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  • Identification of known and novel recurrent viral sequences in data from multiple patients and multiple cancers

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    WillerslevPUB1288.pdf (1.276Mb)
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    Version of Record (VoR)
    Author(s)
    Friis-Nielsen, Jens
    Kjartansdottir, Kristin Ros
    Mollerup, Sarah
    Asplund, Maria
    Mourier, Tobias
    Jensen, Randi Holm
    Hansen, Thomas Arn
    Rey-Iglesia, Alba
    Richter, Stine Raith
    Nielsen, Ida Broman
    Alquezar-Planas, David E.
    Olsen, Pernille V. S.
    Vinner, Lasse
    Fridholm, Helena
    Nielsen, Lars Peter
    Willerslev, Eske
    Sicheritz-Ponten, Thomas
    Lund, Ole
    Hansen, Anders Johannes
    Izarzugaza, Jose M. G.
    Brunak, Soren
    Griffith University Author(s)
    Willerslev, Eske
    Year published
    2016
    Metadata
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    Abstract
    Virus discovery from high throughput sequencing data often follows a bottom-up approach where taxonomic annotation takes place prior to association to disease. Albeit effective in some cases, the approach fails to detect novel pathogens and remote variants not present in reference databases. We have developed a species independent pipeline that utilises sequence clustering for the identification of nucleotide sequences that co-occur across multiple sequencing data instances. We applied the workflow to 686 sequencing libraries from 252 cancer samples of different cancer and tissue types, 32 non-template controls, and 24 test ...
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    Virus discovery from high throughput sequencing data often follows a bottom-up approach where taxonomic annotation takes place prior to association to disease. Albeit effective in some cases, the approach fails to detect novel pathogens and remote variants not present in reference databases. We have developed a species independent pipeline that utilises sequence clustering for the identification of nucleotide sequences that co-occur across multiple sequencing data instances. We applied the workflow to 686 sequencing libraries from 252 cancer samples of different cancer and tissue types, 32 non-template controls, and 24 test samples. Recurrent sequences were statistically associated to biological, methodological or technical features with the aim to identify novel pathogens or plausible contaminants that may associate to a particular kit or method. We provide examples of identified inhabitants of the healthy tissue flora as well as experimental contaminants. Unmapped sequences that co-occur with high statistical significance potentially represent the unknown sequence space where novel pathogens can be identified.
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    Journal Title
    Viruses
    Volume
    8
    Issue
    2
    DOI
    https://doi.org/10.3390/V8020053
    Copyright Statement
    © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons by Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
    Subject
    Microbiology not elsewhere classified
    Microbiology
    Publication URI
    http://hdl.handle.net/10072/172156
    Collection
    • Journal articles

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