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Viruses
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Viroinformatics and Viral Diseases
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Viroinformatics and Viral Diseases

A special issue of Viruses (ISSN 1999-4915). This special issue belongs to the section "General Virology".

Deadline for manuscript submissions: 31 July 2026 | Viewed by 1773

Special Issue Editor

Dr. Hernan Garcia-Ruiz

E-Mail Website
Guest Editor
Department of Plant Pathology, Nebraska Center for Virology, University of Nebraska-Lincoln, Lincoln, NE 68583, USA
Interests: plant virus; virus–host interactions; virus evolution; viral genomics; viroinformatics; gene silencing; antiviral defense; virus replication
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Viroinformatics is an amalgamation of virology, genomics, and bioinformatics, involving the application of genetic information, analyses, and communication technology in viral research. As a result of the availability of high-throuput RNA or small RNA sequening data sets, viroinformatics is revolutionizing the way we study, understand, track, and manage viral diseases. This Special Issue includes cutting-edge applications of viroinformatics in viral surveillance, pathogenesis studies, virus evolution, characterization of genome-wide variation, drug/vaccine design, the emergence of new variants, and outbreak modeling. Big data, machine learning, artificial intelligence, structural bioinformatics, or phylogenetic approaches can be applied to address emerging/re-emerging viruses (e.g., SARS-CoV-2, influenza, arboviruses, and Tobamovirus fructirugosum). Relevant topics include viral sequence analysis using DNA,  RNA, or siRNA; host–pathogen interaction networks; diagnostic innovations; and AI-driven therapeutic discovery. By bridging computational science and experimental virology, it may accelerate solutions for global viral threats and foster interdisciplinary collaboration among virologists, evolutionary biologists, bioinformaticians, and public health researchers.

Dr. Hernan Garcia-Ruiz
Guest Editor

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 submissions that pass pre-check are 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 250 words) can be sent to the Editorial Office for assessment.

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 2600 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

  • viral genomics
  • virus variation
  • genome-wide variation
  • gene silencing
  • antiviral defense
  • virus replication
  • host factors
  • high-throughput sequencing
  • small RNA sequencing

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Published Papers (2 papers)

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Research

16 pages, 5272 KB  
Article
Metagenomics Analysis of Viruses Associated with Cassava Brown Streak Disease in Kenya
by Florence M. Munguti, Katherine LaTourrette, Gonçalo Silva, Solomon Maina, Dora C. Kilalo, Isaac Macharia, Agnes W. Mwango’mbe, Evans N. Nyaboga and Hernan Garcia-Ruiz
Viruses 2026, 18(3), 395; https://doi.org/10.3390/v18030395 - 21 Mar 2026
Viewed by 701
Abstract
Cassava brown streak disease (CBSD), caused by cassava brown streak virus (CBSV; Ipomovirus brunusmanihotis) and Ugandan cassava brown streak virus (UCBSV; Ipomovirus manihotis) (family Potyviridae, genus Ipomovirus), is increasingly becoming a threat to cassava production in several parts of [...] Read more.
Cassava brown streak disease (CBSD), caused by cassava brown streak virus (CBSV; Ipomovirus brunusmanihotis) and Ugandan cassava brown streak virus (UCBSV; Ipomovirus manihotis) (family Potyviridae, genus Ipomovirus), is increasingly becoming a threat to cassava production in several parts of Africa, especially in Eastern, Central and Southern Africa. In Kenya, the disease continues to wreak havoc on cassava production leading to a significant reduction in crop yields and economic losses of up to USD 1 billion. Variation in virus populations make the control of CBSD challenging as virus genomic variation can affect the accuracy of diagnostic tests, lead to resistance breaking isolates and jeopardize strategies of breeding for resistance. CBSV and UCBSV populations obtained from cassava fields in Kenya were characterized. In total, 44 new complete sequences of CBSV and UCBSV were assembled and 40 sequences successfully submitted to GenBank. Single Nucleotide Polymorphism (SNP) analysis revealed that the cylindrical inclusion protein (CI) is the most stable region across the genome of CBSV and UCBSV. In contrast, protein 1 (PI) and the coat protein (CP) were the most hypervariable regions. Phylogenetic analysis showed three major geographical groupings for both UCBSV and CBSV isolates, suggesting a continued spread of the viruses through human-mediated movement of infected planting materials. The data obtained in this study can support the development of disease management strategies through improved molecular diagnostic tests and targets for breeding for resistance against CBSD. Full article
(This article belongs to the Special Issue Viroinformatics and Viral Diseases)
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18 pages, 1642 KB  
Article
Foundation Protein Language Models for Influenza A Virus T-Cell Epitope Prediction: A Transformer-Based Viroinformatics Framework
by Syed Nisar Hussain Bukhari and Kingsley A. Ogudo
Viruses 2026, 18(3), 380; https://doi.org/10.3390/v18030380 - 18 Mar 2026
Viewed by 647
Abstract
Influenza A virus remains a major cause of respiratory disease worldwide and poses a persistent challenge to vaccine development due to its rapid genetic evolution and antigenic variability. T-cell-based immunity has therefore gained increasing importance, as it can provide broader and more durable [...] Read more.
Influenza A virus remains a major cause of respiratory disease worldwide and poses a persistent challenge to vaccine development due to its rapid genetic evolution and antigenic variability. T-cell-based immunity has therefore gained increasing importance, as it can provide broader and more durable protection by targeting conserved viral regions. Accurate identification of T-cell epitopes (TCEs) is a fundamental requirement for epitope-based vaccine design and immunological research. Although numerous computational methods have been proposed, many existing approaches rely on handcrafted physicochemical features, which offer limited ability to capture contextual sequence dependencies. In this study, a transformer-based viroinformatics framework is proposed for the binary prediction of TCEs from Influenza A virus peptide sequences. The framework employs a pretrained Evolutionary Scale Modeling-2 (ESM-2) protein language model (PLM) to generate rich, contextualized embeddings directly from raw amino acid sequences, eliminating the need for manual feature engineering. These embeddings are processed using a lightweight attention-based transformer classifier to learn epitope-specific sequence patterns. The model achieves strong and stable predictive performance, attaining an accuracy of approximately 97% and an AUC close to 0.99 under stratified cross-validation. Ablation analysis further confirms that protein language model representations and self-attention contribute substantially to performance gains over classical machine learning baselines. To enhance practical reliability, Monte Carlo dropout is incorporated during inference to provide uncertainty-aware predictions, enabling differentiation between high-confidence and ambiguous peptide candidates. In addition, attention-based interpretability is used to identify residue-level contributions to model decisions, offering biologically meaningful insights into epitope recognition. Overall, this study demonstrates that PLMs combined with Transformer architectures provide an effective, interpretable, and a promising computational framework for Influenza A TCE discovery and vaccine research. Full article
(This article belongs to the Special Issue Viroinformatics and Viral Diseases)
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