Special Issue "Virus Bioinformatics 2020"

A special issue of Viruses (ISSN 1999-4915).

Deadline for manuscript submissions: 31 October 2020.

Special Issue Editors

Prof. Dr. Manja Marz
Website
Guest Editor
The European Virus Bioinformatics Center and Friedrich Schiller University, Jena, Germany
Interests: high throughput sequencing analysis; bioinformatic analysis and system biology of viruses; comparative genomics; identification and annotation of non-coding RNAs; coevolution of proteins and RNAs; algorithmic bioinformatics
Special Issues and Collections in MDPI journals
Prof. Dr. habil. Bashar Ibrahim
Website
Guest Editor
The European Virus Bioinformatics Center, Jena, Germany and Gulf University for Science and Technology, Hawally, Kuwait
Interests: mathematical and computational systems biology; multiscale and unconventional modelling, simulation, and analysis of complex systems
Special Issues and Collections in MDPI journals
Dr. Franziska Hufsky
Website
Guest Editor
The European Virus Bioinformatics Center and Friedrich Schiller University Jena, Germany
Interests: computational metabolomics and mass spectrometry; algorithms in bioinformatics; virus bioinformatics
Special Issues and Collections in MDPI journals
Prof. Dr. Ronald Dijkman
Website
Guest Editor
Institut für Virologie und Immunologie, Universität Bern, Bern, Switzerland
Interests: microbiology; immunology; virology; influenza viruses; zoonoses
Dr. Alban Ramette
Website SciProfiles
Guest Editor
Institut für Infektionskrankheiten, Universität Bern, Bern, Switzerland
Interests: microbiology; epidemiology; biostatistics; microbial ecology; bioinformatics
Special Issues and Collections in MDPI journals
Dr. Jenna Kelly
Website
Guest Editor
Institut für Virologie und Immunologie, Universität Bern, Bern, Switzerland
Interests: emerging viruses; zoonosis; bioinformatics; host pathogen dynamics; single cell sequencing; next generation sequencing

Special Issue Information

Dear Colleagues,

This Special Issue is related to the 4th International Virus Bioinformatics Meeting (former Annual Meeting of the European Virus Bioinformatics Center) taking place from 05–06 March 2020 at the Eventforum in Bern, Switzerland.

This Special Issue will present articles covering computational approaches in virology, and we welcome any contributions within this cross-disciplinary field. Sub-topics include (but are not limited to) the following: systems virology, virus–host interactions, virus classification and evolution, epidemiology and surveillance, viral metagenomics and ecology, and clinical bioinformatics.

The Special Issue is open to all researchers working on the boundaries between bioinformatics and virology.

Papers such as original research articles and review papers dealing with the recent advancements and current understanding of computational technologies aspects of virology are welcome.

All papers should be submitted online at https://www.mdpi.com/journal/viruses. Please select the correct Special Issue when submitting your paper to Viruses.

Prof. Dr. Manja Marz
PD Dr. habil. Bashar Ibrahim
Dr. Franziska Hufsky
Prof. Dr. Ronald Dijkman
Dr. Alban Ramette
Dr. Jenna Kelly
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Viruses is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • systems virology
  • virus-host interactions
  • virus classification and evolution
  • epidemiology and surveillance
  • viral metagenomics and ecology
  • clinical bioinformatics

Related Special Issue

Published Papers (6 papers)

