Vetinformatics-Driven Approaches to Animal Health and Disease

A special issue of Life (ISSN 2075-1729). This special issue belongs to the section "Animal Science".

Deadline for manuscript submissions: 31 July 2026 | Viewed by 699

Special Issue Editors

Animal Functional Genomics and Bioinformatics Lab., Department of Animal Science and Technology, Chung-Ang University, Anseong, Gyeonggi-do, Korea
Interests: quantitative genetics; functional genomics; system biology; bioinformatics
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Guest Editor
Pere Virgili Institute for Health Research, Tarragona, Spain
Interests: bioinformatics; computational genomics; systems biology; toxicology; drug discovery; vaccine designing

Special Issue Information

Dear Colleagues,

Vetinformatics is an emerging field that brings together bioinformatics and veterinary science to better understand animal health and disease. It allows researchers to explore how diseases affect livestock at the molecular level, helping to develop better strategies for prevention, diagnosis, and treatment. This approach also supports breeding programs by identifying key genetic traits that can enhance animal health and productivity. While the potential of vetinformatics is clear, more work is needed to fully unlock its benefits, particularly in improving animal welfare and controlling diseases. This Special Issue aims to highlight how vetinformatics can be used to tackle livestock diseases and promote the One Health approach, which recognizes the connection between the health of animals, humans, and the environment. We welcome original research and review articles on topics such as RNASeq analysis, single-cell genomics, genome-wide association studies, protein function prediction based on genetic variations, gut microbiome analysis, multi-omics approaches for livestock diseases, and the discovery of drug and vaccine candidates using in silico methods. Vetinformatics offers a promising path toward healthier animals and more sustainable livestock systems.

Dr. Jun-Mo Kim
Dr. Rajesh Kumar Pathak
Guest Editors

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Keywords

  • animal health
  • livestock disease
  • vetinformatics
  • computational genomics
  • RNAseq
  • multi-omics
  • drug discovery
  • molecular docking
  • molecular dynamics simulation
  • vaccine design
  • one health

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Published Papers (1 paper)

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Research

23 pages, 3744 KB  
Article
Dynamic Gene Network Alterations and Identification of Key Genes in the Spleen During African Swine Fever Virus (ASFV) Infection
by Jae-Beom Go, Vuong Nghia Bui, Duy Tung Dao, Ngoc Anh Bui, Jihye Cha, Hu Suk Lee and Dajeong Lim
Life 2025, 15(12), 1844; https://doi.org/10.3390/life15121844 - 30 Nov 2025
Viewed by 368
Abstract
ASFV is responsible for high mortality in domestic pigs and has caused substantial economic impact on the global swine industry due to herd losses, trade restrictions, and disease control measures. We analyzed publicly available spleen RNA-seq data from ASFV-infected pigs (n = [...] Read more.
ASFV is responsible for high mortality in domestic pigs and has caused substantial economic impact on the global swine industry due to herd losses, trade restrictions, and disease control measures. We analyzed publicly available spleen RNA-seq data from ASFV-infected pigs (n = 13 total samples), including 7 pre-infection (0 dpi), 4 samples at 2 days post-infection (2 dpi), and 2 samples at 5 dpi (5 dpi). Weighted gene co-expression network analysis (WGCNA) identified 19 modules; module–trait correlations revealed six modules associated with infection time. A co-expression module enriched for innate immune and antiviral response genes was strongly upregulated at 2 dpi, whereas a module enriched for ribosomal, translation, and metabolic process genes was broadly downregulated at 5 dpi. Protein–protein interaction analysis highlighted hub genes, including EPRS1 and USP7 within core cellular/translation programs and CMPK2 and ZBP1 within innate-immune signaling. Collectively, our results provide a network-level view of dynamic host responses and indicate coordinated shifts in immune and metabolic programs over time. These results identify CMPK2, ZBP1, EPRS1, and USP7 as hypothesis-generating hub gene candidates, warranting further validation to establish mechanistic roles and assess potential translational relevance. Full article
(This article belongs to the Special Issue Vetinformatics-Driven Approaches to Animal Health and Disease)
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