Telomerecat: A ploidy-agnostic method for estimating telomere length from whole genome sequencing data.
Telomerecat: A ploidy-agnostic method for estimating telomere length from whole genome sequencing data.
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端粒：一种倍性 - 敏锐的方法，用于从整个基因组测序数据中估算端粒长度。
DOI：
10.1038/s41598-017-14403-y
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发表时间：
2018-01-22
影响因子：
4.6
通讯作者：
Lynch AG
中科院分区：
文献类型：
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作者：
Farmery JHR;Smith ML;NIHR BioResource - Rare Diseases;Lynch AG
端粒长度是疾病的一个危险因素，端粒长度的动态变化对我们理解细胞复制和活力至关重要。全基因组测序的扩散代表了一个前所未有的机会，以以前难以想象的规模收集端粒生物学的新见解。为此，已经提出了许多从全基因组测序数据估计端粒长度的方法。在这里，我们提出端粒猫，一种新的方法来估计端粒长度。以前的方法依赖于已知细胞中存在的端粒数量，这在分析非整倍体癌症数据和非人类样本时可能会出现问题。端粒猫被设计成不知道端粒的数量，使其适合于癌症研究中估计端粒长度的目的。端粒酶也解释了间质端粒的读取，并提出了一种处理测序错误的新方法。我们表明，与领先的实验和计算方法相比，端粒ecat在端粒长度估计方面表现良好。此外，我们表明，它可以在纵向数据、重复测量和跨物种比较中检测到预期的模式。我们还将该方法应用于癌细胞数据，揭示了与潜在端粒酶基因型的有趣关系。
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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31种癌症类型的端粒长度和躯体改变的系统分析。
影响因子：
30.8
作者：
Barthel FP;Wei W;Tang M;Martinez-Ledesma E;Hu X;Amin SB;Akdemir KC;Seth S;Song X;Wang Q;Lichtenberg T;Hu J;Zhang J;Zheng S;Verhaak RG
通讯作者：
Verhaak RG
DOI：
10.1016/j.mrfmmm.2011.04.003
发表时间：
2012-02-01
影响因子：
2.3
作者：
Aubert, Geraldine;Hills, Mark;Lansdorp, Peter M.
通讯作者：
Lansdorp, Peter M.
影响因子：
64.8
作者：
KIPLING, D;COOKE, HJ
通讯作者：
COOKE, HJ
影响因子：
30.8
作者：
Robles-Espinoza CD;Harland M;Ramsay AJ;Aoude LG;Quesada V;Ding Z;Pooley KA;Pritchard AL;Tiffen JC;Petljak M;Palmer JM;Symmons J;Johansson P;Stark MS;Gartside MG;Snowden H;Montgomery GW;Martin NG;Liu JZ;Choi J;Makowski M;Brown KM;Dunning AM;Keane TM;López-Otín C;Gruis NA;Hayward NK;Bishop DT;Newton-Bishop JA;Adams DJ
通讯作者：
Adams DJ
DOI：
10.1038/nrm2848
发表时间：
2010-03
期刊：
Nature reviews. Molecular cell biology
影响因子：
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作者：
通讯作者：
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