English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Computational strategies to combat COVID-19: useful tools to accelerate SARS-CoV-2 and Coronavirus research

Hufsky, F., Lamkiewicz, K., Almeida, A., Aouacheria, A., Arighi, C., Bateman, A., et al. (2020). Computational strategies to combat COVID-19: useful tools to accelerate SARS-CoV-2 and Coronavirus research. Briefings in Bioinformatics, bbaa232. doi:10.1093/bib/bbaa232.

Item is

Basic

show hide
Genre: Journal Article

Files

show Files
hide Files
:
shh2710.pdf (Publisher version), 3MB
Name:
shh2710.pdf
Description:
OA
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
-
:
shh2710pre.pdf (Preprint), 7MB
Name:
shh2710pre.pdf
Description:
OA . - preprint.org
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
-

Locators

show
hide
Locator:
bioinformatics tools (Supplementary material)
Description:
collected list of bioinformatics tools designed explicitly for SARS-CoV-2 and coronaviruses

Creators

show
hide
 Creators:
Hufsky, Franziska, Author
Lamkiewicz, Kevin, Author
Almeida, Alexandre, Author
Aouacheria, Abdel, Author
Arighi, Cecilia, Author
Bateman, Alex, Author
Baumbach, Jan, Author
Beerenwinkel, Niko, Author
Brandt, Christian, Author
Cacciabue, Marco, Author
Chuguransky, Sara, Author
Drechsel, Oliver, Author
Finn, Robert D., Author
Fritz, Adrian, Author
Fuchs, Stephan, Author
Hattab, Georges, Author
Hauschild, Anne-Christin, Author
Heider, Dominik, Author
Hoffmann, Marie, Author
Hölzer, Martin, Author
Hoops, Stefan, AuthorKaderali, Lars, AuthorKalvari, Ioanna, Authorvon Kleist, Max, AuthorKmiecinski, René, AuthorKühnert, Denise1, Author              Lasso, Gorka, AuthorLibin, Pieter, AuthorList, Markus, AuthorLöchel, Hannah F., AuthorMartin, Maria J., AuthorMartin, Roman, AuthorMatschinske, Julian, AuthorMcHardy, Alice C., AuthorMendes, Pedro, AuthorMistry, Jaina, AuthorNavratil, Vincent, AuthorNawrocki, Eric, AuthorO'Toole, Áine Niamh, AuthorPalacios-Ontiveros, Nancy, AuthorPetrov, Anton I., AuthorRangel-Piñeros, Guillermo, AuthorRedaschi, Nicole, AuthorReimering, Susanne, AuthorReinert, Knut, AuthorReyes, Alejandro, AuthorRichardson, Lorna, AuthorRobertson, David L., AuthorSadegh, Sepideh, AuthorSinger, Joshua B., AuthorTheys, Kristof, AuthorUpton, Chris, AuthorWelzel, Marius, AuthorWilliams, Lowri, AuthorMarz, Manja, Author more..
Affiliations:
1tide, Max Planck Institute for the Science of Human History, Max Planck Society, ou_2591691              

Content

show
hide
Free keywords: virus bioinformatics, SARS-CoV-2, sequencing, epidemiology, drug design, tools
 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 freely available online, either through web applications or public code repositories.

Details

show
hide
Language(s): eng - English
 Dates: 2020-11-04
 Publication Status: Published online
 Pages: 22
 Publishing info: -
 Table of Contents: 1 Introduction

2 Detection and annotation
2.1 PriSeT: Primer Search Tool
2.2 CoVPipe: Amplicon-based genome reconstruction
2.3 poreCov: Rapid sample analysis for nanopore sequencing
2.4 VADR: SARS-CoV-2 genome annotation and validation
2.5 V-Pipe: Calling single-nucleotide variants and viral haplotypes
2.6 Haploflow: Multi-strain aware de novo assembly
2.7 VIRify: Annotation of viruses in meta-omic data
2.8 Genome analysis tools by VBRC
2.9 VIRULIGN: Codon-correct multiple sequence alignments
2.10 Rfam COVID-19 Resources: Coronavirusspecific RNA families
2.11 UniProt COVID-19 protein portal: rapid access to protein information
2.12 Pfam protein families database

3 Tracking, epidemiology and evolution
3.1 Covidex: Alignment-free subtyping using machine learning
3.2 Pangolin: Phylogenetic Assignment of Named Global Outbreak LINeages
3.3 BEAST2: Phylodynamics based on Bayesian inference
3.4 Phylogeographic reconstruction using air transportation data
3.5 COPASI: Modeling SARS-CoV-2 dynamics with differential equations
3.6 COVIDSIM: Epidemiological models of viral spread
3.7 CoV-GLUE: tracking nucleotide changes in the SARS-CoV-2 genome
3.8 PoSeiDon: Positive Selection Detection and Recombination Analysis

4 Drug design
4.1 VirHostNet SARS-CoV-2 release
4.2 CORDITE: CORona Drug InTERactions database
4.3 CoVex: CoronaVirus Explorer
4.4 P-HIPSTer: a virus-host protein-protein interaction resource

5 Concluding remarks
 Rev. Type: Peer
 Identifiers: DOI: 10.1093/bib/bbaa232
DOI: 10.20944/preprints202005.0376.v1
Other: shh2710
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Briefings in Bioinformatics
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: London : H. Stewart Publications
Pages: - Volume / Issue: - Sequence Number: bbaa232 Start / End Page: - Identifier: ISSN: 1467-5463
CoNE: https://pure.mpg.de/cone/journals/resource/974392606063

Source 2

show
hide
Title: Preprints.org
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Basel : MPDI
Pages: - Volume / Issue: - Sequence Number: 0376.v1 Start / End Page: - Identifier: URN: https://www.preprints.org/