ViromeScan: a new tool for metagenomic viral community profiling
© Rampelli et al. 2016
Received: 16 October 2015
Accepted: 8 February 2016
Published: 1 March 2016
Bioinformatics tools available for metagenomic sequencing analysis are principally devoted to the identification of microorganisms populating an ecological niche, but they usually do not consider viruses. Only some software have been designed to profile the viral sequences, however they are not efficient in the characterization of viruses in the context of complex communities, like the intestinal microbiota, containing bacteria, archeabacteria, eukaryotic microorganisms and viruses. In any case, a comprehensive description of the host-microbiota interactions can not ignore the profile of eukaryotic viruses within the virome, as viruses are definitely critical for the regulation of the host immunophenotype.
ViromeScan is an innovative metagenomic analysis tool that characterizes the taxonomy of the virome directly from raw data of next-generation sequencing. The tool uses hierarchical databases for eukaryotic viruses to unambiguously assign reads to viral species more accurately and >1000 fold faster than other existing approaches. We validated ViromeScan on synthetic microbial communities and applied it on metagenomic samples of the Human Microbiome Project, providing a sensitive eukaryotic virome profiling of different human body sites.
ViromeScan allows the user to explore and taxonomically characterize the virome from metagenomic reads, efficiently denoising samples from reads of other microorganisms. This implies that users can fully characterize the microbiome, including bacteria and viruses, by shotgun metagenomic sequencing followed by different bioinformatic pipelines.
KeywordsVirome Microbiome Metagenomics
Viruses constantly inhabit our body . Even asymptomatic hosts harbor viral communities and interactions between viruses and the host do not always end with the death of the virus-infected cells [2, 3]. This emerging vision raises the need to explore the virome as a significant part of our biology, which can profoundly influence the host in several ways, also other than the classical viral infections [1, 2]. For example, like bacteria and fungi, certain viruses can stimulate a low level of immune responses, which are important for the long-term modulation of the human immunological but also metabolic homeostasis. In this regard, Foxman and Iwasaki  showed that a constant reinfection by common low-virulence viruses stimulates antiviral components of the immune system, which correlate with susceptibility to diseases, such as type 1 diabetes and asthma. On the other hand, it has been reported that the viruses usually present in the acute infection of the nasopharynx, are also commonly detected in healthy individuals . Taken together, these data contribute to describe the host-virome interplay as a complex relationship that generally encompasses symbiosis, sometimes with a pathogenic outcome, and can profoundly impact on host health and disease. For these reasons, profiling the taxonomic and phylogenetic composition of such viral communities is pivotal not only to better understand their role in the biology of the human holobiont but also to open new possibilities in the interpretation of complex disorders, with the ultimate goal of eradicating them [1, 4, 6–13].
Even if the importance of the interplay among eukaryotic virome, microbiome and immune system is already evident, the available techniques for virome characterization usually underestimate the quantity and diversity of viruses in the samples . For example, it is recognized that the methods for the viral isolation based on filtering procedures miss giant virus . Viral communities are also difficult to be characterized since there is not a single gene common to all viral genomes, which prevents the application of analogous approaches to ribosomal DNA profiling for bacteria . The viral taxonomic composition of a microbial community could be estimated from metagenomic shotgun sequencing and RNA-seq of the microbiota DNA/RNA, by detecting and assigning the viral reads to the appropriate viral taxa. Metagenomic samples contain indeed nucleic acids for bacteria, archeabacteria, eukaryotes, phages and eukaryotic viruses. However, currently the most advanced experimental procedures foresee to extract and isolate the encapsidated viral fraction [16–18] and only at a later stage, to characterize the metavirome by assembled or read-mapping approaches [19–22]. To sequence unprocessed samples and directly assign the obtained reads would instead allow a faster characterization of the virome in the context of the microbiome of origin and no risk to miss giant virus due to filtering procedures. We thus present ViromeScan, a new tool that accurately profiles viral communities and requires only few minutes to process thousands of metagenomics reads. ViromeScan works with shotgun reads to detect traces of DNA and/or RNA viruses, depending on the input sequences to be processed. ViromeScan has been developed to profile the eukaryotic viral community within the microbiome, in particular it estimates the relative abundance of viruses by filtering out the metagenomic reads of human and bacterial provenance, and mapping the remaining sequences on a hierarchical viral database. ViromeScan is available at the website http://sourceforge.net/projects/viromescan/.
