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.
| Original language | English |
|---|---|
| Article number | e10387 |
| Number of pages | 22 |
| Journal | Molecular Systems Biology |
| Volume | 17 |
| Issue number | 10 |
| DOIs | |
| Publication status | Published - Oct 2021 |
Keywords
- computable knowledge repository
- large-scale biocuration
- omics data analysis
- open access community effort
- systems biomedicine
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In: Molecular Systems Biology, Vol. 17, No. 10, e10387, 10.2021.
Research output: Contribution to journal › Article › Research › peer-review
TY - JOUR
T1 - COVID19 Disease Map, a computational knowledge repository of virus–host interaction mechanisms
AU - Ostaszewski, Marek
AU - Niarakis, Anna
AU - Mazein, Alexander
AU - Kuperstein, Inna
AU - Phair, Robert
AU - Orta-Resendiz, Aurelio
AU - Singh, Vidisha
AU - Aghamiri, Sara Sadat
AU - Acencio, Marcio Luis
AU - Glaab, Enrico
AU - Ruepp, Andreas
AU - Fobo, Gisela
AU - Montrone, Corinna
AU - Brauner, Barbara
AU - Frishman, Goar
AU - Monraz Gómez, Luis Cristóbal
AU - Somers, Julia
AU - Hoch, Matti
AU - Kumar Gupta, Shailendra
AU - Scheel, Julia
AU - Borlinghaus, Hanna
AU - Czauderna, Tobias
AU - Schreiber, Falk
AU - Montagud, Arnau
AU - Ponce de Leon, Miguel
AU - Funahashi, Akira
AU - Hiki, Yusuke
AU - Hiroi, Noriko
AU - Yamada, Takahiro G.
AU - Dräger, Andreas
AU - Renz, Alina
AU - Naveez, Muhammad
AU - Bocskei, Zsolt
AU - Messina, Francesco
AU - Börnigen, Daniela
AU - Fergusson, Liam
AU - Conti, Marta
AU - Rameil, Marius
AU - Nakonecnij, Vanessa
AU - Vanhoefer, Jakob
AU - Schmiester, Leonard
AU - Wang, Muying
AU - Ackerman, Emily E.
AU - Shoemaker, Jason E.
AU - Zucker, Jeremy
AU - Oxford, Kristie
AU - Teuton, Jeremy
AU - Kocakaya, Ebru
AU - Summak, Gökçe Yağmur
AU - Hanspers, Kristina
AU - Kutmon, Martina
AU - Coort, Susan
AU - Eijssen, Lars
AU - Ehrhart, Friederike
AU - Rex, Devasahayam Arokia Balaya
AU - Slenter, Denise
AU - Martens, Marvin
AU - Pham, Nhung
AU - Haw, Robin
AU - Jassal, Bijay
AU - Matthews, Lisa
AU - Orlic-Milacic, Marija
AU - Senff Ribeiro, Andrea
AU - Rothfels, Karen
AU - Shamovsky, Veronica
AU - Stephan, Ralf
AU - Sevilla, Cristoffer
AU - Varusai, Thawfeek
AU - Ravel, Jean Marie
AU - Fraser, Rupsha
AU - Ortseifen, Vera
AU - Marchesi, Silvia
AU - Gawron, Piotr
AU - Smula, Ewa
AU - Heirendt, Laurent
AU - Satagopam, Venkata
AU - Wu, Guanming
AU - Riutta, Anders
AU - Golebiewski, Martin
AU - Owen, Stuart
AU - Goble, Carole
AU - Hu, Xiaoming
AU - Overall, Rupert W.
AU - Maier, Dieter
AU - Bauch, Angela
AU - Gyori, Benjamin M.
AU - Bachman, John A.
AU - Vega, Carlos
AU - Grouès, Valentin
AU - Vazquez, Miguel
AU - Porras, Pablo
AU - Licata, Luana
AU - Iannuccelli, Marta
AU - Sacco, Francesca
AU - Nesterova, Anastasia
AU - Yuryev, Anton
AU - de Waard, Anita
AU - Turei, Denes
AU - Luna, Augustin
AU - Babur, Ozgun
AU - Soliman, Sylvain
AU - Valdeolivas, Alberto
AU - Esteban-Medina, Marina
AU - Peña-Chilet, Maria
AU - Rian, Kinza
AU - Helikar, Tomáš
AU - Puniya, Bhanwar Lal
AU - Modos, Dezso
AU - Treveil, Agatha
AU - Olbei, Marton
AU - De Meulder, Bertrand
AU - Ballereau, Stephane
AU - Dugourd, Aurélien
AU - Naldi, Aurélien
AU - Noël, Vincent
AU - Calzone, Laurence
AU - Sander, Chris
AU - Demir, Emek
AU - Korcsmaros, Tamas
AU - Freeman, Tom C.
AU - Augé, Franck
AU - Beckmann, Jacques S.
AU - Hasenauer, Jan
AU - Wolkenhauer, Olaf
AU - Wilighagen, Egon L.
AU - Pico, Alexander R.
AU - Evelo, Chris T.
AU - Gillespie, Marc E.
AU - Stein, Lincoln D.
