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Article ; Online: LIRcentral: a manually curated online database of experimentally validated functional LIR motifs.

Chatzichristofi, Agathangelos / Sagris, Vasileios / Pallaris, Aristos / Eftychiou, Marios / Kalvari, Ioanna / Price, Nicholas / Theodosiou, Theodosios / Iliopoulos, Ioannis / Nezis, Ioannis P / Promponas, Vasilis J

Autophagy

2023 Volume 19, Issue 12, Page(s) 3189–3200

Abstract: Several selective macroautophagy receptor and adaptor proteins bind members of the Atg8 (autophagy related 8) family using short linear motifs (SLiMs), most often referred to as Atg8-family interacting motifs (AIMs) or LC3-interacting regions (LIRs). AIM/ ...

Abstract	Several selective macroautophagy receptor and adaptor proteins bind members of the Atg8 (autophagy related 8) family using short linear motifs (SLiMs), most often referred to as Atg8-family interacting motifs (AIMs) or LC3-interacting regions (LIRs). AIM/LIR motifs have been extensively studied during the last fifteen years, since they can uncover the underlying biological mechanisms and possible substrates for this key catabolic process of eukaryotic cells. Prompted by the fact that experimental information regarding LIR motifs can be found scattered across heterogeneous literature resources, we have developed LIRcentral (https://lircentral.eu), a freely available online repository for user-friendly access to comprehensive, high-quality information regarding LIR motifs from manually curated publications. Herein, we describe the development of LIRcentral and showcase currently available data and features, along with our plans for the expansion of this resource. Information incorporated in LIRcentral is useful for accomplishing a variety of research tasks, including: (i) guiding wet biology researchers for the characterization of novel instances of LIR motifs, (ii) giving bioinformaticians/computational biologists access to high-quality LIR motifs for building novel prediction methods for LIR motifs and LIR containing proteins (LIRCPs) and (iii) performing analyses to better understand the biological importance/features of functional LIR motifs. We welcome feedback on the LIRcentral content and functionality by all interested researchers and anticipate this work to spearhead a community effort for sustaining this resource which will further promote progress in studying LIR motifs/LIRCPs.
MeSH term(s)	Autophagy-Related Protein 8 Family/metabolism ; Microtubule-Associated Proteins/metabolism ; Autophagy/physiology ; Amino Acid Motifs ; Carrier Proteins/metabolism
Chemical Substances	Autophagy-Related Protein 8 Family ; Microtubule-Associated Proteins ; Carrier Proteins
Language	English
Publishing date	2023-08-02
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2454135-7
ISSN	1554-8635 ; 1554-8627
ISSN (online)	1554-8635
ISSN	1554-8627
DOI	10.1080/15548627.2023.2235851
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Non-Coding RNA Analysis Using the Rfam Database.

Kalvari, Ioanna / Nawrocki, Eric P / Argasinska, Joanna / Quinones-Olvera, Natalia / Finn, Robert D / Bateman, Alex / Petrov, Anton I

Current protocols in bioinformatics

2018 Volume 62, Issue 1, Page(s) e51

Abstract: Rfam is a database of non-coding RNA families in which each family is represented by a multiple sequence alignment, a consensus secondary structure, and a covariance model. Using a combination of manual and literature-based curation and a custom software ...

Abstract	Rfam is a database of non-coding RNA families in which each family is represented by a multiple sequence alignment, a consensus secondary structure, and a covariance model. Using a combination of manual and literature-based curation and a custom software pipeline, Rfam converts descriptions of RNA families found in the scientific literature into computational models that can be used to annotate RNAs belonging to those families in any DNA or RNA sequence. Valuable research outputs that are often locked up in figures and supplementary information files are encapsulated in Rfam entries and made accessible through the Rfam Web site. The data produced by Rfam have a broad application, from genome annotation to providing training sets for algorithm development. This article gives an overview of how to search and navigate the Rfam Web site, and how to annotate sequences with RNA families. The Rfam database is freely available at http://rfam.org. © 2018 by John Wiley & Sons, Inc.
MeSH term(s)	Base Sequence ; Databases, Nucleic Acid ; Genome, Human ; Humans ; Molecular Sequence Annotation ; Nucleic Acid Conformation ; RNA, Untranslated/chemistry ; RNA, Untranslated/genetics ; Riboswitch/genetics ; Sequence Alignment ; Sequence Analysis, RNA
Chemical Substances	RNA, Untranslated ; Riboswitch
Language	English
Publishing date	2018-06-05
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Intramural ; Research Support, Non-U.S. Gov't
ISSN	1934-340X
ISSN (online)	1934-340X
DOI	10.1002/cpbi.51
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Rfam 14: expanded coverage of metagenomic, viral and microRNA families.

