Predicting whole genome sequencing success for archived avian influenza virus (Orthomyxoviridae) samples using real-time and droplet PCRs.

Long-term viral archives are valuable sources of research data. Each archive can store hundreds of thousands of diverse sample types. In the current era of whole genome sequencing, archived samples become a rich source of evolutionary and epidemiological data that can span years, and even decades. However, the ability to obtain high quality viral whole genome sequences from samples of various types, age, and quality is inconsistent. A minimum quality threshold that helps predict the best success of obtaining high quality genomic sequences for both recent and archived samples is highly valuable. Real-time reverse transcription PCR (rrt-PCR) and droplet digital PCR (ddPCR) are useful tools to evaluate nucleic acid integrity. We hypothesized that diagnostic rrt-PCR and ddPCR data for avian influenza virus (AIV) can predict viral whole genome sequencing success. To test this hypothesis we used RNA extracted from cloacal and oropharyngeal swabs stored in the USDA-APHIS National Wildlife Disease Program Wildlife Tissue Archive. We determined that a specific rrt-PCR C

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