Estimating telomere length from whole genome sequence data.
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ABSTRACT:
SUBMITTER: Ding Z
PROVIDER: S-EPMC4027178 | biostudies-literature | 2014 May
REPOSITORIES: biostudies-literature
Publications
Estimating telomere length from whole genome sequence data.
Ding Zhihao Z Mangino Massimo M Aviv Abraham A Spector Tim T Durbin Richard R
Nucleic acids research 20140307 9
Telomeres play a key role in replicative ageing and undergo age-dependent attrition in vivo. Here, we report a novel method, TelSeq, to measure average telomere length from whole genome or exome shotgun sequence data. In 260 leukocyte samples, we show that TelSeq results correlate with Southern blot measurements of the mean length of terminal restriction fragments (mTRFs) and display age-dependent attrition comparably well as mTRFs. ...[more]
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Project description: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.
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