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virMine: automated detection of viral sequences from complex metagenomic samples

Metagenomics has enabled sequencing of viral communities from a myriad of different environments. Viral metagenomic studies routinely uncover sequences with no recognizable homology to known coding regions or genomes. Nevertheless, complete viral genomes have been constructed directly from complex c...

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Detalles Bibliográficos
Autores principales: Garretto, Andrea, Hatzopoulos, Thomas, Putonti, Catherine
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6462185/
https://www.ncbi.nlm.nih.gov/pubmed/30993039
http://dx.doi.org/10.7717/peerj.6695
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author Garretto, Andrea
Hatzopoulos, Thomas
Putonti, Catherine
author_facet Garretto, Andrea
Hatzopoulos, Thomas
Putonti, Catherine
author_sort Garretto, Andrea
collection PubMed
description Metagenomics has enabled sequencing of viral communities from a myriad of different environments. Viral metagenomic studies routinely uncover sequences with no recognizable homology to known coding regions or genomes. Nevertheless, complete viral genomes have been constructed directly from complex community metagenomes, often through tedious manual curation. To address this, we developed the software tool virMine to identify viral genomes from raw reads representative of viral or mixed (viral and bacterial) communities. virMine automates sequence read quality control, assembly, and annotation. Researchers can easily refine their search for a specific study system and/or feature(s) of interest. In contrast to other viral genome detection tools that often rely on the recognition of viral signature sequences, virMine is not restricted by the insufficient representation of viral diversity in public data repositories. Rather, viral genomes are identified through an iterative approach, first omitting non-viral sequences. Thus, both relatives of previously characterized viruses and novel species can be detected, including both eukaryotic viruses and bacteriophages. Here we present virMine and its analysis of synthetic communities as well as metagenomic data sets from three distinctly different environments: the gut microbiota, the urinary microbiota, and freshwater viromes. Several new viral genomes were identified and annotated, thus contributing to our understanding of viral genetic diversity in these three environments.
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spelling pubmed-64621852019-04-16 virMine: automated detection of viral sequences from complex metagenomic samples Garretto, Andrea Hatzopoulos, Thomas Putonti, Catherine PeerJ Bioinformatics Metagenomics has enabled sequencing of viral communities from a myriad of different environments. Viral metagenomic studies routinely uncover sequences with no recognizable homology to known coding regions or genomes. Nevertheless, complete viral genomes have been constructed directly from complex community metagenomes, often through tedious manual curation. To address this, we developed the software tool virMine to identify viral genomes from raw reads representative of viral or mixed (viral and bacterial) communities. virMine automates sequence read quality control, assembly, and annotation. Researchers can easily refine their search for a specific study system and/or feature(s) of interest. In contrast to other viral genome detection tools that often rely on the recognition of viral signature sequences, virMine is not restricted by the insufficient representation of viral diversity in public data repositories. Rather, viral genomes are identified through an iterative approach, first omitting non-viral sequences. Thus, both relatives of previously characterized viruses and novel species can be detected, including both eukaryotic viruses and bacteriophages. Here we present virMine and its analysis of synthetic communities as well as metagenomic data sets from three distinctly different environments: the gut microbiota, the urinary microbiota, and freshwater viromes. Several new viral genomes were identified and annotated, thus contributing to our understanding of viral genetic diversity in these three environments. PeerJ Inc. 2019-04-10 /pmc/articles/PMC6462185/ /pubmed/30993039 http://dx.doi.org/10.7717/peerj.6695 Text en ©2019 Garretto et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Bioinformatics
Garretto, Andrea
Hatzopoulos, Thomas
Putonti, Catherine
virMine: automated detection of viral sequences from complex metagenomic samples
title virMine: automated detection of viral sequences from complex metagenomic samples
title_full virMine: automated detection of viral sequences from complex metagenomic samples
title_fullStr virMine: automated detection of viral sequences from complex metagenomic samples
title_full_unstemmed virMine: automated detection of viral sequences from complex metagenomic samples
title_short virMine: automated detection of viral sequences from complex metagenomic samples
title_sort virmine: automated detection of viral sequences from complex metagenomic samples
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6462185/
https://www.ncbi.nlm.nih.gov/pubmed/30993039
http://dx.doi.org/10.7717/peerj.6695
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