A library preparation optimized for metagenomics of RNA viruses

Abstract

Our understanding of the viral communities associated to animals has not yet reached the level attained on the bacteriome. This situation is due to, among others, technical challenges in adapting metagenomics using high-throughput sequencing to the study of RNA viromes in animals. Although important developments have been achieved in most steps of viral metagenomics, there is yet a key step that has received little attention: the library preparation. This situation differs from bacteriome studies in which developments in library preparation have largely contributed to the democratisation of metagenomics. Here, we present a library preparation optimized for metagenomics of RNA viruses from insect vectors of viral diseases. The library design allows a simple PCR-based preparation, such as those routinely used in bacterial metabarcoding, that is adapted to shotgun sequencing as required in viral metagenomics. We first optimized our library preparation using mock viral communities and then validated a full metagenomic approach incorporating our preparation in two pilot studies with field-caught insect vectors; one including a comparison with a published metagenomic protocol. Our approach provided a fold increase in virus-like sequences compared to other studies, and nearly-full genomes from new virus species. Moreover, our results suggested conserved trends in virome composition within a population of a mosquito species. Finally, the sensitivity of our approach was compared to a commercial diagnostic PCR for the detection of an arbovirus in field-caught insect vectors. Our approach could facilitate studies on viral communities from animals and the democratization of metagenomics in community ecology of viruses.

Spatial scale influences taxon conservation in the eukaryotic virome of a mosquito

“…Duplicate reads had been previously removed with the markdup tool in Samtools (47). Contigs sharing the same best hit were grouped into a viral operational taxonomic unit (vOTU) as previously described (27, 44). Names of vOTUs were generated through combining the best-hit name and the mean percent identity at the amino-acid level of the contigs with the best hit.…”

“…To do so, the vOTUs in the ten libraries of this study were compared to those found in six libraries sequenced in parallel and processed in the same laboratory for another project. These libraries derived from adult females of the mosquito Aedes vexans from Senegal and have been previously described (44). This comparison found 25 vOTUs in common.…”

“…We used an updated version of the SnakeVir bioinformatics pipeline to identify potential viral sequences from reads (44). Briefly, library adapters and low-quality sequences in reads were removed with Cutadapt 1.6 (Martin, 2011).…”

“…The taxonomy of vOTUs was generated with SnakeVir (44). To avoid incomplete virus taxonomies, the vOTUs were grouped into an arbitrary taxonomic level, here called "cluster", similar to the Family rank (44). The cluster was defined as the family of the best hit whenever available in GenBank databases.…”

“…Two arbitrary detection thresholds were applied to the list of vOTUs as previously described (44). First, a vOTUs was considered non-detected in a given library when the number of reads was below ten.…”