从长时间读取的测序数据中检测基因组结构变异的算法的调查
Mian Umair Ahsan1, Qian Liu1, Jonathan Elliot Perdomo1,2
1Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Nature methods
|June 29, 2023
概括
长读测序使新的结构变体 (SV) 发现成为可能. 本综述总结了从长时间阅读中检测,基因型和可视化SV的50多种方法,突出显示了未来的方向.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
背景情况:
- 长读测序技术正在迅速发展,为基因组分析提供了新的能力.
- 这些技术为结构变异 (SV) 检测带来了独特的计算挑战和机会.
研究的目的:
- 提供目前用于SV发现和分析的基于长读的方法的全面摘要.
- 讨论先进的基因组组合和泛基因组努力对SV检测准确性的影响.
主要方法:
- 使用长读序列数据对50多种用于SV检测,基因型和可视化的计算方法进行系统审查和总结.
- 分析不同长读SV调用者的优点和局限性.
主要成果:
- 识别和分类广泛的基于长时间读取的SV分析工具.
- 讨论如何新的基因组组合和泛基因组计划可以增强SV检测.
结论:
- 长读测序显著扩大了可检测的结构变异的范围.
- 基因组组装和泛基因组资源的未来进展对于提高SV调用器准确性和实用性至关重要.
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