<i>DI-tector</i>: defective interfering viral genomes’ detector for next-generation sequencing data

Beauclair, Guillaume; Mura, Marie; Combredet, Chantal; Tangy, Frédéric; Jouvenet, Nolwenn; Komarova, Anastassia V.

doi:10.1261/rna.066910.118

Cited by 37 publications

(59 citation statements)

References 43 publications

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“…A re-analysis of rejected reads would identify these DVG hallmarks. With growing interest in this neglected part of the viral population, informatics tools such as ViReMa 48 , DI-tector 49 and VODKA 50 are emerging. These tools re-examine reads that potentially harbour jumbled or rearranged viral sequences and have enabled a broader appreciation of DVG diversity.…”

Section: Classes Types Structures and Diversitymentioning

confidence: 99%

Defective viral genomes are key drivers of the virus–host interaction

2019

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Viruses survive often harsh host environments, yet we know little about the strategies they utilize to adapt and subsist given their limited genomic resources. We are beginning to appreciate the surprising versatility of viral genomes and how replicationcompetent and -defective virus variants can provide means for adaptation, immune escape and virus perpetuation. This Review summarizes current knowledge of the types of defective viral genomes generated during the replication of RNA viruses and the functions that they carry out. We highlight the universality and diversity of defective viral genomes during infections and discuss their predicted role in maintaining a fit virus population, their impact on human and animal health, and their potential to be harnessed as antiviral tools. Review ARticleNATuRe MicRoBiology extinction interfere with replication of a wild-type viral genome 29,30 . Hypermutations and mutations leading to frame shifts also result in defective viruses 31 (Fig. 1a). One example is mutations introduced by the cellular protein APOBEC3G into pro-retroviruses 32 . APOBEC3G deaminates deoxycytidine nucleotides in the viral DNA, leading to hypermutations that interfere with the ability of the viral genome to integrate into the host genome and replicate. Interestingly, in opposition to an interfering activity for these mutated pro-viruses, it has been proposed that defective proviruses enhance fitness and that complementation among them drives viral persistence and pathogenesis 33,34 .Deletions. The genomic viral species most commonly referred to as DVGs are truncated viral genomes resulting from large internal deletions occurring during viral replication. Deletion DVGs tend to bear single large truncations that remove several or all essential genes required for self-propagation while retaining 5′ and 3′ ends as well as other RNA structural elements required for polymerase binding, replication and/or packaging [35][36][37] . Multiple variants also exist, including DVGs that can be described as 'mosaic' in that they appear to be the result of multiple recombination and rearrangement events, including deletions, insertions, duplications and even inversions of parts of the genome [38][39][40] (Fig. 1b). Copy-backs.Copy-back and snap-back DVGs are rearranged genomes in which a sequence is duplicated in reverse complement to create theoretical stem-like structures (panhandle structures for copy-back DVGs or hairpin structures for snap-back DVGs) 41-43 . Copy-back DVGs have been reported in many negative-sense (ns) RNA viruses and are generated from the 5′ end of the genome in a process that produces DVGs with complementary 3′ and 5′ termini 44,45 . If the complementary region of the DVG comprises almost the entire sequence, the DVG can be further characterized as a snap-back DVG 43 (Fig. 1c). Copy-back DVGs are predicted to be generated when the viral RNA-dependent RNA polymerase (RdRP) detaches from the template and reattaches to the nascent strand, copying back the end of the genome 42,46 .

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Section: Classes Types Structures and Diversitymentioning

confidence: 99%

Defective viral genomes are key drivers of the virus–host interaction

2019

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“…While this manuscript was in preparation, Beauclair et al, (2018)[36] reported development of an open-source bioinformatics DVG detection algorithm named DI-tector. DI-tector aligns the reads against a reference genome and examines non-perfectly aligned reads.…”

Section: Resultsmentioning

confidence: 99%

“…As mentioned, Beauclair et al ., [36] recently reported development of another open-source bioinformatics tool, named DI-tector, for identifying DVGs in HTS data. To demonstrate the ability of their tool to identify DVGs, the authors used publicly available HTS data from RNA extracted from Huh-7 cells infected with Sendai virus, another paramyxovirus (www.ncbi.nlm.nih.gov/sra, accession numbers SRX2600182 and SRX2600183).…”

Section: Resultsmentioning

confidence: 99%

“…We initially aligned the datasets to the same Sendai virus reference used by Beauclair et al . [36] (NC_001552.1). The most prevalent copyback DVG we identified was a well-known DVG (14932/15291) that is prevalent in the Cantell strain of Sendai virus [22].…”

Section: Resultsmentioning

confidence: 99%

“…To evaluate the performance of DVG-profiler against DI-tector, another recently published open-source bioinformatics DVG detection algorithm [36], 8 template sequences were generated using the same genome as described in S2 Table. To assess the sensitivity, five samples were composed of randomly generated reads from the reference sequence spiked with reads from dvg3 (S2 Table) in five different concentrations as described in S3 Table.…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2019

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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.

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Selection of nonstandard viral genomes during the evolution of RNA viruses: A virus survival strategy or a pesky inconvenience?

González Aparicio,

López

2024

Advances in Virus Research

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DI-tector: defective interfering viral genomes’ detector for next-generation sequencing data

Cited by 37 publications

References 43 publications

Defective viral genomes are key drivers of the virus–host interaction

Defective viral genomes are key drivers of the virus–host interaction

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

Selection of nonstandard viral genomes during the evolution of RNA viruses: A virus survival strategy or a pesky inconvenience?

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