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Research

Open AccessCommunication
Determining the Suitability of MinION’s Direct RNA and DNA Amplicon Sequencing for Viral Subtype Identification
Viruses 2020, 12(8), 801; https://doi.org/10.3390/v12080801 - 25 Jul 2020
Abstract
The MinION sequencer is increasingly being used for the detection and outbreak surveillance of pathogens due to its rapid throughput. For RNA viruses, MinION’s new direct RNA sequencing is the next significant development. Direct RNA sequencing studies are currently limited and comparisons of [...] Read more.
The MinION sequencer is increasingly being used for the detection and outbreak surveillance of pathogens due to its rapid throughput. For RNA viruses, MinION’s new direct RNA sequencing is the next significant development. Direct RNA sequencing studies are currently limited and comparisons of its diagnostic performance relative to different DNA sequencing approaches are lacking as a result. We sought to address this gap and sequenced six subtypes from the mycovirus CHV-1 using MinION’s direct RNA sequencing and DNA sequencing based on a targeted viral amplicon. Reads from both techniques could correctly identify viral presence and species using BLAST, though direct RNA reads were more frequently misassigned to closely related CHV species. De novo consensus sequences were error prone but suitable for viral species identification. However, subtype identification was less accurate from both reads and consensus sequences. This is due to the high sequencing error rate and the limited sequence divergence between some CHV-1 subtypes. Importantly, neither RNA nor amplicon sequencing reads could be used to obtain reliable intra-host variants. Overall, both sequencing techniques were suitable for virus detection, though limitations are present due to the error rate of MinION reads. Full article
(This article belongs to the Special Issue Virus Bioinformatics 2020)
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Open AccessArticle
Covid-19 Transmission Trajectories–Monitoring the Pandemic in the Worldwide Context
Viruses 2020, 12(7), 777; https://doi.org/10.3390/v12070777 - 20 Jul 2020
Abstract
The Covid-19 pandemic is developing worldwide with common dynamics but also with marked differences between regions and countries. These are not completely understood, but presumably, provide a clue to find ways to mitigate epidemics until strategies leading to its eradication become available. We [...] Read more.
The Covid-19 pandemic is developing worldwide with common dynamics but also with marked differences between regions and countries. These are not completely understood, but presumably, provide a clue to find ways to mitigate epidemics until strategies leading to its eradication become available. We describe an iteractive monitoring tool available in the internet. It enables inspection of the dynamic state of the epidemic in 187 countries using trajectories that visualize the transmission and removal rates of the epidemic and in this way bridge epi-curve tracking with modelling approaches. Examples were provided which characterize state of epidemic in different regions of the world in terms of fast and slow growing and decaying regimes and estimate associated rate factors. The basic spread of the disease is associated with transmission between two individuals every two-three days on the average. Non-pharmaceutical interventions decrease this value to up to ten days, whereas ‘complete lock down’ measures are required to stop the epidemic. Comparison of trajectories revealed marked differences between the countries regarding efficiency of measures taken against the epidemic. Trajectories also reveal marked country-specific recovery and death rate dynamics. The results presented refer to the pandemic state in May to July 2020 and can serve as ‘working instruction’ for timely monitoring using the interactive monitoring tool as a sort of ‘seismometer’ for the evaluation of the state of epidemic, e.g., the possible effect of measures taken in both, lock-down and lock-up directions. Comparison of trajectories between countries and regions will support developing hypotheses and models to better understand regional differences of dynamics of Covid-19. Full article
(This article belongs to the Special Issue Virus Bioinformatics 2020)
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Open AccessArticle
Deploying Machine and Deep Learning Models for Efficient Data-Augmented Detection of COVID-19 Infections
Viruses 2020, 12(7), 769; https://doi.org/10.3390/v12070769 - 16 Jul 2020
Abstract
This generation faces existential threats because of the global assault of the novel Corona virus 2019 (i.e., COVID-19). With more than thirteen million infected and nearly 600000 fatalities in 188 countries/regions, COVID-19 is the worst calamity since the World War II. These misfortunes [...] Read more.
This generation faces existential threats because of the global assault of the novel Corona virus 2019 (i.e., COVID-19). With more than thirteen million infected and nearly 600000 fatalities in 188 countries/regions, COVID-19 is the worst calamity since the World War II. These misfortunes are traced to various reasons, including late detection of latent or asymptomatic carriers, migration, and inadequate isolation of infected people. This makes detection, containment, and mitigation global priorities to contain exposure via quarantine, lockdowns, work/stay at home, and social distancing that are focused on “flattening the curve”. While medical and healthcare givers are at the frontline in the battle against COVID-19, it is a crusade for all of humanity. Meanwhile, machine and deep learning models have been revolutionary across numerous domains and applications whose potency have been exploited to birth numerous state-of-the-art technologies utilised in disease detection, diagnoses, and treatment. Despite these potentials, machine and, particularly, deep learning models are data sensitive, because their effectiveness depends on availability and reliability of data. The unavailability of such data hinders efforts of engineers and computer scientists to fully contribute to the ongoing assault against COVID-19. Faced with a calamity on one side and absence of reliable data on the other, this study presents two data-augmentation models to enhance learnability of the Convolutional Neural Network (CNN) and the Convolutional Long Short-Term Memory (ConvLSTM)-based deep learning models (DADLMs) and, by doing so, boost the accuracy of COVID-19 detection. Experimental results reveal improvement in terms of accuracy of detection, logarithmic loss, and testing time relative to DLMs devoid of such data augmentation. Furthermore, average increases of 4% to 11% in COVID-19 detection accuracy are reported in favour of the proposed data-augmented deep learning models relative to the machine learning techniques. Therefore, the proposed algorithm is effective in performing a rapid and consistent Corona virus diagnosis that is primarily aimed at assisting clinicians in making accurate identification of the virus. Full article
