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基因表达和变异效应的初级细胞类型特定序列模型的地图

Ksenia Sokolova¹, Chandra L Theesfeld², Aaron K Wong³

¹Department of Computer Science, Princeton University, Princeton, NJ 08544, USA; Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA.

Cell reports methods

|September 13, 2023

概括

这项研究介绍了ExPectoSC,这是一种使用深度学习来从DNA序列中预测特定人体细胞类型的基因表达的工具. 它通过识别相关细胞类型,帮助将非编码DNA的遗传变异与疾病风险联系起来.

科学领域:

基因组学就是基因组学.
计算生物学计算生物学
人体生理学人体生理学

背景情况:

人类生物学依赖于来自共同基因组的专门细胞,大多数基因之外的变异.
非编码基因组中的遗传变异与大多数疾病风险有关.
将非编码变异与特定细胞中的基因表达变化联系起来是具有挑战性的.

研究的目的:

开发一种直接从DNA序列中预测细胞类型特定基因表达的方法.
为各种人类细胞类型创建预测模型的地图.
优先考虑与复杂的人类疾病和临床变异相关的细胞类型.

主要方法:

开发了ExPectoSC,这是一个模块化深度学习模型套件.
在7个器官系统中对105种主要人体细胞类型进行了训练.
应用模型来预测基因表达和优先考虑与疾病相关的变异.

主要成果:

通过系统性评估证明了ExPectoSC模型的准确性.
创建了基于序列的基因表达和变异效应的公开可用的地图集.
具有不确定的意义的优先级ClinVar变体,顶级预测经过实验验证.

结论:

ExPectoSC提供了一种强大的工具,用于以细胞类型特定的方式理解非编码变异对基因表达的影响.
"ExPectoSC"地图库促进了对人类疾病遗传基础的研究.
这种方法可以扩展到新的细胞类型和变体,使未来的发现成为可能.

关键词:

CP:系统生物学系统生物学深度学习是一种深度学习.功能性基因组学功能性基因组学基因表达的预测和预测.人类疾病人类疾病变种效应是变种效应的影响.

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