Molecular Systems Biology (Oct 2021)

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

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

DOI
https://doi.org/10.15252/msb.202110387
Journal volume & issue
Vol. 17, no. 10
pp. 1 – 22

Abstract

Read online

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.

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