Kalvari, Ioanna / Nawrocki, Eric P / Ontiveros-Palacios, Nancy / Argasinska, Joanna / Lamkiewicz, Kevin / Marz, Manja / Griffiths-Jones, Sam / Toffano-Nioche, Claire / Gautheret, Daniel / Weinberg, Zasha / Rivas, Elena / Eddy, Sean R / Finn, Robert D / Bateman, Alex / Petrov, Anton I

Nucleic acids research

2020 Volume 49, Issue D1, Page(s) D192–D200

Abstract: Rfam is a database of RNA families where each of the 3444 families is represented by a multiple sequence alignment of known RNA sequences and a covariance model that can be used to search for additional members of the family. Recent developments have ... ...

Abstract	Rfam is a database of RNA families where each of the 3444 families is represented by a multiple sequence alignment of known RNA sequences and a covariance model that can be used to search for additional members of the family. Recent developments have involved expert collaborations to improve the quality and coverage of Rfam data, focusing on microRNAs, viral and bacterial RNAs. We have completed the first phase of synchronising microRNA families in Rfam and miRBase, creating 356 new Rfam families and updating 40. We established a procedure for comprehensive annotation of viral RNA families starting with Flavivirus and Coronaviridae RNAs. We have also increased the coverage of bacterial and metagenome-based RNA families from the ZWD database. These developments have enabled a significant growth of the database, with the addition of 759 new families in Rfam 14. To facilitate further community contribution to Rfam, expert users are now able to build and submit new families using the newly developed Rfam Cloud family curation system. New Rfam website features include a new sequence similarity search powered by RNAcentral, as well as search and visualisation of families with pseudoknots. Rfam is freely available at https://rfam.org.
MeSH term(s)	Bacteria/genetics ; Bacteria/metabolism ; Base Pairing ; Base Sequence ; Databases, Nucleic Acid ; Humans ; Internet ; Metagenome ; MicroRNAs/classification ; MicroRNAs/genetics ; MicroRNAs/metabolism ; Molecular Sequence Annotation ; Nucleic Acid Conformation ; RNA, Bacterial/classification ; RNA, Bacterial/genetics ; RNA, Bacterial/metabolism ; RNA, Untranslated/classification ; RNA, Untranslated/genetics ; RNA, Untranslated/metabolism ; RNA, Viral/classification ; RNA, Viral/genetics ; RNA, Viral/metabolism ; Sequence Alignment ; Sequence Analysis, RNA ; Software ; Viruses/genetics ; Viruses/metabolism
Chemical Substances	MicroRNAs ; RNA, Bacterial ; RNA, Untranslated ; RNA, Viral
Language	English
Publishing date	2020-11-16
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, N.I.H., Intramural ; Research Support, Non-U.S. Gov't
ZDB-ID	186809-3
ISSN	1362-4962 ; 1362-4954 ; 0301-5610 ; 0305-1048
ISSN (online)	1362-4962 ; 1362-4954
ISSN	0301-5610 ; 0305-1048
DOI	10.1093/nar/gkaa1047
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Rfam 13.0: shifting to a genome-centric resource for non-coding RNA families.

Kalvari, Ioanna / Argasinska, Joanna / Quinones-Olvera, Natalia / Nawrocki, Eric P / Rivas, Elena / Eddy, Sean R / Bateman, Alex / Finn, Robert D / Petrov, Anton I

Nucleic acids research

2017 Volume 46, Issue D1, Page(s) D335–D342

Abstract: The Rfam database is a collection of RNA families in which each family is represented by a multiple sequence alignment, a consensus secondary structure, and a covariance model. In this paper we introduce Rfam release 13.0, which switches to a new genome- ... ...

