2021 FDA Science Forum
A Non-Redundant, Reference Virus Database (RVDB) for Adventitious Virus Detection in Biologics Using High-Throughput Sequencing (HTS) Technologies
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Contributing OfficeCenter for Biologics Evaluation and Research
Abstract
Adventitious viruses are a major safety concern in all biological products. The currently recommended in vitro and in vivo have limitations for broad virus detection, are tedious to set-up, and have an extended testing period (> 28 days). Additionally, a large number of animals are used for the in vivo assays, which is globally discouraged by the 3R's initiative. High-throughput sequencing technologies have demonstrated detection of known and novel viruses in biological materials and can generate results with a short turn-around-time. However, there are challenges for HTS bioinformatics detection of distantly-related viruses due to limitations of the publicly-available databases, which are not complete with all viral sequences, and virus detection can be obscured by the large amount of cellular content. Therefore, we undertook efforts to create a non-redundant, reference viral database (RVDB) that would include all viral, viral-like, and viral-related sequences, and have a reduced cellular sequence content. This was done in consultation with the Advanced Virus Detection Technologies Interest Group, which includes scientists from industry, regulatory and other government agencies (including NCBI), technology providers, and academia. RVDB contains complete viral genomes as well as partial viral sequences. The unclustered (U) and clustered (C) versions of RVDB are publicly-available at the University of Delaware RVDB Site (https://rvdb.dbi.udel.edu/). The nucleotidic RVDBs were converted to proteic databases by Marc Eloit and Thomas Bigot (available at http://rvdb-prot.pasteur.fr/ and at the University of Delaware RVDB Site) The updated RVDB v20.0 contains 3,180,577 sequences in U-RVDB and 789,728 sequences in C-RVDB. The python scripts to construct RVDB can be found at GitHub (https://github.com/ArifaKhanLab/RVDB).
