COVID-19 Disease Map, a computational knowledge repository of SARS-CoV-2 virus-host interaction mechanisms

  1. Marek Ostaszewski
  2. Anna Niarakis
  3. Alexander Mazein
  4. Inna Kuperstein
  5. Robert Phair
  6. Aurelio Orta-Resendiz
  7. Vidisha Singh
  8. Sara Sadat Aghamiri
  9. Marcio Luis Acencio
  10. Enrico Glaab
  11. Andreas Ruepp
  12. Gisela Fobo
  13. Corinna Montrone
  14. Barbara Brauner
  15. Goar Frishman
  16. Luis Cristóbal Monraz Gómez
  17. Julia Somers
  18. Matti Hoch
  19. Shailendra Kumar Gupta
  20. Julia Scheel
  21. Hanna Borlinghaus
  22. Tobias Czauderna
  23. Falk Schreiber
  24. Arnau Montagud
  25. Miguel Ponce de Leon
  26. Akira Funahashi
  27. Yusuke Hiki
  28. Noriko Hiroi
  29. Takahiro G. Yamada
  30. Andreas Dräger
  31. Alina Renz
  32. Muhammad Naveez
  33. Zsolt Bocskei
  34. Francesco Messina
  35. Daniela Börnigen
  36. Liam Fergusson
  37. Marta Conti
  38. Marius Rameil
  39. Vanessa Nakonecnij
  40. Jakob Vanhoefer
  41. Leonard Schmiester
  42. Muying Wang
  43. Emily E. Ackerman
  44. Jason Shoemaker
  45. Jeremy Zucker
  46. Kristie Oxford
  47. Jeremy Teuton
  48. Ebru Kocakaya
  49. Gökçe Yağmur Summak
  50. Kristina Hanspers
  51. Martina Kutmon
  52. Susan Coort
  53. Lars Eijssen
  54. Friederike Ehrhart
  55. D. A. B. Rex
  56. Denise Slenter
  57. Marvin Martens
  58. Nhung Pham
  59. Robin Haw
  60. Bijay Jassal
  61. Lisa Matthews
  62. Marija Orlic-Milacic
  63. Andrea Senff Ribeiro
  64. Karen Rothfels
  65. Veronica Shamovsky
  66. Ralf Stephan
  67. Cristoffer Sevilla
  68. Thawfeek Varusai
  69. Jean-Marie Ravel
  70. Rupsha Fraser
  71. Vera Ortseifen
  72. Silvia Marchesi
  73. Piotr Gawron
  74. Ewa Smula
  75. Laurent Heirendt
  76. Venkata Satagopam
  77. Guanming Wu
  78. Anders Riutta
  79. Martin Golebiewski
  80. Stuart Owen
  81. Carole Goble
  82. Xiaoming Hu
  83. Rupert W. Overall
  84. Dieter Maier
  85. Angela Bauch
  86. Benjamin M. Gyori
  87. John A. Bachman
  88. Carlos Vega
  89. Valentin Grouès
  90. Miguel Vazquez
  91. Pablo Porras
  92. Luana Licata
  93. Marta Iannuccelli
  94. Francesca Sacco
  95. Anastasia Nesterova
  96. Anton Yuryev
  97. Anita de Waard
  98. Denes Turei
  99. Augustin Luna
  100. Ozgun Babur
  101. Sylvain Soliman
  102. Alberto Valdeolivas
  103. Marina Esteban- Medina
  104. Maria Peña-Chilet
  105. Kinza Rian
  106. Tomáš Helikar
  107. Bhanwar Lal Puniya
  108. Dezso Modos
  109. Agatha Treveil
  110. Marton Olbei
  111. Bertrand De Meulder
  112. Aurélien Dugourd
  113. Aurélien Naldi
  114. Vincent Noë
  115. Laurence Calzone
  116. Chris Sander
  117. Emek Demir
  118. Tamas Korcsmaros
  119. Tom C. Freeman
  120. Franck Augé
  121. Jacques S. Beckmann
  122. Jan Hasenauer
  123. Olaf Wolkenhauer
  124. Egon L. Wilighagen
  125. Alexander R. Pico
  126. Chris T. Evelo
  127. Marc E. Gillespie
  128. Lincoln D. Stein
  129. Henning Hermjakob
  130. Peter D’Eustachio
  131. Julio Saez-Rodriguez
  132. Joaquin Dopazo
  133. Alfonso Valencia
  134. Hiroaki Kitano
  135. Emmanuel Barillot
  136. Charles Auffray
  137. Rudi Balling
  138. Reinhard Schneider
  139. the COVID-19 Disease Map Community

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Abstract

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. We discuss the tools, platforms, and guidelines necessary for the distributed development of its contents by a multi-faceted community of biocurators, domain experts, bioinformaticians, and computational biologists. We highlight the role of relevant databases and text mining approaches in enrichment and validation of the curated mechanisms. We describe the contents of the Map and their relevance to the molecular pathophysiology of COVID-19 and the analytical and computational modelling approaches that can be applied for mechanistic data interpretation and predictions. We conclude by demonstrating concrete applications of our work through several use cases and highlight new testable hypotheses.

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  1. Version published to 10.1101/2020.10.26.356014v3 on bioRxiv
  2. Version published to 10.1101/2020.10.26.356014v2 on bioRxiv
  3. ScreenIT

    SciScore for 10.1101/2020.10.26.356014: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  4. ScreenIT

    SciScore for 10.1101/2020.10.26.356014: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.Randomizationnot detected.Blindingnot detected.Power Analysisnot detected.Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Technische Universität München, Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, 85748
    Mathematical Modeling of Biological Systems
    suggested: (National University of Singapore, Computational Bioengineering Laboratory, RRID:SCR_000284)
    3] Interaction No Yes No No Elsevier Pathway Collection14 Pathway Yes Yes Yes Yes9 10 https://www.semanticscholar.org/cord19/download (accessed on 20.10.2020) 11 https://data.mendeley.com/datasets/h9vs5s8fz2/draft?a=f40961bb-9798-4fd1-8025-e2a3ba47b02e 12 https://www.imexconsortium.org 13 https://signor.uniroma2.it/covid/ 14 https://pathwaystudio.com bioRxiv preprint doi: https://doi.org/10.1101/2020.10.26.356014; this version posted October 28, 2020.
    https://www.imexconsortium.org
    suggested: (IMEx - The International Molecular Exchange Consortium, RRID:SCR_002805)
    This pipeline was applied to the CORD-19 dataset and the collection of MEDLINE abstracts associated with the genes in the SARS-CoV-2 PPI network [34] using the Entrez Gene Reference-Into-Function (GeneRIF).
    Entrez Gene
    suggested: (Entrez Gene, RRID:SCR_002473)
    Finally, INDRA 15 https://biokb.lcsb.uni.lu 16 https://ailani.ai/cgi/login_bioxm_portal.cgi 17 https://git-r3lab.uni.lu/covid/models/-/tree/master/Resources/Text%20mining 18 https://opennlp.apache.org 19 https://rupertoverall.net/covidminer bioRxiv preprint doi: https://doi.org/10.1101/2020.10.26.356014; this version posted October 28, 2020.
    bioRxiv
    suggested: (bioRxiv, RRID:SCR_003933)
    These can then be used directly for activity simulations using the BioLayout network analysis tool [309].
    BioLayout
    suggested: (BioLayout Express 3D, RRID:SCR_007179)

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  5. Version published to 10.1101/2020.10.26.356014v1 on bioRxiv