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dc.contributor.authorFarmery, James
dc.contributor.authorSmith, Mike
dc.contributor.authorNIHR BioResource - Rare Diseases
dc.contributor.authorLynch, Andy
dc.date.accessioned2018-01-23T15:30:10Z
dc.date.available2018-01-23T15:30:10Z
dc.date.issued2018-01-22
dc.identifier251746309
dc.identifier96a95404-9ab6-4dcd-91d8-ec5295451131
dc.identifier85040985054
dc.identifier000422904600013
dc.identifier.citationFarmery , J , Smith , M , NIHR BioResource - Rare Diseases & Lynch , A 2018 , ' Telomerecat : a ploidy-agnostic method for estimating telomere length from whole genome sequencing data ' , Scientific Reports , vol. 8 , 1300 . https://doi.org/10.1038/s41598-017-14403-yen
dc.identifier.issn2045-2322
dc.identifier.otherORCID: /0000-0002-7876-7338/work/41026514
dc.identifier.urihttps://hdl.handle.net/10023/12591
dc.descriptionFunding: Cancer Research UK Programme Grant to Simon Tavaré (C14303/A17197) (JHRF, AGL, MLS); European Commission through the Horizon 2020 project SOUND (Grant Agreement no. 633974) (AGL).en
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.extent17
dc.format.extent3013643
dc.language.isoeng
dc.relation.ispartofScientific Reportsen
dc.subjectQH301 Biologyen
dc.subjectRC0254 Neoplasms. Tumors. Oncology (including Cancer)en
dc.subjectT Technologyen
dc.subjectDASen
dc.subjectSDG 3 - Good Health and Well-beingen
dc.subject.lccQH301en
dc.subject.lccRC0254en
dc.subject.lccTen
dc.titleTelomerecat : a ploidy-agnostic method for estimating telomere length from whole genome sequencing dataen
dc.typeJournal articleen
dc.contributor.institutionUniversity of St Andrews. Statisticsen
dc.contributor.institutionUniversity of St Andrews. School of Medicineen
dc.contributor.institutionUniversity of St Andrews. Population and Behavioural Science Divisionen
dc.contributor.institutionUniversity of St Andrews. Cellular Medicine Divisionen
dc.identifier.doihttps://doi.org/10.1038/s41598-017-14403-y
dc.description.statusPeer revieweden


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