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 - 2016/2/1
Y1 - 2016/2/1
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
SN - 1999-4915
VL - 8
JO - Viruses
JF - Viruses
IS - 2
M1 - 53
ER -