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dc.contributor.authorFarmery, James HRen
dc.contributor.authorSmith, Mike Len
dc.contributor.authorNIHR BioResource - Rare Diseases,en
dc.contributor.authorLynch, Andyen
dc.date.accessioned2018-05-02T10:42:02Z
dc.date.available2018-05-02T10:42:02Z
dc.date.issued2018-01-22en
dc.identifier.issn2045-2322
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/275426
dc.description.abstractTelomere length is a risk factor in disease and the dynamics of telomere length are crucial to our understanding of cell replication and vitality. The proliferation of whole genome sequencing represents an unprecedented opportunity to glean new insights into telomere biology on a previously unimaginable scale. To this end, a number of approaches for estimating telomere length from whole-genome sequencing data have been proposed. Here we present Telomerecat, a novel approach to the estimation of telomere length. Previous methods have been dependent on the number of telomeres present in a cell being known, which may be problematic when analysing aneuploid cancer data and non-human samples. Telomerecat is designed to be agnostic to the number of telomeres present, making it suited for the purpose of estimating telomere length in cancer studies. Telomerecat also accounts for interstitial telomeric reads and presents a novel approach to dealing with sequencing errors. We show that Telomerecat performs well at telomere length estimation when compared to leading experimental and computational methods. Furthermore, we show that it detects expected patterns in longitudinal data, repeated measurements, and cross-species comparisons. We also apply the method to a cancer cell data, uncovering an interesting relationship with the underlying telomerase genotype.
dc.format.mediumElectronicen
dc.languageengen
dc.publisherNature Publishing Group
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectNIHR BioResource - Rare Diseasesen
dc.subjectTelomereen
dc.subjectMesenchymal Stem Cellsen
dc.subjectHumansen
dc.subjectCarcinoma, Hepatocellularen
dc.subjectLiver Neoplasmsen
dc.subjectTelomeraseen
dc.subjectGene Expressionen
dc.subjectGenotypeen
dc.subjectPloidiesen
dc.subjectAlgorithmsen
dc.subjectInduced Pluripotent Stem Cellsen
dc.subjectPrimary Cell Cultureen
dc.subjectTelomere Homeostasisen
dc.subjectWhole Genome Sequencingen
dc.titleTelomerecat: A ploidy-agnostic method for estimating telomere length from whole genome sequencing data.en
dc.typeArticle
prism.issueIdentifier1en
prism.publicationDate2018en
prism.publicationNameScientific reportsen
prism.startingPage1300
prism.volume8en
dc.identifier.doi10.17863/CAM.22637
dcterms.dateAccepted2017-09-22en
rioxxterms.versionofrecord10.1038/s41598-017-14403-yen
rioxxterms.versionVoR*
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2018-01-22en
dc.contributor.orcidLynch, Andrew [0000-0002-7876-7338]
dc.identifier.eissn2045-2322
rioxxterms.typeJournal Article/Reviewen
pubs.funder-project-idCancer Research UK (C14303_do not transfer)
pubs.funder-project-idCambridge University Hospitals NHS Foundation Trust (CUH) (BRC 2012-2017)
pubs.funder-project-idMRC (MR/L006197/1)
pubs.funder-project-idEC FP7 CP (305626)
pubs.funder-project-idEuropean Commission Horizon 2020 (H2020) Societal Challenges (633974)
cam.orpheus.successThu Jan 30 12:59:23 GMT 2020 - The item has an open VoR version.*
rioxxterms.freetoread.startdate2100-01-01


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International