(This article belongs to the Special Issue Virus Bioinformatics 2020)
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Open AccessArticle
Synonymous Dinucleotide Usage: A Codon-Aware Metric for Quantifying Dinucleotide Representation in Viruses
Viruses 2020, 12(4), 462; https://doi.org/10.3390/v12040462 - 20 Apr 2020
Cited by 1
Abstract
Distinct patterns of dinucleotide representation, such as CpG and UpA suppression, are characteristic of certain viral genomes. Recent research has uncovered vertebrate immune mechanisms that select against specific dinucleotides in targeted viruses. This evidence highlights the importance of systematically examining the dinucleotide composition [...] Read more.
Distinct patterns of dinucleotide representation, such as CpG and UpA suppression, are characteristic of certain viral genomes. Recent research has uncovered vertebrate immune mechanisms that select against specific dinucleotides in targeted viruses. This evidence highlights the importance of systematically examining the dinucleotide composition of viral genomes. We have developed a novel metric, called synonymous dinucleotide usage (SDU), for quantifying dinucleotide representation in coding sequences. Our method compares the abundance of a given dinucleotide to the null hypothesis of equal synonymous codon usage in the sequence. We present a Python3 package, DinuQ, for calculating SDU and other relevant metrics. We have applied this method on two sets of invertebrate- and vertebrate-specific flaviviruses and rhabdoviruses. The SDU shows that the vertebrate viruses exhibit consistently greater under-representation of CpG dinucleotides in all three codon positions in both datasets. In comparison to existing metrics for dinucleotide quantification, the SDU allows for a statistical interpretation of its values by comparing it to a null expectation based on the codon table. Here we apply the method to viruses, but coding sequences of other living organisms can be analysed in the same way. Full article
(This article belongs to the Special Issue Virus Bioinformatics 2020)
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Open AccessArticle
The In Silico Prediction of Hotspot Residues that Contribute to the Structural Stability of Subunit Interfaces of a Picornavirus Capsid
Viruses 2020, 12(4), 387; https://doi.org/10.3390/v12040387 - 31 Mar 2020
Abstract
The assembly of picornavirus capsids proceeds through the stepwise oligomerization of capsid protein subunits and depends on interactions between critical residues known as hotspots. Few studies have described the identification of hotspot residues at the protein subunit interfaces of the picornavirus capsid, some [...] Read more.
The assembly of picornavirus capsids proceeds through the stepwise oligomerization of capsid protein subunits and depends on interactions between critical residues known as hotspots. Few studies have described the identification of hotspot residues at the protein subunit interfaces of the picornavirus capsid, some of which could represent novel drug targets. Using a combination of accessible web servers for hotspot prediction, we performed a comprehensive bioinformatic analysis of the hotspot residues at the intraprotomer, interprotomer and interpentamer interfaces of the Theiler’s murine encephalomyelitis virus (TMEV) capsid. Significantly, many of the predicted hotspot residues were found to be conserved in representative viruses from different genera, suggesting that the molecular determinants of capsid assembly are conserved across the family. The analysis presented here can be applied to any icosahedral structure and provides a platform for in vitro mutagenesis studies to further investigate the significance of these hotspots in critical stages of the virus life cycle with a view to identify potential targets for antiviral drug design. Full article
(This article belongs to the Special Issue Virus Bioinformatics 2020)
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Open AccessArticle
Rapid, Unbiased PRRSV Strain Detection Using MinION Direct RNA Sequencing and Bioinformatics Tools
Viruses 2019, 11(12), 1132; https://doi.org/10.3390/v11121132 - 07 Dec 2019
Cited by 4
Abstract
Prompt detection and effective control of porcine reproductive and respiratory syndrome virus (PRRSV) during outbreaks is important given its immense adverse impact on the swine industry. However, the diagnostic process can be challenging due to the high genetic diversity and high mutation rate [...] Read more.
Prompt detection and effective control of porcine reproductive and respiratory syndrome virus (PRRSV) during outbreaks is important given its immense adverse impact on the swine industry. However, the diagnostic process can be challenging due to the high genetic diversity and high mutation rate of PRRSV. A diagnostic method that can provide more detailed genetic information about pathogens is urgently needed. In this study, we evaluated the ability of Oxford Nanopore MinION direct RNA sequencing to generate a PRRSV whole genome sequence and detect and discriminate virus at the strain-level. A nearly full length PRRSV genome was successfully generated from raw sequence reads, achieving an accuracy of 96% after consensus genome generation. Direct RNA sequencing reliably detected the PRRSV strain present with an accuracy of 99.9% using as few as 5 raw sequencing reads and successfully differentiated multiple co-infecting strains present in a sample. In addition, PRRSV strain information was obtained from clinical samples containing 104 to 106 viral copies or more within 6 hours of sequencing. Overall, direct viral RNA sequencing followed by bioinformatic analysis proves to be a promising approach for identification of the viral strain or strains involved in clinical infections, allowing for more precise prevention and control strategies during PRRSV outbreaks. Full article
(This article belongs to the Special Issue Virus Bioinformatics 2020)
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