一个可概括的Hi-C基础模型,用于跨物种的染色体架构,单细胞和多omics分析
Xiao Wang1,2, Yuanyuan Zhang3, Suhita Ray4
1Department of Genome Sciences, University of Washington, Seattle, WA, USA.
bioRxiv : the preprint server for biology
|January 7, 2025
概括
HiCFoundation是一个新的AI模型,它将3D基因组结构与表观基因组数据集成在一起. 它增强了Hi-C分析,揭示了细胞分化过程中的全基因组循环损失,并使单细胞分辨率成为可能.
科学领域:
- 基因组学就是基因组学.
- 计算生物学 计算生物学
- 表观遗传学 在表观遗传学中,表观遗传学是指表观遗传学.
背景情况:
- 核DNA形成一个复杂的3D结构,影响细胞功能.
- 高通量染色体构造捕获 (Hi-C) 测量了基因组架构,而像ATAC-seq这样的表观基因组测试描述了调节.
- 整合Hi-C和表观基因组数据是具有挑战性的,因为数据属性和分析不一致.
研究的目的:
- 开发HiCFoundation,这是一个新的基础模型,用于对3D基因组结构和监管功能的综合分析.
- 改进Hi-C数据的分析,包括分辨率增强和循环检测.
- 将染色质构成与基因组功能联系起来,并使单细胞分辨率分析成为可能.
主要方法:
- 培训 HiCFoundation 在数百个测试中的数百万个 HiC 接触矩阵子矩阵上.
- 应用该模型来分析316个物种的基因组结构.
- 增强低覆盖范围的Hi-C数据以研究细胞分化.
- 从Hi-C数据预测表观基因组活动,并分析单细胞Hi-C数据.
主要成果:
- HiCFoundation在3D基因组分析任务中实现了最先进的性能.
- 该模型揭示了在造血干细胞和祖细胞分化过程中全基因组循环损失.
- 它证明了跨多种物种的概括性,并使单细胞基因组结构分析成为可能.
- HiCFoundation成功地将染色质构成与表观基因组功能联系起来.
结论:
- HiCFoundation为3D基因组架构分析提供了一个统一,高效和可解释的平台.
- 它促进了对基因组3D结构及其跨物种的调节机制的系统研究.
- 该模型推进了单细胞和多细胞分析,为基因组功能提供了新的见解.
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