Open AccessCOVID19 Disease Map, a computational knowledge repository of virus–host interaction mechanisms
Author(s) -
Ostaszewski Marek,
Niarakis Anna,
Mazein Alexander,
Kuperstein Inna,
Phair Robert,
OrtaResendiz 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,
OrlicMilacic Marija,
Senff-Ribeiro Andrea,
Rothfels Karen,
Shamovsky Veronica,
Stephan Ralf,
Sevilla Cristoffer,
Varusai Thawfeek,
Ravel JeanMarie,
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,
EstebanMedina Marina,
PeñaChilet 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,
SaezRodriguez Julio,
Dopazo Joaquin,
Valencia Alfonso,
Kitano Hiroaki,
Barillot Emmanuel,
Auffray Charles,
Balling Rudi,
Schneider Reinhard
Publication year - 2021
Publication title -
molecular systems biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 8.523
H-Index - 148
ISSN - 1744-4292
DOI - 10.15252/msb.202110387
Subject(s) - interoperability , data science , computer science , representation (politics) , resource (disambiguation) , infectious disease (medical specialty) , disease , biology , computational biology , world wide web , computer network , politics , political science , law , medicine , pathology
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.