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Infection, Genetics and Evolution
Other literature type . Article . 2018 . Peer-reviewed
License: Elsevier TDM
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Virus detection in high-throughput sequencing data without a reference genome of the host

Authors: Kruppa, Jochen; Jo, Wendy K; van der Vries, Erhard; Ludlow, Martin; Osterhaus, Albert; Baumgaertner, Wolfgang; Jung, Klaus; +3 Authors

Virus detection in high-throughput sequencing data without a reference genome of the host

Abstract

Discovery of novel viruses in host samples is a multidisciplinary process which relies increasingly on next-generation sequencing (NGS) followed by computational analysis. A crucial step in this analysis is to separate host sequence reads from the sequence reads of the virus to be discovered. This becomes especially difficult if no reference genome of the host is available. Furthermore, if the total number of viral reads in a sample is low, de novo assembly of a virus which is a requirement for most existing pipelines is hard to realize. We present a new modular, computational pipeline for discovery of novel viruses in host samples. While existing pipelines rely on the availability of the hosts reference genome for filtering sequence reads, our new pipeline can also cope with cases for which no reference genome is available. As a further novelty of our method a decoy module is used to assess false classification rates in the discovery process. Additionally, viruses with a low read coverage can be identified and visually reviewed. We validate our pipeline on simulated data as well as two experimental samples with known virus content. For the experimental samples, we were able to reproduce the laboratory findings. Our newly developed pipeline is applicable for virus detection in a wide range of host species. The three modules we present can either be incorporated individually in other pipelines or be used as a stand-alone pipeline. We are the first to present a decoy approach within a virus detection pipeline that can be used to assess error rates so that the quality of the final result can be judged. We provide an implementation of our modules via Github. However, the principle of the modules can easily be re-implemented by other researchers.

Country
Netherlands
Subjects by Vocabulary

Microsoft Academic Graph classification: Sequence assembly Computational biology Biology Genome Business process discovery Pipeline (software) Pipeline transport Metagenomics Host (network) Reference genome

Keywords

Microbiology (medical), Databases, Pharmaceutical, Genome, Viral, Microbiology, Genetics, Animals, Humans, Amino Acid Sequence, Virus discovery, Molecular Biology, Ecology, Evolution, Behavior and Systematics, Base Sequence, Computational Biology, High-Throughput Nucleotide Sequencing, Genomics, Decoy database, Infectious Diseases, Virus Diseases, Viruses, Read mapping, Metagenomics, Reference genome, Algorithms

  • BIP!
    Impact byBIP!
    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    9
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
  • citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    9
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
9
Top 10%
Average
Average
gold
Funded by
EC| COMPARE
Project
COMPARE
COllaborative Management Platform for detection and Analyses of (Re-)emerging and foodborne outbreaks in Europe
  • Funder: European Commission (EC)
  • Project Code: 643476
  • Funding stream: H2020 | RIA
Validated by funder
Related to Research communities
Sustainable Development Solutions Network - Greece
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