Accurate and exact CNV identification from targeted high-throughput sequence data.
Accurate and exact CNV identification from targeted high-throughput sequence data.
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DOI：
10.1186/1471-2164-12-184
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发表时间：
2011-04-12
期刊：
影响因子：
4.4
通讯作者：
Walsh T
中科院分区：
文献类型：
--
作者：
Nord AS;Lee M;King MC;Walsh T
大规模平行测序的条形码DNA样品显著提高筛选效率的临床重要基因。短读比对非常适合于单核苷酸和indel检测。然而，缺乏靶向富集检测CNV的方法。我们提出了一种结合覆盖和地图信息的方法，用于识别目标序列数据中的缺失和重复。测序数据首先使用样本间标准化覆盖率数据的比较来扫描其收益和损失。通过测试跨越CNV断点的序列的签名来确认CNV调用。使用我们的方法，无论断点是否在测序目标区域内，都可以识别CNVs。对于目标序列中至少有一个断点的CNV，可以确定精确的CNV断点。在96名受试者的测试数据集中，使用多路复用技术对约1 Mb基因组序列进行测序，我们的方法检测到小至31 bp的突变，预测定量拷贝数，并且假阳性率低。该方法的应用允许识别目标序列数据的增益和损失，当与短读比对器结合时，提供全面的突变筛选。
Massively parallel sequencing of barcoded DNA samples significantly increases screening efficiency for clinically important genes. Short read aligners are well suited to single nucleotide and indel detection. However, methods for CNV detection from targeted enrichment are lacking. We present a method combining coverage with map information for the identification of deletions and duplications in targeted sequence data. Sequencing data is first scanned for gains and losses using a comparison of normalized coverage data between samples. CNV calls are confirmed by testing for a signature of sequences that span the CNV breakpoint. With our method, CNVs can be identified regardless of whether breakpoints are within regions targeted for sequencing. For CNVs where at least one breakpoint is within targeted sequence, exact CNV breakpoints can be identified. In a test data set of 96 subjects sequenced across ~1 Mb genomic sequence using multiplexing technology, our method detected mutations as small as 31 bp, predicted quantitative copy count, and had a low false-positive rate. Application of this method allows for identification of gains and losses in targeted sequence data, providing comprehensive mutation screening when combined with a short read aligner.
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DOI：
10.1073/pnas.1007983107
发表时间：
2010-07-13
影响因子：
11.1
作者：
Walsh, Tom;Lee, Ming K.;King, Mary-Claire
通讯作者：
King, Mary-Claire
影响因子：
64.8
作者：
通讯作者：
--
影响因子：
14.9
作者：
Diskin SJ;Li M;Hou C;Yang S;Glessner J;Hakonarson H;Bucan M;Maris JM;Wang K
通讯作者：
Wang K
影响因子：
7
作者：
Li, Heng;Ruan, Jue;Durbin, Richard
通讯作者：
Durbin, Richard
影响因子：
30.8
作者：
Campbell, Peter J.;Stephens, Philip J.;Pleasance, Erin D.;O'Meara, Sarah;Li, Heng;Santarius, Thomas;Stebbings, Lucy A.;Leroy, Catherine;Edkins, Sarah;Hardy, Claire;Teague, Jon W.;Menzies, Andrew;Goodhead, Ian;Turner, Daniel J.;Clee, Christopher M.;Quail, Michael A.;Cox, Antony;Brown, Clive;Durbin, Richard;Hurles, Matthew E.;Edwards, Paul A. W.;Bignell, Graham R.;Stratton, Michael R.;Futreal, P. Andrew
通讯作者：
Futreal, P. Andrew