Abstract	The Rfam database is a collection of RNA families in which each family is represented by a multiple sequence alignment, a consensus secondary structure, and a covariance model. In this paper we introduce Rfam release 13.0, which switches to a new genome-centric approach that annotates a non-redundant set of reference genomes with RNA families. We describe new web interface features including faceted text search and R-scape secondary structure visualizations. We discuss a new literature curation workflow and a pipeline for building families based on RNAcentral. There are 236 new families in release 13.0, bringing the total number of families to 2687. The Rfam website is http://rfam.org.
MeSH term(s)	Databases, Nucleic Acid ; Genome ; Humans ; Molecular Sequence Annotation ; Nucleic Acid Conformation ; RNA, Untranslated/chemistry ; RNA, Untranslated/classification ; RNA, Untranslated/genetics ; Sequence Alignment ; Sequence Analysis, RNA
Chemical Substances	RNA, Untranslated
Language	English
Publishing date	2017-11-07
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	186809-3
ISSN	1362-4962 ; 1362-4954 ; 0301-5610 ; 0305-1048
ISSN (online)	1362-4962 ; 1362-4954
ISSN	0301-5610 ; 0305-1048
DOI	10.1093/nar/gkx1038
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: iLIR: A web resource for prediction of Atg8-family interacting proteins.

Kalvari, Ioanna / Tsompanis, Stelios / Mulakkal, Nitha C / Osgood, Richard / Johansen, Terje / Nezis, Ioannis P / Promponas, Vasilis J

Autophagy

2014 Volume 10, Issue 5, Page(s) 913–925

Abstract: Macroautophagy was initially considered to be a nonselective process for bulk breakdown of cytosolic material. However, recent evidence points toward a selective mode of autophagy mediated by the so-called selective autophagy receptors (SARs). SARs act ... ...

Abstract	Macroautophagy was initially considered to be a nonselective process for bulk breakdown of cytosolic material. However, recent evidence points toward a selective mode of autophagy mediated by the so-called selective autophagy receptors (SARs). SARs act by recognizing and sorting diverse cargo substrates (e.g., proteins, organelles, pathogens) to the autophagic machinery. Known SARs are characterized by a short linear sequence motif (LIR-, LRS-, or AIM-motif) responsible for the interaction between SARs and proteins of the Atg8 family. Interestingly, many LIR-containing proteins (LIRCPs) are also involved in autophagosome formation and maturation and a few of them in regulating signaling pathways. Despite recent research efforts to experimentally identify LIRCPs, only a few dozen of this class of-often unrelated-proteins have been characterized so far using tedious cell biological, biochemical, and crystallographic approaches. The availability of an ever-increasing number of complete eukaryotic genomes provides a grand challenge for characterizing novel LIRCPs throughout the eukaryotes. Along these lines, we developed iLIR, a freely available web resource, which provides in silico tools for assisting the identification of novel LIRCPs. Given an amino acid sequence as input, iLIR searches for instances of short sequences compliant with a refined sensitive regular expression pattern of the extended LIR motif (xLIR-motif) and retrieves characterized protein domains from the SMART database for the query. Additionally, iLIR scores xLIRs against a custom position-specific scoring matrix (PSSM) and identifies potentially disordered subsequences with protein interaction potential overlapping with detected xLIR-motifs. Here we demonstrate that proteins satisfying these criteria make good LIRCP candidates for further experimental verification. Domain architecture is displayed in an informative graphic, and detailed results are also available in tabular form. We anticipate that iLIR will assist with elucidating the full complement of LIRCPs in eukaryotes.
MeSH term(s)	Adaptor Proteins, Signal Transducing/chemistry ; Adaptor Proteins, Signal Transducing/metabolism ; Amino Acid Sequence ; Animals ; Arabidopsis ; Autophagy-Related Protein 8 Family ; Databases, Protein ; Drosophila melanogaster ; Humans ; Internet ; Microfilament Proteins/chemistry ; Microfilament Proteins/metabolism ; Microtubule-Associated Proteins/chemistry ; Microtubule-Associated Proteins/metabolism ; Multigene Family ; Plasmodium falciparum ; Protein Binding ; Protein Interaction Domains and Motifs ; Protein Interaction Maps ; Saccharomyces cerevisiae
Chemical Substances	Adaptor Proteins, Signal Transducing ; Autophagy-Related Protein 8 Family ; GABARAPL2 protein, human ; Microfilament Proteins ; Microtubule-Associated Proteins
Language	English
Publishing date	2014-02-26
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2454135-7
ISSN	1554-8635 ; 1554-8627
ISSN (online)	1554-8635
ISSN	1554-8627
DOI	10.4161/auto.28260
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: LiSIs: An Online Scientific Workflow System for Virtual Screening.

