Studying the transmission of simian retroviruses to humans can help define the importance of these infections to public health. We identified a substantial prevalence (4/231, 1.8%) of infection with simian foamy viruses (SFV) among humans occupationally exposed to nonhuman primates. Evidence of SFV infection included seropositivity, proviral DNA detection and isolation of foamy virus. The infecting SFV originated from an African green monkey (one person) and baboons (three people). These infections have not as yet resulted in either disease or sexual transmission, and may represent benign endpoint infections.

The Sf9 cell line, derived from Spodoptera frugiperda, is used as a cell substrate for biological products, and no viruses have been reported in this cell line after extensive testing. We used degenerate PCR assays and massively parallel sequencing ( IMPORTANCEThe Spodoptera frugiperda Sf9 cell line is used as a cell substrate for the development and manufacture of biological products. Extensive testing has not previously identified any viruses in this cell line. This paper reports on the identification and characterization of a novel rhabdovirus in Sf9 cells. This was accomplished through the use of next-generation sequencing platforms, de novo assembly tools, and extensive bioinformatics analysis. Rhabdovirus identification was further confirmed by transmission electron microscopy. Infectivity studies showed the lack of replication of Sf-rhabdovirus in human cell lines. The overall study highlights the use of a combinatorial testing approach including conventional methods and new technologies for evaluation of cell lines for unexpected viruses and use of comprehensive bioinformatics strategies for obtaining confident next-generation sequencing results.

Detection of distantly related viruses by high-throughput sequencing (HTS) is bioinformatically challenging because of the lack of a public database containing all viral sequences, without abundant nonviral sequences, which can extend runtime and obscure viral hits. Our reference viral database (RVDB) includes all viral, virus-related, and virus-like nucleotide sequences (excluding bacterial viruses), regardless of length, and with overall reduced cellular sequences. Semantic selection criteria (SEM-I) were used to select viral sequences from GenBank, resulting in a first-generation viral database (VDB). This database was manually and computationally reviewed, resulting in refined, semantic selection criteria (SEM-R), which were applied to a new download of updated GenBank sequences to create a second-generation VDB. Viral entries in the latter were clustered at 98% by CD-HIT-EST to reduce redundancy while retaining high viral sequence diversity. The viral identity of the clustered representative sequences (creps) was confirmed by BLAST searches in NCBI databases and HMMER searches in PFAM and DFAM databases. The resulting RVDB contained a broad representation of viral families, sequence diversity, and a reduced cellular content; it includes full-length and partial sequences and endogenous nonretroviral elements, endogenous retroviruses, and retrotransposons. Testing of RVDBv10.2, with an in-house HTS transcriptomic data set indicated a significantly faster run for virus detection than interrogating the entirety of the NCBI nonredundant nucleotide database, which contains all viral sequences but also nonviral sequences. RVDB is publically available for facilitating HTS analysis, particularly for novel virus detection. It is meant to be updated on a regular basis to include new viral sequences added to GenBank. To facilitate bioinformatics analysis of high-throughput sequencing (HTS) data for the detection of both known and novel viruses, we have developed a new reference viral database (RVDB) that provides a broad representation of different virus species from eukaryotes by including all viral, virus-like, and virus-related sequences (excluding bacteriophages), regardless of their size. In particular, RVDB contains endogenous nonretroviral elements, endogenous retroviruses, and retrotransposons. Sequences were clustered to reduce redundancy while retaining high viral sequence diversity. A particularly useful feature of RVDB is the reduction of cellular sequences, which can enhance the run efficiency of large transcriptomic and genomic data analysis and increase the specificity of virus detection.