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Computational strategies to combat COVID-19: useful tools to accelerate SARS-CoV-2 and coronavirus research.

ABSTRACT: SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) is a novel virus of the family Coronaviridae. The virus causes the infectious disease COVID-19. The biology of coronaviruses has been studied for many years. However, bioinformatics tools designed explicitly for SARS-CoV-2 have only recently been developed as a rapid reaction to the need for fast detection, understanding and treatment of COVID-19. To control the ongoing COVID-19 pandemic, it is of utmost importance to get insight into the evolution and pathogenesis of the virus. In this review, we cover bioinformatics workflows and tools for the routine detection of SARS-CoV-2 infection, the reliable analysis of sequencing data, the tracking of the COVID-19 pandemic and evaluation of containment measures, the study of coronavirus evolution, the discovery of potential drug targets and development of therapeutic strategies. For each tool, we briefly describe its use case and how it advances research specifically for SARS-CoV-2. All tools are free to use and available online, either through web applications or public code repositories. Contact:evbc@unj-jena.de.

SUBMITTER: Hufsky F

PROVIDER: S-EPMC7665365 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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Computational strategies to combat COVID-19: useful tools to accelerate SARS-CoV-2 and coronavirus research.

Hufsky Franziska F Lamkiewicz Kevin K Almeida Alexandre A Aouacheria Abdel A Arighi Cecilia C Bateman Alex A Baumbach Jan J Beerenwinkel Niko N Brandt Christian C Cacciabue Marco M Chuguransky Sara S Drechsel Oliver O Finn Robert D RD Fritz Adrian A Fuchs Stephan S Hattab Georges G Hauschild Anne-Christin AC Heider Dominik D Hoffmann Marie M Hölzer Martin M Hoops Stefan S Kaderali Lars L Kalvari Ioanna I von Kleist Max M Kmiecinski Renó R Kühnert Denise D Lasso Gorka G Libin Pieter P List Markus M Löchel Hannah F HF Martin Maria J MJ Martin Roman R Matschinske Julian J McHardy Alice C AC Mendes Pedro P Mistry Jaina J Navratil Vincent V Nawrocki Eric P EP O'Toole Áine Niamh ÁN Ontiveros-Palacios Nancy N Petrov Anton I AI Rangel-Pineros Guillermo G Redaschi Nicole N Reimering Susanne S Reinert Knut K Reyes Alejandro A Richardson Lorna L Robertson David L DL Sadegh Sepideh S Singer Joshua B JB Theys Kristof K Upton Chris C Welzel Marius M Williams Lowri L Marz Manja M

Briefings in bioinformatics 20210301 2

SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) is a novel virus of the family Coronaviridae. The virus causes the infectious disease COVID-19. The biology of coronaviruses has been studied for many years. However, bioinformatics tools designed explicitly for SARS-CoV-2 have only recently been developed as a rapid reaction to the need for fast detection, understanding and treatment of COVID-19. To control the ongoing COVID-19 pandemic, it is of utmost importance to get insight into ...[more]

PMID: 33147627

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Project description:Chemical respiratory sensitization is an immunological process that manifests clinically mostly as occupational asthma and is responsible for 1 in 6 cases of adult asthma, although this may be an underestimate of the prevalence, as it is under-diagnosed. Occupational asthma results in unemployment for roughly one-third of those affected due to severe health issues. Despite its high prevalence, chemical respiratory sensitization is difficult to predict, as there are currently no validated models and the mechanisms are not entirely understood, creating a significant challenge for regulatory bodies and industry alike. The Adverse Outcome Pathway (AOP) for respiratory sensitization is currently incomplete. However, some key events have been identified, and there is overlap with the comparatively well-characterized AOP for dermal sensitization. Because of this, and the fact that dermal sensitization is often assessed by in vivo, in chemico, or in silico methods, regulatory bodies are defaulting to the dermal sensitization status of chemicals as a proxy for respiratory sensitization status when evaluating chemical safety. We identified a data set of known human respiratory sensitizers, which we used to investigate the accuracy of a structural alert model, Toxtree, designed for skin sensitization and the Centre for Occupational and Environmental Health (COEH)'s model, a model developed specifically for occupational asthma. While both models had a reasonable level of accuracy, the COEH model achieved the highest balanced accuracy at 76%; when the models agreed, the overall accuracy was 87%. There were important differences between the models: Toxtree had superior performance for some structural alerts and some categories of well-characterized skin sensitizers, while the COEH model had high accuracy in identifying sensitizers that lacked identified skin sensitization reactivity domains. Overall, both models achieved respectable accuracy. However, neither model addresses potency, which, along with data quality, remains a hurdle, and the field must prioritize these issues to move forward.

Dataset Information

Computational strategies to combat COVID-19: useful tools to accelerate SARS-CoV-2 and coronavirus research.

Publications

Computational strategies to combat COVID-19: useful tools to accelerate SARS-CoV-2 and coronavirus research.

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