VIROME: a standard operating procedure for analysis of viral metagenome sequences

By K. Eric Wommack, Jaysheel D. Bhavsar, Shawn W. Polson, J. Chen, Michael D. Dumas, Sharath Srinivasiah, M. Furman, S. Jamindar, Daniel J. Nasko. 2012

183 citations recorded by the source corpus.

One consistent finding among studies using shotgun metagenomics to analyze whole viral communities is that most viral sequences show no significant homology to known sequences. Thus, bioinformatic analyses based on sequence collections such as GenBank nr, which are largely comprised of sequences from known organisms, tend to ignore a majority of sequences within most shotgun viral metagenome libraries. Here we describe a bioinformatic pipeline, the Viral Informatics Resource for Metagenome Exploration (VIROME), that emphasizes the classification of viral metagenome sequences (predicted open-reading frames) based on homology search results against both known and environmental sequences. Functional and taxonomic information is derived from five annotated sequence databases which are linked to the UniRef 100 database. Environmental classifications are obtained from hits against a custom database, MetaGenomes On-Line, which contains 49 million predicted environmental peptides. Each predicted viral metagenomic ORF run through the VIROME pipeline is placed into one of seven ORF classes, thus, every sequence receives a meaningful annotation. Additionally, the pipeline includes quality control measures to remove contaminating and poor quality sequence and assesses the potential amount of cellular DNA contamination in a viral metagenome library by screening for rRNA genes. Access to the VIROME pipeline and analysis results are provided through a web-application interface that is dynamically linked to a relational back-end database. The VIROME web-application interface is designed to allow users flexibility in retrieving sequences (reads, ORFs, predicted peptides) and search results for focused secondary analyses.

Subfields: Animal Science and Zoology, Ecology, Molecular Biology

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Citations in the corpus

Identifying viruses from metagenomic data using deep learning (2020, methodology): “Gene homology-based methods, VIROME [9], DIA-MOND [13], VirSorter [4], JGI Earth Virome Pipeline [19], MARVEL [6], and most provirus ﬁ nding tools, Prophinder [15], Phage-Finder [14], PhiSpy [16], and PHASTER [17], determine viral sequences mainly by comparing genes in the query sequence against…”
The Pacific Ocean Virome (POV): A Marine Viral Metagenomic Dataset and Associated Protein Clusters for Quantitative Viral Ecology (2013, background): “On the latter, viral metagenomics is more difficult than microbial metagenomics as (i) viral genome representation in public databases is more sparse (as of November 2012 there are 115 marine phage genomes in Genbank, Table S1), (ii) analytical tools paralleling those for microbes (e.g., MG-RAST [21”
The First Microbial Colonizers of the Human Gut: Composition, Activities, and Health Implications of the Infant Gut Microbiota (2017, background): “tion of virus-associated sequences in metagenomic data sets has to overcome the challenge of detecting relationships between extremely divergent sequences, where the majority of genes (up to 90%) identified in viral metagenomic data sets have no known function (476, 477).”
VirFinder: a novel k-mer based tool for identifying viral sequences from assembled metagenomic data (2017, methodology): “The tool VIROME, while built to specifically analyze viral fraction metagenomes, categorizes predicted proteins as microbial or viral via blast searches against metagenomic databases and UniRef proteins [16].”
VIBRANT: automated recovery, annotation and curation of microbial viruses, and evaluation of viral community function from genomic sequences (2020, methodology): “For several years, VirSorter [37], which succeeded tools such as VIROME [38] and Metavir [39], has been the most widely used for its ability to identify viral metagenomic fragments (scaffolds) from large metagenomic assemblies.”