aghamiri, Sara Sadat, Acencio, Marcio Luis, Glaab, Enrico, Ruepp, Andreas, Brauner, Barbara, Frishman, Goar, Somers, Julia, Hoch, Matti, Kumar Gupta, Shailendra, Borlinghaus, Hanna, Czauderna, Tobias, Hiki, Yusuke, Yamada, Takahiro G, Renz, Alina, Naveez, Muhammad, Bocskei, Zsolt, Börnigen, Daniela, Conti, Marta, Rameil, Marius, Vanhoefer, Jakob, Schmiester, Leonard, Wang, Muying, Ackerman, Emily E, Shoemaker, Jason E, Oxford, Kristie, Kocakaya, Ebru, Hanspers, Kristina, Kutmon, Martina, Coort, Susan, Eijssen, Lars, Ehrhart, Friederike, Slenter, Denise, Pham, Nhung, Haw, Robin, Jassal, Bijay, Matthews, Lisa, Senff-Ribeiro, Andrea, 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, Yuryev, Anton, de Waard, Anita, Babur, Ozgun, Soliman, Sylvain, Esteban‐Medina, Marina, Peña‐Chilet, Maria, Rian, Kinza, Helikar, Tomáš, Puniya, Bhanwar Lal, Modos, Dezso, Treveil, Agatha, Dugourd, Aurélien, Noël, Vincent, Calzone, Laurence, Demir, Emek, Augé, Franck, Hasenauer, Jan, Wolkenhauer, Olaf, Willighagen, Egon L, Evelo, Chris T, Stein, Lincoln D, Saez‐Rodriguez, Julio, Dopazo, Joaquin, Valencia, Alfonso, Kitano, Hiroaki, Auffray, Charles, Balling, Rudi, Schneider, Reinhard
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.10.2021; EMBO Press; Wiley; John Wiley and Sons Inc; Springer Nature
• Subjects: Antiviral Agents - therapeutic use; Automation; BASIC BIOLOGICAL SCIENCES; Biology; Community; computable knowledge repository; Computational Biology - methods; Computer applications; Computer Graphics; Coronaviruses; COVID-19; COVID-19 - drug therapy; COVID-19 - genetics; COVID-19 - immunology; COVID-19 - virology; Cytokines - genetics; Cytokines - immunology; Data interpretation; Data mining; Data Mining - statistics & numerical data; Databases, Factual; Drug development; EMBO10; EMBO23; Gene Expression Regulation; Graphical representations; Host Microbial Interactions - genetics; Host Microbial Interactions - immunology; Humans; Immunity, Cellular - drug effects; Immunity, Humoral - drug effects; Immunity, Innate - drug effects; Interfaces; Knowledge; large-scale biocuration; Life Sciences; Lymphocytes - drug effects; Lymphocytes - immunology; Lymphocytes - virology; Medical research; Metabolic Networks and Pathways - genetics; Metabolic Networks and Pathways - immunology; Molecular modelling; Myeloid Cells - drug effects; Myeloid Cells - immunology; Myeloid Cells - virology; Network analysis; omics data analysis; open access community effort; Pandemics; Protein Interaction Mapping; Proteins; Repositories; SARS-CoV-2 - drug effects; SARS-CoV-2 - genetics; SARS-CoV-2 - immunology; SARS-CoV-2 - pathogenicity; Severe acute respiratory syndrome; Severe acute respiratory syndrome coronavirus 2; Signal Transduction; Software; Subject specialists; systems biomedicine; Transcription Factors - genetics; Transcription Factors - immunology; Viral diseases; Viral Proteins - genetics; Viral Proteins - immunology; Viruses; omics data analysis; large‐scale biocuration; open access community effort; computable knowledge repository; systems biomedicine; large-scale biocuration; Virology & Host Pathogen Interaction; systems biomedicine Subject Categories Computational Biology; Microbiology
• ISSN: 1744-4292; 1744-4292
• DOI: 10.15252/msb.202110387

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. Graphical 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.