Kannas, Christos C / Kalvari, Ioanna / Lambrinidis, George / Neophytou, Christiana M / Savva, Christiana G / Kirmitzoglou, Ioannis / Antoniou, Zinonas / Achilleos, Kleo G / Scherf, David / Pitta, Chara A / Nicolaou, Christos A / Mikros, Emanuel / Promponas, Vasilis J / Gerhauser, Clarissa / Mehta, Rajendra G / Constantinou, Andreas I / Pattichis, Constantinos S

Combinatorial chemistry & high throughput screening

2015 Volume 18, Issue 3, Page(s) 281–295

Abstract: Modern methods of drug discovery and development in recent years make a wide use of computational algorithms. These methods utilise Virtual Screening (VS), which is the computational counterpart of experimental screening. In this manner the in silico ... ...

Abstract	Modern methods of drug discovery and development in recent years make a wide use of computational algorithms. These methods utilise Virtual Screening (VS), which is the computational counterpart of experimental screening. In this manner the in silico models and tools initial replace the wet lab methods saving time and resources. This paper presents the overall design and implementation of a web based scientific workflow system for virtual screening called, the Life Sciences Informatics (LiSIs) platform. The LiSIs platform consists of the following layers: the input layer covering the data file input; the pre-processing layer covering the descriptors calculation, and the docking preparation components; the processing layer covering the attribute filtering, compound similarity, substructure matching, docking prediction, predictive modelling and molecular clustering; post-processing layer covering the output reformatting and binary file merging components; output layer covering the storage component. The potential of LiSIs platform has been demonstrated through two case studies designed to illustrate the preparation of tools for the identification of promising chemical structures. The first case study involved the development of a Quantitative Structure Activity Relationship (QSAR) model on a literature dataset while the second case study implemented a docking-based virtual screening experiment. Our results show that VS workflows utilizing docking, predictive models and other in silico tools as implemented in the LiSIs platform can identify compounds in line with expert expectations. We anticipate that the deployment of LiSIs, as currently implemented and available for use, can enable drug discovery researchers to more easily use state of the art computational techniques in their search for promising chemical compounds. The LiSIs platform is freely accessible (i) under the GRANATUM platform at: http://www.granatum.org and (ii) directly at: http://lisis.cs.ucy.ac.cy.
MeSH term(s)	Algorithms ; Biological Science Disciplines ; High-Throughput Screening Assays ; Internet ; Medical Informatics ; Quantitative Structure-Activity Relationship
Language	English
Publishing date	2015-01-18
Publishing country	United Arab Emirates
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2064785-2
ISSN	1875-5402 ; 1386-2073
ISSN (online)	1875-5402
ISSN	1386-2073
DOI	10.2174/1386207318666150305123341
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Discovering genetic polymorphism associated with gene expression levels across the whole genome.

Antoniades, Athos / Kalvari, Ioanna / Pattichis, Constantinos / Jones, Neil / Matthews, Paul M / Domenici, Enrico / Muglia, Pierandrea

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

2009 Volume 2009, Page(s) 5466–5469

Abstract: Genetic differences have been shown to contribute to gene expression variability. A complete evaluation of the associations between a whole genome scan with 550k Single Nucleotide Polymorphisms (SNPs) and 54k detectable expression levels (probesets) was ... ...

Abstract	Genetic differences have been shown to contribute to gene expression variability. A complete evaluation of the associations between a whole genome scan with 550k Single Nucleotide Polymorphisms (SNPs) and 54k detectable expression levels (probesets) was performed on 176 human peripheral blood samples. The results are presented along with visualizations that reveal cis and trans gene expression regulatory effects. The algorithmic approach followed utilized a distributed computational system. The analysis was performed using a linear regression adjusting for all relevant covariates. Permutation testing on a random subset of the top results provided an indication of the significance levels adjusted for multiple testing and the non independence of SNPs due to linkage disequilibrium. The database of the produced results can be used as a resource to assess the functional impact of genetic polymorphisms to gene expression regulation. This resource is applicable across all disease areas.
MeSH term(s)	Base Sequence ; Chromosome Mapping/methods ; DNA Mutational Analysis/methods ; Depressive Disorder, Major/diagnosis ; Depressive Disorder, Major/genetics ; Gene Expression Profiling/methods ; Genetic Linkage ; Genetic Predisposition to Disease/genetics ; Humans ; Molecular Sequence Data ; Polymorphism, Single Nucleotide/genetics
Language	English
Publishing date	2009-11-18
Publishing country	United States
Document type	Journal Article
ISSN	2375-7477
ISSN	2375-7477
DOI	10.1109/IEMBS.2009.5334061
Database	MEDical Literature Analysis and Retrieval System OnLINE

