A robust model for read count data in exome sequencing experiments and implications for copy number variant calling.
A robust model for read count data in exome sequencing experiments and implications for copy number variant calling.
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DOI：
10.1093/bioinformatics/bts526
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发表时间：
2012-11-01
期刊：
影响因子：
--
通讯作者：
Nejentsev S
中科院分区：
文献类型：
--
作者：
Plagnol V;Curtis J;Epstein M;Mok KY;Stebbings E;Grigoriadou S;Wood NW;Hambleton S;Burns SO;Thrasher AJ;Kumararatne D;Doffinger R;Nejentsev S
动机：外显子组测序已被证明是发现孟德尔疾病遗传基础的有效工具。众所周知，拷贝数变异(CNV)与这些疾病的病因学有关。然而，从外显子组序列数据中调用CNV是具有挑战性的。典型的读取深度策略包括使用另一个样本(或样本的组合)作为参考，以控制捕获和测序步骤中的可变性。然而，样本之间的技术差异使分析变得复杂，并可能产生虚假的CNV呼叫。结果：在这里，我们介绍了ExomeDepth，一种新的CNV调用算法，旨在控制这种技术变异性。ExomeDepth对读取计数数据使用健壮的模型，并使用该模型构建优化的参考集，以便最大限度地提高检测CNV的能力。因此，ExomeDepth对更广泛的外显子组数据集有效，甚至对于小的(例如，一到两个外显子)和杂合缺失也是如此。我们使用这种新的方法分析了24名原发免疫缺陷患者的外显子组数据。根据数据质量和准确的目标区域，我们发现每个样本有170到250个外显子CNV调用。我们的分析发现了基因GATA2和DOCK8中的两个新的致病缺失。可用性：本分析中使用的代码已被实施到名为ExomeDepth的R包中，并可在全面R档案网络(CRAN)上获得。补充信息：补充数据可在BioInformation Online上获得。
Motivation: Exome sequencing has proven to be an effective tool to discover the genetic basis of Mendelian disorders. It is well established that copy number variants (CNVs) contribute to the etiology of these disorders. However, calling CNVs from exome sequence data is challenging. A typical read depth strategy consists of using another sample (or a combination of samples) as a reference to control for the variability at the capture and sequencing steps. However, technical variability between samples complicates the analysis and can create spurious CNV calls. Results: Here, we introduce ExomeDepth, a new CNV calling algorithm designed to control for this technical variability. ExomeDepth uses a robust model for the read count data and uses this model to build an optimized reference set in order to maximize the power to detect CNVs. As a result, ExomeDepth is effective across a wider range of exome datasets than the previously existing tools, even for small (e.g. one to two exons) and heterozygous deletions. We used this new approach to analyse exome data from 24 patients with primary immunodeficiencies. Depending on data quality and the exact target region, we find between 170 and 250 exonic CNV calls per sample. Our analysis identified two novel causative deletions in the genes GATA2 and DOCK8. Availability: The code used in this analysis has been implemented into an R package called ExomeDepth and is available at the Comprehensive R Archive Network (CRAN). Contact: v.plagnol@ucl.ac.uk Supplementary Information: Supplementary data are available at Bioinformatics online.
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影响因子：
5.8
作者：
Sathirapongsasuti, Jarupon Fah;Lee, Hane;Nelson, Stanley F.
通讯作者：
Nelson, Stanley F.
DOI：
10.1093/bioinformatics/btq293
发表时间：
2010-08-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Zeitouni B;Boeva V;Janoueix-Lerosey I;Loeillet S;Legoix-né P;Nicolas A;Delattre O;Barillot E
通讯作者：
Barillot E
影响因子：
3
作者：
Xie C;Tammi MT
通讯作者：
Tammi MT
影响因子：
64.8
作者：
通讯作者：
--
DOI：
10.1056/nejmoa0905506
发表时间：
2009-11-19
期刊：
The New England journal of medicine
影响因子：
--
作者：
Zhang Q;Davis JC;Lamborn IT;Freeman AF;Jing H;Favreau AJ;Matthews HF;Davis J;Turner ML;Uzel G;Holland SM;Su HC
通讯作者：
Su HC