Workflow of the software
Once downloaded, ViromeScan locally processes the metagenome for searching eukaryotic viral sequences. Input files should be single-end or paired-end reads in fastq format (for paired-end reads compressed files in gzip, bzip2 and zip formats are also accepted) retrieved from shotgun sequencing or RNA-seq. Depending on the research strategy, ViromeScan gives users the option to choose from a range of in-house built reference databases, including human DNA virus database, human DNA/RNA virus database, eukaryotic DNA virus database and eukaryotic DNA/RNA virus database. The human virus databases contain only viruses that have the human being as the natural host; on the other hand, the eukaryotic virus databases also include viruses for vertebrates, invertebrates, fungi, algae and plants, while excluding bacteriophages. All databases are based on the complete viral genomes available on the NCBI website . The NCBI IDs of the viral genomes used to build the different databases are reported in Additional file 1.
Since the sequences analyzed are whole-genome or RNA-seq products, it is plausible that the candidate viral reads contain a small percentage of human reads. For this reason, it is necessary to subject the sequences to the control for human contamination. As reported in the HMP procedures , Human Best Match Tagger (BMTagger)  is an efficient tool that discriminates among human, viral and microbial reads. First of all, BMTagger attempts to discriminate between human reads and the other reads by comparing the 18mers produced from the input file with those contained in the reference human database. If this process fails, an additional alignment procedure is performed to guarantee the detection of all matches with up to two errors.
Human-filtered reads may also contain an amount of bacterial sequences, which need to be filtered out to avoid biases due to bacterial contamination. Bacterial reads are identified and masked using BMTagger, the same tool utilized for the human sequence removal procedure. In particular, in order to detect bacterial sequences, human-filtered reads are screened against the genomic DNA of a representative group of bacterial taxa that are known to be common in the human body niches. See Additional file 2 for the list of bacteria included in this process. Nevertheless, the user can customize the filtering procedure by replacing the bacterial database within the ViromeScan folder with the microbial sequences of interest, associated to environments other than the human body (e.g. microbiome associated with animals, soil or water).
Finally, filtered reads are again compared to the viral genomes of the chosen hierarchical viral database using bowtie2 , allowing the definitive association of each virome sequence to a viral genome. For each sample analyzed, the total amount of counts is summarized in a table as number of hits and relative abundance. Additionally, graphs representing the abundances at family, genus and species level are provided, using the “graphics” and “base” R packages.
Validation of the tool and comparison with other existing methods
Five different mock communities each containing 20 human DNA viruses at different relative abundances were built and submitted to ViromeScan for its validation. The mock communities contained also human sequences and reads of other microorganisms to test the filtering steps of the pipeline. The simulated metagenomes were composed of sequences of 100 bp randomly generated from the chosen genomic DNAs by an in-house developed script. In order to compare the performance of ViromeScan with other existing tools, the same mock samples were analyzed using Metavir  and blastN . In particular, in the Metavir pipeline, we determined the taxonomic composition using the number of best hits normalized by genome length through the GAAS metagenomic tool . The genomes used to generate the five mock communities are reported in Additional file 3.
Case study: using ViromeScan to profile the eukaryotic DNA virome across different human body sites
Twenty metagenomic samples from HMP , belonging to four body sites, including stool, mid vagina, buccal mucosa and retroauricular crease, were used to illustrate the results that can be obtained by ViromeScan. The IDs of HMP samples are reported in Additional file 4. These metagenomes had been sequenced using the Illumina GAIIx platform with 101 bp paired-end reads. The entire metagenomic dataset was utilized to study the differences in the composition of the viral communities across different body sites. No ethics approval was required for any work performed in this study.
