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

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.

SUBMITTER: Lin HH

PROVIDER: S-EPMC5466706 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature

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

Lin Hsin-Hung HH Liao Yu-Chieh YC

GigaScience 20170201 2

<h4>Background</h4>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 ...[more]

PMID: 28369462

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Project description:BackgroundMetagenomics has transformed our understanding of microbial diversity across ecosystems, with recent advances enabling de novo assembly of genomes from metagenomes. These metagenome-assembled genomes are critical to provide ecological, evolutionary, and metabolic context for all the microbes and viruses yet to be cultivated. Metagenomes can now be generated from nanogram to subnanogram amounts of DNA. However, these libraries require several rounds of PCR amplification before sequencing, and recent data suggest these typically yield smaller and more fragmented assemblies than regular metagenomes.MethodsHere we evaluate de novo assembly methods of 169 PCR-amplified metagenomes, including 25 for which an unamplified counterpart is available, to optimize specific assembly approaches for PCR-amplified libraries. We first evaluated coverage bias by mapping reads from PCR-amplified metagenomes onto reference contigs obtained from unamplified metagenomes of the same samples. Then, we compared different assembly pipelines in terms of assembly size (number of bp in contigs ≥ 10 kb) and error rates to evaluate which are the best suited for PCR-amplified metagenomes.ResultsRead mapping analyses revealed that the depth of coverage within individual genomes is significantly more uneven in PCR-amplified datasets versus unamplified metagenomes, with regions of high depth of coverage enriched in short inserts. This enrichment scales with the number of PCR cycles performed, and is presumably due to preferential amplification of short inserts. Standard assembly pipelines are confounded by this type of coverage unevenness, so we evaluated other assembly options to mitigate these issues. We found that a pipeline combining read deduplication and an assembly algorithm originally designed to recover genomes from libraries generated after whole genome amplification (single-cell SPAdes) frequently improved assembly of contigs ≥10 kb by 10 to 100-fold for low input metagenomes.ConclusionsPCR-amplified metagenomes have enabled scientists to explore communities traditionally challenging to describe, including some with extremely low biomass or from which DNA is particularly difficult to extract. Here we show that a modified assembly pipeline can lead to an improved de novo genome assembly from PCR-amplified datasets, and enables a better genome recovery from low input metagenomes.

Dataset Information

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

Publications

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

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