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首页> 外文期刊>Journal of Bioinformatics and Computational Biology >A novel approach to dna copy number data segmentation
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A novel approach to dna copy number data segmentation

机译：dna拷贝数数据分割的新方法

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摘要

DNA copy number (DCN) is the number of copies of DNA at a region of a genome. The alterations of DCN are highly associated with the development of different tumors. Recently, microarray technologies are being employed to detect DCN changes at many loci at the same time in tumor samples. The resulting DCN data are often very noisy, and the tumor sample is often contaminated by normal cells. The goal of computational analysis of array-based DCN data is to infer the underlying DCNs from raw DCN data. Previous methods for this task do not model the tumorormal cell mixture ratio explicitly and they cannot output segments with DCN annotations. We developed a novel model-based method using the minimum description length (MDL) principle for DCN data segmentation. Our new method can output underlying DCN for each chromosomal segment, and at the same time, infer the underlying tumor proportion in the test samples. Empirical results show that our method achieves better accuracies on average as compared to three previous methods, namely Circular Binary Segmentation, Hidden Markov Model and Ultrasome.
机译：DNA拷贝数（DCN）是基因组区域中DNA的拷贝数。 DCN的变化与不同肿瘤的发生高度相关。最近，微阵列技术已被用于同时检测肿瘤样本中多个基因座的DCN变化。产生的DCN数据通常非常嘈杂，并且肿瘤样本经常被正常细胞污染。对基于阵列的DCN数据进行计算分析的目的是从原始DCN数据中推断基础DCN。用于此任务的先前方法无法显式建模肿瘤/正常细胞混合比，并且无法输出带有DCN注释的片段。我们使用最小描述长度（MDL）原理开发了一种基于模型的新颖方法，用于DCN数据分段。我们的新方法可以为每个染色体片段输出潜在的DCN，并同时推断测试样品中潜在的肿瘤比例。实验结果表明，与之前的三种方法（圆二值分割，隐马尔可夫模型和超微粒体）相比，我们的方法平均具有更好的精度。

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