TY - JOUR

T1 - Identification of known and novel recurrent viral sequences in data from multiple patients and multiple cancers

AU - Friis-Nielsen, Jens

AU - Kjartansdóttir, Kristin Rós

AU - Mollerup, Sarah

AU - Asplund, Maria

AU - Mourier, Tobias

AU - Jensen, Randi Holm

AU - Hansen, Thomas Arn

AU - Rey de la Iglesia, Alba

AU - Richter, Stine Raith

AU - Nielsen, Ida Broman

AU - Alquezar Planas, David Eugenio

AU - Olsen, Pernille Vibeke Selmer

AU - Vinner, Lasse

AU - Fridholm, Eva Marie Helena

AU - Nielsen, Lars Peter

AU - Willerslev, Eske

AU - Sicheritz-Pontén, Thomas

AU - Lund, Ole

AU - Hansen, Anders Johannes

AU - Izarzugaza, Jose M. G.

AU - Brunak, Søren

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

KW - Journal Article

KW - Research Support, Non-U.S. Gov't

U2 - 10.3390/v8020053

DO - 10.3390/v8020053

M3 - Journal article

C2 - 26907326

VL - 8

JO - Viruses

JF - Viruses

SN - 1999-4915

IS - 2

M1 - 53

ER -