生物
假阳性悖论
计算生物学
断点
基因组
错误发现率
结构变异
全基因组测序
核型
染色体
DNA测序
遗传学
计算机科学
人工智能
基因
作者
Zirui Dong,Lupin Jiang,Chuanchun Yang,Hua Hu,Xiuhua Wang,Haixiao Chen,Kwong Wai Choy,Huamei Hu,Yanling Dong,Bin Hu,Juchun Xu,Yang Long,Sujie Cao,Hui Chen,Wenjing Wang,Hui Jiang,Fengping Xu,Hong Yao,Xun Xu,Zhiqing Liang
摘要
Balanced chromosomal rearrangement (or balanced chromosome abnormality, BCA) is a common chromosomal structural variation. Next-generation sequencing has been reported to detect BCA-associated breakpoints with the aid of karyotyping. However, the complications associated with this approach and the requirement for cytogenetics information has limited its application. Here, we provide a whole-genome low-coverage sequencing approach to detect BCA events independent of knowing the affected regions and with low false positives. First, six samples containing BCAs were used to establish a detection protocol and assess the efficacy of different library construction approaches. By clustering anomalous read pairs and filtering out the false-positive results with a control cohort and the concomitant mapping information, we could directly detect BCA events for each sample. Through optimizing the read depth, BCAs in all samples could be blindly detected with only 120 million read pairs per sample for data from a small-insert library and 30 million per sample for data from nonsize-selected mate-pair library. This approach was further validated using another 13 samples that contained BCAs. Our approach advances the application of high-throughput whole-genome low-coverage analysis for robust BCA detection-especially for clinical samples-without the need for karyotyping.
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