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A Robust Approach for Blind Detection of Balanced Chromosomal Rearrangements with Whole‐Genome Low‐Coverage Sequencing
Author(s) -
Dong Zirui,
Jiang Lupin,
Yang Chuanchun,
Hu Hua,
Wang Xiuhua,
Chen Haixiao,
Choy Kwong Wai,
Hu Huamei,
Dong Yanling,
Hu Bin,
Xu Juchun,
Long Yang,
Cao Sujie,
Chen Hui,
Wang WenJing,
Jiang Hui,
Xu Fengping,
Yao Hong,
Xu Xun,
Liang Zhiqing
Publication year - 2014
Publication title -
human mutation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.981
H-Index - 162
eISSN - 1098-1004
pISSN - 1059-7794
DOI - 10.1002/humu.22541
Subject(s) - biology , false positive paradox , computational biology , breakpoint , genome , false discovery rate , structural variation , whole genome sequencing , karyotype , chromosome , dna sequencing , genetics , computer science , artificial intelligence , gene
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 BCA s 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, BCA s 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 BCA s. 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.