2014
DOI: 10.1371/journal.pone.0105227
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Metagenomic Survey for Viruses in Western Arctic Caribou, Alaska, through Iterative Assembly of Taxonomic Units

Abstract: Pathogen surveillance in animals does not provide a sufficient level of vigilance because it is generally confined to surveillance of pathogens with known economic impact in domestic animals and practically nonexistent in wildlife species. As most (re-)emerging viral infections originate from animal sources, it is important to obtain insight into viral pathogens present in the wildlife reservoir from a public health perspective. When monitoring living, free-ranging wildlife for viruses, sample collection can b… Show more

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Cited by 20 publications
(27 citation statements)
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“…Obtained reads were analysed by BLASTN and BLASTX with a bioinformatics virus discovery pipeline using standard parameters as previously described [58]. The European Nucleotide Archive Study accession number of this study is PRJEB19187, with secondary study accession number ERP021170.…”
Section: Methodsmentioning
confidence: 99%
“…Obtained reads were analysed by BLASTN and BLASTX with a bioinformatics virus discovery pipeline using standard parameters as previously described [58]. The European Nucleotide Archive Study accession number of this study is PRJEB19187, with secondary study accession number ERP021170.…”
Section: Methodsmentioning
confidence: 99%
“…The investigation was performed on frozen brain and liver tissue of one pheasant with a lymphohistiocytic hepatitis and a periocular dermatitis and on periocular skin of another pheasant presenting with periocular dermatitis and conjunctivitis. Obtained reads were analyzed by blastN and blastX analysis (Schurch et al 2014).…”
Section: Microbiologymentioning
confidence: 99%
“…De novo assemblies of MG data alone usually result in a large fraction of reads that are unmappable to the assembled contigs and therefore remain unused, thereby leading to suboptimal data usage [43, 58–60]. Previous studies have assembled sets of unmappable reads iteratively to successfully obtain additional contigs, leading to an overall increase in the number of predicted genes, which in turn results in improved data usage [43, 58–60].…”
Section: Resultsmentioning
confidence: 99%
“…Previous studies have assembled sets of unmappable reads iteratively to successfully obtain additional contigs, leading to an overall increase in the number of predicted genes, which in turn results in improved data usage [43, 58–60]. Therefore, IMP uses an iterative assembly strategy to maximize NGS read usage.…”
Section: Resultsmentioning
confidence: 99%