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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.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.otherPURE: 251746309
dc.identifier.otherPURE UUID: 96a95404-9ab6-4dcd-91d8-ec5295451131
dc.identifier.otherScopus: 85040985054
dc.identifier.otherORCID: /0000-0002-7876-7338/work/41026514
dc.identifier.otherWOS: 000422904600013
dc.identifier.urihttp://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.language.isoeng
dc.relation.ispartofScientific Reportsen
dc.rights© 2018, he Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/en
dc.subjectQH301 Biologyen
dc.subjectRC0254 Neoplasms. Tumors. Oncology (including Cancer)en
dc.subjectT Technologyen
dc.subjectDASen
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.description.versionPublisher PDFen
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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