Special Issue "Bioinformatics and Computational Approaches in Viral Genomics and Evolution"

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

Deadline for manuscript submissions: closed (31 May 2020).

Special Issue Editors

Dr. John-Sebastian Eden
Website
Guest Editor
The Westmead Institute for Medical Research and the University of Sydney, Sydney, Australia
Interests: virology; virus evolution; RNA viruses; phylogenetics; bioinformatics; pathogen discovery; metagenomics; RNA-seq
Dr. Sebastián Duchêne
Website
Guest Editor
The University of Melbourne and the Peter Doherty Institute for Infection and Immunity, Parkville, Australia
Interests: phylodynamics; phylogenetics; infectious disease epidemiology; molecular evolution
Prof. Dr. Mang Shi
Website
Guest Editor
1. Sun Yat-sen University, Guangzhou, China
2. The University of Sydney, Sydney, Australia
Interests: virus evolution; metagenomics; meta-transcriptomics; macroevolution; pathogen discovery; virology

Special Issue Information

Dear colleague,

In recent years, bioinformatics and computational methods have become a core component of virus research, underpinning the fields of virus genomics and evolution. The scale and complexity of analyses have grown along with major advances in genetic sequencing and, consequently, have required a paradigm shift with regard to the statistical and computational requirements of viral genomic analysis. For example, in phylogenetics, new methods have been developed to estimate trees with large numbers of taxa (>10,000 sequences) and to infer complex transmission dynamics using machine learning techniques. Similarly, integrative phylodynamic methods can combine key phenotypic “traits” such as sampling location, case counts, and time with virus genetic data to obtain new insights into the epidemic spread of important pathogens such as influenza virus, Zika virus, and HIV. From a macro-evolution perspective, the use of genomic- and metagenomic-based approaches has expanded our knowledge of the diversity and evolutionary history of the entire virosphere, providing new insight into many old questions such as virus origin, genome evolution, evolution time scales, and virus–host interactions.

The purpose of this Special Issue is to bring together a series of articles (both reviews and original research) related the development and application of novel sequencing and analytical approaches to better understand the discovery, transmission, evolution, and molecular epidemiology of viruses across all hosts.

Dr. John-Sebastian Eden
Dr. Sebastián Duchêne
Prof. Dr. Mang Shi
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

  • bioinformatics
  • computational biology
  • phylogenetics
  • virus evolution
  • virus genomics
  • metagenomics
  • macroevolution
  • virus–host interactions

Published Papers (3 papers)

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Research

Open AccessArticle
Drug Resistance Prediction Using Deep Learning Techniques on HIV-1 Sequence Data
Viruses 2020, 12(5), 560; https://doi.org/10.3390/v12050560 - 19 May 2020
Abstract
The fast replication rate and lack of repair mechanisms of human immunodeficiency virus (HIV) contribute to its high mutation frequency, with some mutations resulting in the evolution of resistance to antiretroviral therapies (ART). As such, studying HIV drug resistance allows for real-time evaluation [...] Read more.
The fast replication rate and lack of repair mechanisms of human immunodeficiency virus (HIV) contribute to its high mutation frequency, with some mutations resulting in the evolution of resistance to antiretroviral therapies (ART). As such, studying HIV drug resistance allows for real-time evaluation of evolutionary mechanisms. Characterizing the biological process of drug resistance is also critically important for sustained effectiveness of ART. Investigating the link between “black box” deep learning methods applied to this problem and evolutionary principles governing drug resistance has been overlooked to date. Here, we utilized publicly available HIV-1 sequence data and drug resistance assay results for 18 ART drugs to evaluate the performance of three architectures (multilayer perceptron, bidirectional recurrent neural network, and convolutional neural network) for drug resistance prediction, jointly with biological analysis. We identified convolutional neural networks as the best performing architecture and displayed a correspondence between the importance of biologically relevant features in the classifier and overall performance. Our results suggest that the high classification performance of deep learning models is indeed dependent on drug resistance mutations (DRMs). These models heavily weighted several features that are not known DRM locations, indicating the utility of model interpretability to address causal relationships in viral genotype-phenotype data. Full article
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Open AccessArticle
Genomic Analyses of Potential Novel Recombinant Human Adenovirus C in Brazil
Viruses 2020, 12(5), 508; https://doi.org/10.3390/v12050508 - 04 May 2020
Abstract
Human Adenovirus species C (HAdV-C) is the most common etiologic agent of respiratory disease. In the present study, we characterized the nearly full-length genome of one potential new HAdV-C recombinant strain constituted by Penton and Fiber proteins belonging to type 89 and a [...] Read more.
Human Adenovirus species C (HAdV-C) is the most common etiologic agent of respiratory disease. In the present study, we characterized the nearly full-length genome of one potential new HAdV-C recombinant strain constituted by Penton and Fiber proteins belonging to type 89 and a chimeric Hexon protein of types 1 and 89. By using viral metagenomics techniques, we screened out, in the states of Tocantins and Pará, Northern and North regions of Brazil, from 2010 to 2016, 251 fecal samples of children between 0.5 to 2.5 years old. These children were presenting acute diarrhea not associated with common pathogens (i.e., rotavirus, norovirus). We identified two HAdV-C strains in two distinct patients. Phylogenetic analysis performed using all complete genomes available at GenBank database indicated that one strain (HAdV-C BR-245) belonged to type 1. The phylogenetic analysis also indicated that the second strain (HAdV-C BR-211) was located at the base of the clade formed by the newly HAdV-C strains type 89. Recombination analysis revealed that strain HAdV-C BR-211 is a chimera in which the variable regions of Hexon gene combined HAdV-C1 and HAdV-C89 sequences. Therefore, HAdV-C BR-211 strain possesses a genomic backbone of type HAdV-C89 and a unique insertion of HAdV-C1 in the Hexon sequence. Recombination may play an important driving force in HAdV-C diversity and evolution. Studies employing complete genomic sequencing on circulating HAdV-C strains in Brazil are needed to understand the clinical significance of the presented data. Full article
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Open AccessCommunication
A Novel Hepe-Like Virus from Farmed Giant Freshwater Prawn Macrobrachium rosenbergii
Viruses 2020, 12(3), 323; https://doi.org/10.3390/v12030323 - 17 Mar 2020
Abstract
The family Hepeviridae includes several positive-stranded RNA viruses, which infect a wide range of mammalian species, chicken, and trout. However, few hepatitis E viruses (HEVs) have been characterized from invertebrates. In this study, a hepevirus, tentatively named Crustacea hepe-like virus 1 (CHEV1), from [...] Read more.
The family Hepeviridae includes several positive-stranded RNA viruses, which infect a wide range of mammalian species, chicken, and trout. However, few hepatitis E viruses (HEVs) have been characterized from invertebrates. In this study, a hepevirus, tentatively named Crustacea hepe-like virus 1 (CHEV1), from the economically important crustacean, the giant freshwater prawn Macrobrachium rosenbergii, was characterized. The complete genome consisted of 7750 nucleotides and had a similar structure to known hepatitis E virus genomes. Phylogenetic analyses suggested it might be a novel hepe-like virus within the family Hepeviridae. To our knowledge, this is the first hepe-like virus characterized from crustaceans. Full article
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