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Updated: Jan 19, 2026

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用基于图形的注意力模型预测特定于背景的异形变异的变异解决预测

Aviya Litman¹, Zhicheng Pan², Ksenia Sokolova³

¹Quantitative and Computational Biology Program, Princeton University, Princeton, NJ 08540, USA; Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08540, USA.

|January 17, 2026

概括

新的人工智能工具Otari分析全长基因转录,揭示遗传变化如何影响RNA拼接. 这有助于理解复杂的疾病,如自闭症,通过精确指明异型失调.

科学领域:

基因组学和分子生物学
计算生物学和生物信息学

背景情况:

细胞基因产生多个转录异型,对转录和蛋白质组多样性和功能调节至关重要.
遗传变异可以改变RNA处理信号,影响异构体结构和丰度,但在全长异构体分辨率下建模这些效应是复杂的.

研究的目的:

介绍Otari,一个基于注意力的图形神经网络框架,用于预测组织特异的差异化异形态丰度.
通过整合序列衍生信号来实现基因变异效应的异型解析解释.

主要方法:

奥塔里接受了人类基因组序列和长时间读取的转录组在各种人类组织和大脑区域的训练.
该框架整合了序列衍生的表观遗传和后转录信号,以预测异构体的丰富性.
奥塔里被应用于大型变体数据集,包括自闭症队列.

主要成果:

奥塔里成功地预测了组织特异差异化异型的丰富性.
该模型揭示了在基因水平上无法检测到的异形失调模式.
确定了与自闭症病理生理学相关的异形丰度和微埃克逊使用的变异驱动变化.

结论:

奥塔里提供了一个强大的资源,用于在多种组织中进行大规模的异构体水平分析.
该框架增强了对转录异型的遗传变异影响的解释.
在异形分辨率上,Otari有助于更深入地了解疾病机制,如自闭症.

关键词:

替代性拼接是一种替代性的拼接.关注注意力注意力注意力注意力自闭症自闭症是什么图形神经网络的神经网络它们是异构体.长时间读取RNA-seqq.转录后监管的规定.翻译学翻译学翻译学翻译学变体效应预测变体效应预测

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