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用TREAT和otter来描述跨长读测序平台的串联重复复杂性
Niccoló Tesi1,2,3, Alex Salazar4, Yaran Zhang4
1Section Genomics of Neurodegenerative Diseases and Aging, Department of Clinical Genetics, Vrije Universiteit Amsterdam, Amsterdam UMC, 1081HV Amsterdam, The Netherlands; n.tesi@amsterdamumc.nl a.n.salazar@amsterdamumc.nl h.holstege@amsterdamumc.nl.
Genome research
|October 15, 2024
概括
我们开发了TREAT和otter,这些工具用于使用长读序列的精确并列重复 (TR) 分析. 这些工具改善了TR基因型定型,并识别了与疾病相关的TR扩展,推进了基因组变异研究.
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.
- 人类遗传学 人类遗传学
背景情况:
- 串联重复 (TRs) 对人类基因组变异和疾病易感性至关重要.
- 准确的TR特征是具有挑战性的,特别是长时间读取的测序数据.
- 现有的生物信息学工具难以进行全面的TR分析.
研究的目的:
- 推出otter,一个快速的,跨平台的TRs的本地组装器.
- 介绍TREAT,一个端到端的工作流程,用于TR的表征,可视化和分析.
- 为了实现精确的TR基因型定型和病原性扩散的识别,跨多种测序平台.
主要方法:
- 开发了otter,一个针对性的局部组装器,与牛津纳米孔技术 (ONT) 和太平洋生物科学 (PacBio) 数据兼容.
- 将otter集成到TREAT中,这是TR分析的全面工作流.
- 使用长读序列数据对比/TREAT性能与现有工具.
主要成果:
- 在TR基因型和图案表征方面取得了最先进的准确性.
- 在临床上相关的位置成功识别了具有病原性TR扩张的个体.
- 复制了TRs与阿尔茨海默病之间的已知关联 (例如,在APOC1,SPI1,ABCA7附近).
- 评估了长期读取的TR基因型定型中的潜在偏差,并注意到罕见的覆盖率下降影响了准确性.
结论:
- 在不同的测序技术中,TREAT/otter提供了准确的,端到端的TR分析.
- 这些工具有助于TR在研究和临床环境中的比较和应用.
- 在长时间读取的数据中识别和解决TR错误基因类型的潜在来源.
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