Results and discussion
The ViromeScan software is specifically dedicated to the analysis of the virome. In particular, it can be used to detect viruses inside the microbiome from a given environment utilizing raw reads, mostly in fastq format generated by next-generation sequencing technologies. ViromeScan has the advantage of using a read-mapping approach that allows i) the characterization of the virome within a metagenome, including bacterial, eukaryotic and host sequences, without specific extraction/purification strategies, and ii) the preservation of all the information retained in the input files, information that may be lost by an assembly approach . In particular, in the context of a metagenomic dataset, the viral DNA could be undersequenced due to the huge amount of bacterial and human DNA in the samples, making the assembly difficult or even impossible for viruses with a limited number of reads. However, as all the existing read-mapping approaches, ViromeScan is blind to viral sequences that are not closely related to viruses already present in the repository. For this reason and in the light of the considerable number of unknown viruses inside a metagenomic sample [32–34], the integration of a read-mapping and an assembling approach could be pursued to retrieve a more exhaustive virome profile of metagenomic samples.
ViromeScan determines the taxonomic composition of the virome by sequence alignment of the reads to completely known viral genomes, and displays the results as either number of hits or normalized hits (relative abundance). The ViromeScan classifier can be used for multiple analysis of the virome, in particular the normalized results describe the structure of the viral community in terms of relative abundance, and the read count output defines the richness and diversity of such community in the context of the metagenome of origin. The initial choice of the appropriate reference database is possible because the hierarchical databases built within ViromeScan contain sequences for DNA or DNA/RNA eukaryotic viruses, making the tool very adaptable to the needs of the user. Specifically, 92 genomes for the human DNA virus database, 664 for the human DNA/RNA virus database, 1646 for the eukaryotic DNA virus database and 4370 for the eukaryotic DNA/RNA virus database were employed in the construction of the tool. In addition, every ViromeScan user can create his/her own database for a customized analysis, including assembled sequences of unknown viruses, which could be useful to extend the taxon detection limit of the tool. Finally, another advantage of the ViromeScan tool is that the same metagenomic sample utilized to characterize bacteria, archeabacteria and eukarya within the microbiome, can be used for the viral profiling, opening new perspectives in metagenomic characterization studies.
ViromeScan provides the users with new perspectives in the virome characterization analysis. Shotgun metagenomics and RNAseq techniques are rapidly decreasing in cost and already supply a community-wide profiling of the bacterial, archeal and eukaryotic microbiome. By enabling an efficient detection of the viral counterpart from shotgun sequencing, ViromeScan extensively integrates the analysis of the microorganisms that inhabit the human body. Furthermore, ViromeScan can be applied to any environment as a tool for taxonomic profiling of the virome with resolution up to species level. An interesting and flexible aspect for users is that the pipeline of analysis can also be used with a customized database containing viral genomes of interest. However, this version of the tool remains blind to new viruses, which are not present in the database. For this reason, the pipeline will be integrated in the near future with an optional and parallel assembling step to identify unknown viruses within the metagenome. The ViromeScan databases of eukaryotic and human viruses will be periodically updated based on new genome releases.
Availability and requirements
Project name: ViromeScan
Project home page: http://sourceforge.net/projects/viromescan/
Operating systems: Linux, OS X
Programming language: Bash, R, Perl, Java
Other requirements: Bowtie2, Bmtagger tools from NCBI, Picard tools
Any restriction to use by non-academics: No
human best match tagger
end anchored max scoring segments
genome relative abundance and average size
human microbiome project
This work was supported by the European Union’s Seventh Framework Programme (FP7) under grant agreement n. 613979 (MyNewGut).
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
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