Combinatorial detection algorithm for copy number variations using high-throughput sequencing reads

被引:0
|
作者
Yang H. [1 ]
Zhu D. [1 ]
机构
[1] School of Computer Science and Technology, Shandong University, Qingdao
来源
International Journal of Pattern Recognition and Artificial Intelligence | 2019年 / 33卷 / 14期
基金
中国国家自然科学基金;
关键词
Combinatorial detection algorithm; Copy number variation; Hidden Markov model; High-throughput sequencing; Split read;
D O I
10.1142/S0218001419500228
中图分类号
学科分类号
摘要
Copy number variation (CNV) is a prevalent kind of genetic structural variation which leads to an abnormal number of copies of large genomic regions, such as gain or loss of DNA segments larger than 1kb. CNV exists not only in human genome but also in plant genome. Current researches have testified that CNV is associated with many complex diseases. In this paper, guanine-cytosine (GC) bias, mappability and their effect on read depth signals in sequencing data are discussed first. Subsequently, a new correction method for GC bias and an improved combinatorial detection algorithm for CNV using high-throughput sequencing reads based on hidden Markov model (CNV-HMM) are proposed. The corrected read depth signals have lower correlation with GC content, mappability of reads and the width of analysis window. Then we create a hidden Markov model which maps the reads onto the reference genome and records the unmapped reads. The unmapped reads are counted and normalized. The CNV-HMM detects the abnormal signal of read count and gains the candidate CNVs using the expectation maximization (EM) algorithm. Finally, we filter the candidate CNVs using split reads to promote the performance of our algorithm. The experiment result indicates that the CNV-HMM algorithm has higher accuracy and sensitivity for CNVs detection than most current detection algorithms. © 2019 World Scientific Publishing Company.
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