基于外显子组测序的拷贝数变异和杂合性丢失检测：ExomeCNV。
Exome sequencing-based copy-number variation and loss of heterozygosity detection: ExomeCNV.
机构信息
Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA.
出版信息
Bioinformatics. 2011 Oct 1;27(19):2648-54. doi: 10.1093/bioinformatics/btr462. Epub 2011 Aug 9.
Abstract
MOTIVATION
The ability to detect copy-number variation (CNV) and loss of heterozygosity (LOH) from exome sequencing data extends the utility of this powerful approach that has mainly been used for point or small insertion/deletion detection.
RESULTS
We present ExomeCNV, a statistical method to detect CNV and LOH using depth-of-coverage and B-allele frequencies, from mapped short sequence reads, and we assess both the method's power and the effects of confounding variables. We apply our method to a cancer exome resequencing dataset. As expected, accuracy and resolution are dependent on depth-of-coverage and capture probe design.
AVAILABILITY
CRAN package 'ExomeCNV'.
CONTACT
fsathira@fas.harvard.edu; snelson@ucla.edu
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
摘要
动机
从外显子测序数据中检测拷贝数变异 (CNV) 和杂合性丢失 (LOH) 的能力扩展了这种强大方法的实用性，该方法主要用于点突变或小插入/缺失检测。
结果
我们提出了 ExomeCNV，这是一种使用覆盖深度和 B 等位基因频率从映射的短序列读取中检测 CNV 和 LOH 的统计方法，我们评估了该方法的功效和混杂变量的影响。我们将我们的方法应用于癌症外显子重测序数据集。正如预期的那样，准确性和分辨率取决于覆盖深度和捕获探针设计。
可用性
CRAN 软件包“ExomeCNV”。
联系人
fsathira@fas.harvard.edu; snelson@ucla.edu
补充信息
补充数据可在 Bioinformatics 在线获得。
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