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首页> 外文期刊>BMC Bioinformatics >A forward-backward fragment assembling algorithm for the identification of genomic amplification and deletion breakpoints using high-density single nucleotide polymorphism (SNP) array
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A forward-backward fragment assembling algorithm for the identification of genomic amplification and deletion breakpoints using high-density single nucleotide polymorphism (SNP) array

机译:使用高密度单核苷酸多态性(SNP)阵列鉴定基因组扩增和缺失断点的前向后片段组装算法

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摘要

Background DNA copy number aberration (CNA) is one of the key characteristics of cancer cells. Recent studies demonstrated the feasibility of utilizing high density single nucleotide polymorphism (SNP) genotyping arrays to detect CNA. Compared with the two-color array-based comparative genomic hybridization (array-CGH), the SNP arrays offer much higher probe density and lower signal-to-noise ratio at the single SNP level. To accurately identify small segments of CNA from SNP array data, segmentation methods that are sensitive to CNA while resistant to noise are required. Results We have developed a highly sensitive algorithm for the edge detection of copy number data which is especially suitable for the SNP array-based copy number data. The method consists of an over-sensitive edge-detection step and a test-based forward-backward edge selection step. Conclusion Using simulations constructed from real experimental data, the method shows high sensitivity and specificity in detecting small copy number changes in focused regions. The method is implemented in an R package FASeg, which includes data processing and visualization utilities, as well as libraries for processing Affymetrix SNP array data.
机译:背景DNA拷贝数异常(CNA)是癌细胞的关键特征之一。最近的研究证明了利用高密度单核苷酸多态性(SNP)基因分型阵列检测CNA的可行性。与基于双色阵列的比较基因组杂交(array-CGH)相比,SNP阵列在单个SNP水平上具有更高的探针密度和更低的信噪比。为了从SNP阵列数据中准确识别CNA的小片段,需要对CNA敏感而又抗噪声的分割方法。结果我们开发了一种高度灵敏的算法,用于拷贝数数据的边缘检测,特别适用于基于SNP阵列的拷贝数数据。该方法包括过度敏感的边缘检测步骤和基于测试的前后边缘选择步骤。结论通过使用真实实验数据构建的模拟,该方法在检测聚焦区域的小拷贝数变化方面显示出高灵敏度和特异性。该方法在R软件包FASeg中实现,其中包括数据处理和可视化实用程序以及用于处理Affymetrix SNP阵列数据的库。

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