Preprocessing Sequence Coverage Data for More Precise Detection of Copy Number Variations.
Preprocessing Sequence Coverage Data for More Precise Detection of Copy Number Variations.
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DOI：
10.1109/tcbb.2018.2869738
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发表时间：
2020-05
期刊：
影响因子：
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通讯作者：
Nabavi S
中科院分区：
文献类型：
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作者：
Zare F;Ansari S;Najarian K;Nabavi S
拷贝数变异（CNV）是一种基因组/遗传变异，在表型多样性、进化和疾病易感性中起重要作用。下一代测序（NGS）技术为以更高分辨率更准确地检测CNV创造了机会。然而，由于高水平的噪声和偏差、数据异质性和NGS数据的“大数据”性质，CNV的有效和精确检测仍然具有挑战性。序列覆盖率（读数）数据主要用于检测CNV，特别是全外显子组测序数据。读数数据被几种类型的偏差和噪声污染，阻碍了CNV的准确检测。在这项工作中，我们引入了一种新的预处理管道，用于减少噪声和偏差，以提高异质NGS数据（如癌症全外显子组测序数据）中CNV的检测准确性。我们采用了几种标准化方法来减少读数的偏差，这些偏差是由于读数的GC含量、读数比对问题和样品杂质造成的。我们还开发了一种基于Taut String的新型高效平滑方法，以降低噪声并提高CNV检测能力。使用模拟和真实的数据，我们表明，采用所提出的预处理流水线显着提高CNV检测的准确性。
Copy number variation (CNV) is a type of genomic/genetic variation that plays an important role in phenotypic diversity, evolution, and disease susceptibility. Next generation sequencing (NGS) technologies have created an opportunity for more accurate detection of CNVs with higher resolution. However, efficient and precise detection of CNVs remains challenging due to high levels of noise and biases, data heterogeneity and the “big data” nature of NGS data. Sequence coverage (readcount) data are mostly used for detecting CNVs, specially for whole exome sequencing data. Readcount data are contaminated with several types of biases and noise that hinder accurate detection of CNVs. In this work, we introduce a novel preprocessing pipeline for reducing noise and biases to improve the detection accuracy of CNVs in heterogeneous NGS data, such as cancer whole exome sequencing data. We have employed several normalization methods to reduce readcount’s biases that are due to GC content of reads, read alignment problems, and sample impurity. We have also developed a novel efficient and effective smoothing approach based on Taut String to reduce noise and increase CNV detection power. Using simulated and real data we showed that employing the proposed preprocessing pipeline significantly improves the accuracy of CNV detection.