为了对体质结构变异的功能解释:大质量和单细胞方法
Dohun Yi1, Jin-Wu Nam1,2,3,4, Hyobin Jeong1,3,4
1Department of Life Science, College of Natural Sciences, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea.
Briefings in bioinformatics
|August 17, 2023
概括
本综述涵盖了使用全基因组测序 (WGS) 检测结构变异 (SV) 的进展. 它比较了批量和单细胞方法来识别这些基因组变化在健康和疾病.
科学领域:
- 基因组学就是基因组学.
- 癌症生物学 癌症生物学
- 生物信息学是一种生物信息学.
背景情况:
- 结构变异 (SV) 是细胞衰老过程中积累的基因组重组,在癌症发育中至关重要.
- 实体性SVs虽然很重要,但难以检测,与单核酸变体相比,限制了它们的研究.
研究的目的:
- 审查最近基于全基因组测序 (WGS) 的方法来检测体质SVs.
- 为了比较大量与单细胞方法的优点和局限性,用于SV分析.
- 讨论将SV与功能读数联系起来的计算策略.
主要方法:
- 从批量WGS数据中对体质SV调用进行最先进的计算算法进行总结.
- 分析用于体质 SV 分析的尖端单细胞技术.
- 评估绩效指标,包括对复制中立的SV和成本的敏感性.
主要成果:
- 实体性SV检测算法的比较,有和没有匹配的正常对照.
- 突出批量和单细胞SV分析方法的独特特征和权衡.
- 展示了将SV数据与转录组和表观组分析集成的计算方法.
结论:
- 基于WGS的方法的进步增强了在组织和单细胞水平上对体质SV的检测.
- 了解体质性SVs对于了解健康和疾病,特别是癌症至关重要.
- 将SV数据与功能基因组学集成为未来研究提供了有前途的途径.
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