Telomerecat: A ploidy-agnostic method for estimating telomere length from whole genome sequencing data

Abstract Telomere 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...

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Autores principales:	James H. R. Farmery, Mike L. Smith, NIHR BioResource - Rare Diseases, Andy G. Lynch
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/925eca9b0f364764afe1780662261112
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spelling	oai:doaj.org-article:925eca9b0f364764afe17806622611122021-12-02T15:08:15ZTelomerecat: A ploidy-agnostic method for estimating telomere length from whole genome sequencing data10.1038/s41598-017-14403-y2045-2322https://doaj.org/article/925eca9b0f364764afe17806622611122018-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-14403-yhttps://doaj.org/toc/2045-2322Abstract Telomere 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.James H. R. FarmeryMike L. SmithNIHR BioResource - Rare DiseasesAndy G. LynchNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 8, Iss 1, Pp 1-17 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q James H. R. Farmery Mike L. Smith NIHR BioResource - Rare Diseases Andy G. Lynch Telomerecat: A ploidy-agnostic method for estimating telomere length from whole genome sequencing data
description	Abstract Telomere 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.
format	article
author	James H. R. Farmery Mike L. Smith NIHR BioResource - Rare Diseases Andy G. Lynch
author_facet	James H. R. Farmery Mike L. Smith NIHR BioResource - Rare Diseases Andy G. Lynch
author_sort	James H. R. Farmery
title	Telomerecat: A ploidy-agnostic method for estimating telomere length from whole genome sequencing data
title_short	Telomerecat: A ploidy-agnostic method for estimating telomere length from whole genome sequencing data
title_full	Telomerecat: A ploidy-agnostic method for estimating telomere length from whole genome sequencing data
title_fullStr	Telomerecat: A ploidy-agnostic method for estimating telomere length from whole genome sequencing data
title_full_unstemmed	Telomerecat: A ploidy-agnostic method for estimating telomere length from whole genome sequencing data
title_sort	telomerecat: a ploidy-agnostic method for estimating telomere length from whole genome sequencing data
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/925eca9b0f364764afe1780662261112
work_keys_str_mv	AT jameshrfarmery telomerecataploidyagnosticmethodforestimatingtelomerelengthfromwholegenomesequencingdata AT mikelsmith telomerecataploidyagnosticmethodforestimatingtelomerelengthfromwholegenomesequencingdata AT nihrbioresourcerarediseases telomerecataploidyagnosticmethodforestimatingtelomerelengthfromwholegenomesequencingdata AT andyglynch telomerecataploidyagnosticmethodforestimatingtelomerelengthfromwholegenomesequencingdata
_version_	1718388201837559808

Telomerecat: A ploidy-agnostic method for estimating telomere length from whole genome sequencing data

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