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

Research output: Contribution to journalJournal articleResearchpeer-review

  • Jens Friis-Nielsen
  • Kristin Rós Kjartansdóttir
  • Sarah Mollerup
  • Maria Asplund
  • Tobias Mourier
  • Randi Holm Jensen
  • Thomas Arn Hansen
  • Stine Raith Richter
  • David Eugenio Alquezar Planas
  • Eva Marie Helena Fridholm
  • Lars Peter Nielsen
  • Thomas Sicheritz-Pontén
  • Ole Lund
  • Jose M. G. Izarzugaza

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.

Original languageEnglish
Article number53
Issue number2
Number of pages16
Publication statusPublished - 2016

    Research areas

  • Journal Article, Research Support, Non-U.S. Gov't

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