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. 2017 Feb 1;6(2):1-10.
doi: 10.1093/gigascience/gix003.

drVM: a new tool for efficient genome assembly of known eukaryotic viruses from metagenomes

drVM: a new tool for efficient genome assembly of known eukaryotic viruses from metagenomes

Hsin-Hung Lin et al. Gigascience. .

Abstract

Background: Virus discovery using high-throughput next-generation sequencing has become more commonplace. However, although analysis of deep next-generation sequencing data allows us to identity potential pathogens, the entire analytical procedure requires competency in the bioinformatics domain, which includes implementing proper software packages and preparing prerequisite databases. Simple and user-friendly bioinformatics pipelines are urgently required to obtain complete viral genome sequences from metagenomic data.

Results: This manuscript presents a pipeline, drVM (detect and reconstruct known viral genomes from metagenomes), for rapid viral read identification, genus-level read partition, read normalization, de novo assembly, sequence annotation, and coverage profiling. The first two procedures and sequence annotation rely on known viral genomes as a reference database. drVM was validated via the analysis of over 300 sequencing runs generated by Illumina and Ion Torrent platforms to provide complete viral genome assemblies for a variety of virus types including DNA viruses, RNA viruses, and retroviruses. drVM is available for free download at: https://sourceforge.net/projects/sb2nhri/files/drVM/ and is also assembled as a Docker container, an Amazon machine image, and a virtual machine to facilitate seamless deployment.

Conclusions: drVM was compared with other viral detection tools to demonstrate its merits in terms of viral genome completeness and reduced computation time. This substantiates the platform's potential to produce prompt and accurate viral genome sequences from clinical samples.

Keywords: bioinformatics; detect; metagenomics; next-generation sequencing (NGS); reconstruct.

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Figures

Figure 1.
Figure 1.
A schematic flowchart of drVM. CreateDB.py processes viral sequences to produce SNAP and BLAST databases. drVM.py analyzes NGS reads to produce viral genomes.
Figure 2.
Figure 2.
Input and output explanation of drVM. (A) Input associated with the command-line interface. (B) Input associated with the graphical user interface. (C) Output file structure generated by drVM. (D) Coverage profiles produced by drVM.
Figure 3.
Figure 3.
drVM results for read mixture simulations of human enterovirus (CA16), human rhinovirus (HRV), human respiratory syncytial virus (HRSV), and reads from ERR690488. (A-D) 20X-40X-80X: 741 CA16, 1430 HRV, and 6090 HRSV reads. (E-H) 20X-20X-20X: 741 CA16, 715 HRV, and 1522 HRSV reads.

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