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不同变异类型的解卷和分化推断,将批量DNA-seq与单细胞RNA-seq集成在一起

Nishat Anjum Bristy¹, Russell Schwartz^1,2

¹Ray and Stephanie Lane Computational Biology Department, Carnegie Mellon University, Pittsburgh, 15213, PA, USA.

bioRxiv : the preprint server for biology

|February 20, 2025

概括

这项研究引入了TUSV-int,这是一种新的计算方法,它集成了大量DNA测序和单细胞RNA测序数据,以重建精确的瘤遗传学. TUSV-int通过分析单核酸变异,副本数量变化和结构变异来增强克隆子结构和突变史的分辨率.

科学领域:

癌症基因组学癌症基因组学
计算生物学计算生物学
生物信息学是一种生物信息学.

背景情况:

重建克隆血统树对于理解癌症基因组学至关重要.
传统的批量DNA测序 (DNA-seq) 缺乏分辨率,而单细胞DNA测序 (scDNA-seq) 是昂贵且有限的.
单细胞RNA测序 (scRNA-seq) 是广泛可用的,但对检测结构变异的基因组覆盖范围有限.

研究的目的:

开发一种结合大量DNA-seq和scRNA-seq数据的计算方法,以改善瘤遗传学.
为了使单核酸变异 (SNVs),复制数变异 (CNAs) 和结构变异 (SVs) 的同时分析.
通过结合不同基因组技术的优势,克服现有方法的局限性.

主要方法:

开发了TUSV-int,一种使用整数线性编程 (ILP) 的方法.
集成的大量DNA-seq和scRNA-seq数据用于解卷和遗传学推断.
应用于一个乳腺癌数据集与现有的DNA-seq和scRNA-seq数据.

主要成果:

与使用有限数据或变异类型的方法相比,TUSV-int表现出更好的解卷性能.
该方法有效地解决了克隆结构和突变历史.
成功应用于已发表的乳腺癌数据集,展示了增强的分辨率.

结论:

TUSV-int通过整合各种基因组数据,为癌症遗传学提供了一种强大的方法.
该方法增强了克隆子结构和突变事件的分辨率.
为癌症基因组学研究提供了宝贵的工具,特别是用于分析复杂的结构变异.

关键词:

癌症癌症癌症癌症癌症整数线性编程的整数线性编程测序的测序是指测序的测序.一个单细胞的单细胞.身体进化的体质演变.瘤的原始发育过程

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