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

Hufsky, Franziska / Lamkiewicz, Kevin / Almeida, Alexandre / Aouacheria, Abdel / Arighi, Cecilia / Bateman, Alex / Baumbach, Jan / Beerenwinkel, Niko / Brandt, Christian / Cacciabue, Marco / Chuguransky, Sara / Drechsel, Oliver / Finn, Robert D / Fritz, Adrian / Fuchs, Stephan / Hattab, Georges / Hauschild, Anne-Christin / Heider, Dominik / Hoffmann, Marie /

Hölzer, Martin / Hoops, Stefan / Kaderali, Lars / Kalvari, Ioanna / von Kleist, Max / Kmiecinski, Renó / Kühnert, Denise / Lasso, Gorka / Libin, Pieter / List, Markus / Löchel, Hannah F / Martin, Maria J / Martin, Roman / Matschinske, Julian / McHardy, Alice C / Mendes, Pedro / Mistry, Jaina / Navratil, Vincent / Nawrocki, Eric P / O'Toole, Áine Niamh / Ontiveros-Palacios, Nancy / Petrov, Anton I / Rangel-Pineros, Guillermo / Redaschi, Nicole / Reimering, Susanne / Reinert, Knut / Reyes, Alejandro / Richardson, Lorna / Robertson, David L / Sadegh, Sepideh / Singer, Joshua B / Theys, Kristof / Upton, Chris / Welzel, Marius / Williams, Lowri / Marz, Manja

Briefings in bioinformatics

2020 Volume 22, Issue 2, Page(s) 642–663

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 ... ...

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.
MeSH term(s)	Biomedical Research ; COVID-19/epidemiology ; COVID-19/prevention & control ; COVID-19/virology ; Computational Biology ; Genome, Viral ; Humans ; Pandemics ; SARS-CoV-2/genetics ; SARS-CoV-2/isolation & purification
Keywords	covid19
Language	English
Publishing date	2020-11-03
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, N.I.H., Intramural ; Research Support, Non-U.S. Gov't ; Review
ZDB-ID	2068142-2
ISSN	1477-4054 ; 1467-5463
ISSN (online)	1477-4054
ISSN	1467-5463
DOI	10.1093/bib/bbaa232
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Computational strategies to combat COVID-19

Hölzer, Martin / Hoops, Stefan / Kaderali, Lars / Kalvari, Ioanna / von Kleist, Max / Kmiecinski, Renó / Kühnert, Denise / Lasso, Gorka / Libin, Pieter / List, Markus / Löchel, Hannah F. / Martin, Maria J. / Martin, Roman / Matschinske, Julian / McHardy, Alice C. / Mendes, Pedro / Mistry, Jaina / Navratil, Vincent / Nawrocki, Eric P. / O'Toole, Áine Niamh / Ontiveros-Palacios, Nancy / Petrov, Anton I / Rangel-Pineros, Guillermo / Redaschi, Nicole / Reimering, Susanne / Reinert, Knut / Reyes, Alejandro / RIchardson, Lorna / Robertson, David L. / Sadegh, Sepideh / Singer, Joshua B.

useful tools to accelerate SARS-CoV-2 and coronavirus research

2020

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. Peer Reviewed
Keywords	virus bioinformatics ; SARS-CoV-2 ; sequencing ; epidemiology ; drug design ; tools ; 610 Medizin und Gesundheit ; ddc:610
Subject code	020
Language	English
Publishing date	2020-11-04
Publisher	Robert Koch-Institut
Publishing country	de
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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

Hölzer, Martin / Hoops, Stefan / Kaderali, Lars / Kalvari, Ioanna / von Kleist, Max / Kmiecinski, Renó / Kühnert, Denise / Lasso, Gorka / Libin, Pieter / List, Markus / Löchel, Hannah F / Martin, Maria J / Martin, Roman / Matschinske, Julian / McHardy, Alice C / Mendes, Pedro / Mistry, Jaina / Navratil, Vincent / Nawrocki, Eric P / O039, / Toole, Áine Niamh / Ontiveros-Palacios, Nancy / Petrov, Anton I / Rangel-Pineros, Guillermo / Redaschi, Nicole / Reimering, Susanne / Reinert, Knut / Reyes, Alejandro / Richardson, Lorna / Robertson, David L / Sadegh, Sepideh / Singer, Joshua B

Brief. bioinform

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.
Keywords	covid19
Publisher	WHO
Document type	Article
Note	WHO #Covidence: #906657
Database	COVID19

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