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

James H. R. Farmery*, Mike L. Smith, Andy G. Lynch, Aarnoud Huissoon, Abigail Furnell, Adam Mead, Adam P. Levine, Adnan Manzur, Adrian Thrasher, Alan Greenhalgh, Alasdair Parker, Alba Sanchis-Juan, Alex Richter, Alice Gardham, Allan Lawrie, Aman Sohal, Amanda Creaser-Myers, Amy Frary, Andreas Greinacher, Andreas ThemistocleousAndrew J. Peacock, Andrew Marshall, Andrew Mumford, Andrew Rice, Andrew Webster, Angie Brady, Ania Koziell, Ania Manson, Anita Chandra, Anke Hensiek, Anna Huis in't Veld, Anna Maw, Anne M. Kelly, Anthony Moore, Anton Vonk Noordegraaf, Antony Attwood, Archana Herwadkar, Ardi Ghofrani, Arjan C. Houweling, Barbara Girerd, Bruce Furie, Carmen M. Treacy, Carolyn M. Millar, Carrock Sewell, Catherine Roughley, Catherine Titterton, Catherine Williamson, Charaka Hadinnapola, Charu Deshpande, Johan W. M. Heemskerk, NIHR-BioResource Rare Dis

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Web of Science)


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.
Original languageEnglish
Article number1300
Number of pages4
JournalScientific Reports
Publication statusPublished - 3 Sept 2018

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