Long-Read High-Throughput Sequencing (HTS) Revealed That the Sf-Rhabdovirus X+ Genome Contains a 3.7 kb Internal Duplication.

We previously reported a novel rhabdovirus produced from the Spodoptera frugiperda Sf9 cell line, designated as Sf-rhabdovirus X+ since it contained a unique accessory gene X. The Sf-rhabdovirus X+ genome sequence was generated using Sanger sequencing and short-read high-throughput sequencing (HTS). In this study, we have used long-read HTS technologies, PacBio's single-molecule real-time sequencing and Oxford's Nanopore RNA direct sequencing, to analyze the parent Sf9 cell line transcriptome and the virus RNA produced from an X+ cell clone, respectively. A unique 3.7 kb duplication was identified in the L gene between nucleotide position 8523 and 8524, preceded by a GA dinucleotide insertion. This duplication contained a partial G gene, the complete X gene, and a partial L gene, which extended from nucleotide positions 4767-8523 in the X+ virus. Thus, the X+ genome length is 17,361 nucleotides, and we have re-designated the virus as Sf-rhabdovirus X+3.7. The 3.7 kb duplication was found in all Sf9 cell clones producing the X+ variant virus. Furthermore, the Sf-rhabdovirus X+3.7 genome was stable at passage 30, which was the highest passage tested. These results highlight the importance of combining short-read and long-read technologies for accurately sequencing virus genomes using HTS.

Identification and quantification of defective virus genomes in high throughput sequencing data using DVG-profiler, a novel post-sequence alignment processing algorithm.

Most viruses are known to spontaneously generate defective viral genomes (DVG) due to errors during replication. These DVGs are subgenomic and contain deletions that render them unable to complete a full replication cycle in the absence of a co-infecting, non-defective helper virus. DVGs, especially of the copyback type, frequently observed with paramyxoviruses, have been recognized to be important triggers of the antiviral innate immune response. DVGs have therefore gained interest for their potential to alter the attenuation and immunogenicity of vaccines. To investigate this potential, accurate identification and quantification of DVGs is essential. Conventional methods, such as RT-PCR, are labor intensive and will only detect primer sequence-specific species. High throughput sequencing (HTS) is much better suited for this undertaking. Here, we present an HTS-based algorithm called DVG-profiler to identify and quantify all DVG sequences in an HTS data set generated from a virus preparation. DVG-profiler identifies DVG breakpoints relative to a reference genome and reports the directionality of each segment from within the same read. The specificity and sensitivity of the algorithm was assessed using both in silico data sets as well as HTS data obtained from parainfluenza virus 5, Sendai virus and mumps virus preparations. HTS data from the latter were also compared with conventional RT-PCR data and with data obtained using an alternative algorithm. The data presented here demonstrate the high specificity, sensitivity, and robustness of DVG-profiler. This algorithm was implemented within an open source cloud-based computing environment for analyzing HTS data. DVG-profiler might prove valuable not only in basic virus research but also in monitoring live attenuated vaccines for DVG content and to assure vaccine lot to lot consistency.