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

• DOI: 10.1371/journal.pone.0216944
• Publication Date: 2019-05-17T17:25:42Z
• Authors: Trent J. Bosma; Konstantinos Kara...; Luis Santana-Quin...; Natalia Ilyushina; Tatiana Zagorodnyaya; Svetlana Petrovskaya; Majid Laassri; Raymond P. Donnelly; Steven Rubin; Vahan Simonyan; Christian J. Sauder
• Keywords: Sendai virus; Subgenomic mRNA; Helper virus

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