AU - Hermjakob, Henning
AU - D'Eustachio, Peter
AU - Saez-Rodriguez, Julio
AU - Dopazo, Joaquin
AU - Valencia, Alfonso
AU - Kitano, Hiroaki
AU - Barillot, Emmanuel
AU - Auffray, Charles
AU - Balling, Rudi
AU - Schneider, Reinhard
AU - the COVID-19 Disease Map Community
N1 - Funding Information: A. Niarakis collaborates with SANOFI‐AVENTIS R&D via a public–private partnership grant (CIFRE contract, n° 2020/0766). D. Maier and A. Bauch are employed at Biomax Informatics AG and will be affected by any effect of this publication on the commercial version of the AILANI software. J.A. Bachman and B. Gyori received consulting fees from Two Six Labs, LLC. T. Helikar has served as a shareholder and/or has consulted for Discovery Collective, Inc. R. Balling and R. Schneider are founders and shareholders of MEGENO S.A. and ITTM S.A. J. Saez‐Rodriguez receives funding from GSK and Sanofi and consultant fees from Travere Therapeutics. The remaining authors have declared that they have no Conflict of interest. Funding Information: We would like to thank Andjela Tatarovic, architect, and Gina Crovetto, a researcher in the field of cancer, for their help with the design of the top-level view diagrams. We would like to acknowledge the Responsible and Reproducible Research (R3) team of the Luxembourg Centre for Systems Biomedicine for supporting the project and providing necessary communication and data sharing resources. The work presented in this paper was carried out using the ELIXIR Luxembourg tools and services. This study was supported by the Luxembourg National Research Fund (FNR) COVID-19 Fast-Track grant programme, grant COVID-19/2020-1/14715687/CovScreen (E. Glaab); European Commission, INFORE grant H2020-ICT-825070 (A. Montagud, M. Ponce de Leon, M. Vazques and A. Valencia); European Commission, PerMedCoE grant H2020-ICT-951773 (A. Montagud, M. Ponce de Leon, M. Vazques and A. Valencia) the Federal Ministry of Education and Research (BMBF, Germany) and the Baden-W?rttemberg Ministry of Science, the Excellence Strategy of the German Federal and State Governments (A. Renz); German Center for Infection Research (DZIF), grant no 8020708703 (A. Dr?ger); The Netherlands Organisation for Health Research and Development (ZonMw), grant no 10430012010015, (M. Kutmon, S. Coort, F. Ehrhart, N. Pham, E.L. Willighagen, C.T. Evelo); H2020 Marie Sk?odowska-Curie Actions, grant number 765274 (J. Scheel); National Institutes of Health, USA (NIH), grant number U41 HG003751 (L.D. Stein). The development of Reactome is supported by grants from the US National Institutes of Health (U41 HG003751) and the European Molecular Biology Laboratory. Funding Information: We would like to thank Andjela Tatarovic, architect, and Gina Crovetto, a researcher in the field of cancer, for their help with the design of the top‐level view diagrams. We would like to acknowledge the Responsible and Reproducible Research (R3) team of the Luxembourg Centre for Systems Biomedicine for supporting the project and providing necessary communication and data sharing resources. The work presented in this paper was carried out using the ELIXIR Luxembourg tools and services. This study was supported by the Luxembourg National Research Fund (FNR) COVID‐19 Fast‐Track grant programme, grant COVID‐19/2020‐1/14715687/CovScreen (E. Glaab); European Commission, INFORE grant H2020‐ICT‐825070 (A. Montagud, M. Ponce de Leon, M. Vazques and A. Valencia); European Commission, PerMedCoE grant H2020‐ICT‐951773 (A. Montagud, M. Ponce de Leon, M. Vazques and A. Valencia) the Federal Ministry of Education and Research (BMBF, Germany) and the Baden‐Württemberg Ministry of Science, the Excellence Strategy of the German Federal and State Governments (A. Renz); German Center for Infection Research (DZIF), grant no 8020708703 (A. Dräger); The Netherlands Organisation for Health Research and Development (ZonMw), grant no 10430012010015, (M. Kutmon, S. Coort, F. Ehrhart, N. Pham, E.L. Willighagen, C.T. Evelo); H2020 Marie Skłodowska‐Curie Actions, grant number 765274 (J. Scheel); National Institutes of Health, USA (NIH), grant number U41 HG003751 (L.D. Stein). The development of Reactome is supported by grants from the US National Institutes of Health (U41 HG003751) and the European Molecular Biology Laboratory. Publisher Copyright: © 2021 The Authors. Published under the terms of the CC BY 4.0 license
PY - 2021/10
Y1 - 2021/10
N2 - 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.
AB - 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.
KW - computable knowledge repository
KW - large-scale biocuration
KW - omics data analysis
KW - open access community effort
KW - systems biomedicine
UR - https://www.scopus.com/pages/publications/85118262224
U2 - 10.15252/msb.202110387
DO - 10.15252/msb.202110387
M3 - Article
AN - SCOPUS:85118262224
SN - 1744-4292
VL - 17
JO - Molecular Systems Biology
JF - Molecular Systems Biology
IS - 10
M1 - e10387
ER -