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通过配对末端测序进行染色体片段重复的基因组检测与描绘

Genomic Detection and Delineation of Chromoanasynthesis by Mate-Pair Sequencing.

作者信息

Zheng Yuting, Zhang Yanyan, Cheung Sau Wai, Choy Kwong Wai, Dong Zirui, Gu Shen

机构信息

Department of Obstetrics and Gynaecology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, SAR, China.

Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China.

出版信息

Methods Mol Biol. 2025;2968:111-129. doi: 10.1007/978-1-0716-4750-9_6.

Abstract

Chromoanagenesis encompasses catastrophic genomic rearrangements, with chromoanasynthesis referring to unbalanced germline events involving one or multiple chromosomes, distinct from the mostly balanced rearrangements seen in cancer-associated chromothripsis and chromoplexy. Initially identified via chromosomal microarray analysis (CMA) and custom high-density arrays, chromoanasynthesis detection and delineation was improved by next-generation sequencing (NGS). However, the short read-lengths and read-depth variations of NGS limit its fine-mapping capabilities. While third-generation sequencing (TGS) offers higher accuracy than that of NGS by providing long read sequences, it remains costly. Accurate characterization of rearrangement patterns is crucial for understanding disease pathogenicity, as small copy-number variations (CNVs) near breakpoints can significantly alter diagnostic interpretations. To overcome the limitations of CMA and regular NGS, we developed a mate-pair library construction method using large DNA inserts (~5 kb) and low-pass genome sequencing (GS) to identify CNVs, structural variants (SVs), and absence of homozygosity (AOHs), as well as to assemble the genomic organization. This methodology, illustrated through a chromoanasynthesis case study, enhances our ability to efficiently and cost-effectively characterize complex genomic rearrangements, addressing both technical challenges and clinical diagnostic needs.

摘要

染色体混乱包括灾难性的基因组重排,而染色体合成是指涉及一条或多条染色体的不平衡种系事件,这与癌症相关的染色体碎裂和染色体并合中常见的大多为平衡的重排不同。染色体合成最初是通过染色体微阵列分析(CMA)和定制高密度阵列鉴定出来的,下一代测序(NGS)改进了染色体合成的检测和描绘。然而,NGS的短读长和读深变化限制了其精细定位能力。虽然第三代测序(TGS)通过提供长读序列比NGS具有更高的准确性,但成本仍然很高。重排模式的准确表征对于理解疾病致病性至关重要,因为断点附近的小拷贝数变异(CNV)会显著改变诊断解释。为了克服CMA和常规NGS的局限性,我们开发了一种配对文库构建方法,使用大DNA插入片段(约5 kb)和低通量基因组测序(GS)来识别CNV、结构变异(SV)和纯合性缺失(AOH),以及组装基因组结构。通过一个染色体合成病例研究说明的这种方法,增强了我们有效且经济地表征复杂基因组重排的能力,既解决了技术挑战,又满足了临床诊断需求。

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