Rabbit Haemorrhagic Disease Virus Database: Enhancing Environmental Surveillance with In Silico and Literature-Derived Primers for PCR Applications

The rabbit haemorrhagic disease virus (RHDV) impacts the European rabbit and was first discovered in China in 1984. Since then, it spread globally, leading to significant rabbit population declines. To manage the vast amount of genomic data available, including of RHDV, various strategies and computational tools have been developed. These tools can automatically retrieve and curate nucleotide and amino acid information from multiple sources, enabling better identification and therapeutic approaches. This study developed an automated computational method to retrieve primers from the literature (AROLit) and design in silico primers in Python (iSOP) for the detection of RHDV. The AROLit and iSOP databases were published online under the name RHDV database (https://rhdv-primers-identification-db.jc-biotechaiteam.com/). The workflows, applicable to any virus or bacterial genome, support optimal primer pair selection for PCR. Five top primers from AROLit and six from iSOP were validated in the laboratory to evaluate specificity to several RHDV strains. The RHDV database offers a unique combination of in silico and literature-derived primers optimized for PCR applications. This freely accessible database adheres to the FAIR principles and includes optimal primer pair selection workflows. The best primers in the database, validated in the lab, demonstrated promising specificity to several RHDV strains. Through the development of advanced techniques for pathogen detection, this research seeks to significantly improve the accuracy and efficiency of environmental surveillance. By enabling early and precise identification of viruses, the work aims to facilitate proactive management strategies to mitigate the impacts on both wild and domestic animal populations.