About: Identification of Known and Novel Recurrent Viral Sequences in Data from Multiple Patients and Multiple Cancers

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Identification of Known and Novel Recurrent Viral Sequences in Data from Multiple Patients and Multiple Cancers
Creator	Fridholm, Helena Mollerup, Sarah Mourier, Tobias Vinner, Lasse Willerslev, Eske Asplund, Maria Brunak, Søren Friis-Nielsen, Jens Lund, Ole Rey-Iglesia, Alba Sicheritz-Pontén, Thomas Alquezar-Planas, David Gilbert, Marcus Hansen, Anders Hansen, Thomas Holm Jensen, Randi Izarzugaza, Jose Nielsen, Ida Nielsen, Lars Olsen, Pernille Richter, Stine Rós Kjartansdóttir, Kristín
source	PMC
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 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.
has issue date	2016-02-19(xsd:dateTime)
bibo:doi	10.3390/v8020053
bibo:pmid	26907326
has license	cc-by
sha1sum (hex)	9c8a4fff809abaa0b5ede6a28db62da5edd082e1
schema:url	https://doi.org/10.3390/v8020053
resource representing a document's title	Identification of Known and Novel Recurrent Viral Sequences in Data from Multiple Patients and Multiple Cancers
has PubMed Central identifier	PMC4776208
has PubMed identifier	26907326
schema:publication	Viruses
resource representing a document's body	covid:9c8a4fff809abaa0b5ede6a28db62da5edd082e1#body_text
is schema:about of	named entity 'pathogens' named entity 'sequences' named entity 'fails' named entity 'inhabitants' named entity 'sequence space' named entity 'nucleotide' named entity 'VIRAL' named entity 'ACROSS' named entity 'OCCUR' named entity 'ASSOCIATED' named entity 'TEMPLATE' named entity 'LIBRARIES' named entity 'NON-' named entity 'EFFECTIVE' named entity 'TECHNICAL' named entity 'RECURRENT' named entity 'TISSUE TYPES' named entity 'WORKFLOW' named entity 'FLORA' named entity 'METHOD' named entity 'IDENTIFICATION' named entity 'HAVE' named entity 'VARIANTS' named entity 'AIM' named entity 'NOT PRESENT' named entity 'WHERE' named entity 'SEQUENCING DATA' named entity 'ASSOCIATE' named entity 'BIOLOGICAL' named entity 'PARTICULAR' named entity 'FOLLOWS' named entity 'STATISTICALLY' named entity 'TO DETECT' named entity 'APPROACH' named entity 'NUCLEOTIDE SEQUENCES' named entity '686' named entity 'UNKNOWN SEQUENCE' named entity 'DATABASES' named entity 'FAILS' named entity 'REFERENCE' named entity 'SPECIES' named entity 'REMOTE' named entity 'TO IDENTIFY' named entity 'EXPERIMENTAL' named entity 'SEQUENCE CLUSTERING' named entity 'KIT' named entity 'DISEASE' named entity 'APPLIED' named entity 'RECURRENT' named entity 'TISSUE' named entity 'HIGH' named entity 'ANNOTATION' named entity 'DIFFERENT' named entity 'SEQUENCING' named entity 'CANCER' named entity 'FEATURES' named entity 'SAMPLES' named entity 'CASES' named entity 'CONTAMINANTS' named entity 'CANCERS'

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