About this Research Topic
Vetinformatics: An Insight for Decoding Livestock Systems Through In Silico Biology
Vetinformatics is an emerging field of study that involves the integration of bioinformatics with veterinary science and livestock. The potential of vetinformatics lies in its ability to decode complex molecular mechanisms in livestock, which can lead to improved livestock health, productivity, and sustainability. Vetinformatics can improve our understanding of disease genetics, identify genetic markers for breeding, and develop treatments for livestock diseases.
One of the key benefits of vetinformatics is its ability to process and analyze big data in biology quickly and efficiently. By analyzing multi-omics data, we can identify genes that are associated with specific diseases or traits, drug targets which can be used to develop breeding programs and targeted therapies. It can also be used to improve animal welfare by identifying risk factors for disease and developing strategies to prevent their effects by analyzing data on animal behavior to identify patterns that indicate discomfort for development of effective strategies and intervention that improve animal welfare. Therefore, vetinformatics has tremendous potential to improve sustainability and livestock productivity.
Many human diseases have their origins in animals, as demonstrated by the SARS-CoV-2 pandemic, which emphasized the close connection between animals, humans, and the environment. Recent global health crises have once again highlighted the need for One Health, making it an essential research area that necessitates the use of in silico biology.
This Research Topic aims to consolidate new knowledge related to the treatment and management of livestock diseases through the use of vetinformatics. Additionally, it will provide valuable insights into the concept of “One Health” emphasizing the interconnectedness of human, animal, and environmental health. This knowledge will enable the application of in silico biology for enhancing livestock productivity and sustainability.
This Research Topic will include potential topics, but are not limited to:
• Single-cell genomics for investigating the pathogenesis of livestock diseases.
• Genome-wide association studies for investigating disease-associated SNPs in livestock.
• In silico assessment and prediction of the effects of non-synonymous SNPs on protein structure.
• Study microbial communities associated with livestock, such as the gut microbiome for livestock health and disease.
• Investigate drug targets and design drug and vaccine candidates for livestock diseases using multi-omics data interaction.
• Utilization of In silico biology for One Health.
Keywords: network analysis, molecular docking, Genome analysis, single cell analysis, RNAseq, microbiome, multi-omics, target identification, protein modeling, molecular dynamics, vaccine designing, veterinary medicine, Artificial intelligence, machine learning, one health
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