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索弗里托:一种深度学习模型,用于预测高分辨率复制时间

Dante Bolzan^1,2, Ferhat Ay^1,2,3

¹Center for Autoimmunity and Inflammation, La Jolla Institute for Immunology, La Jolla, CA 92037, United States.

Bioinformatics (Oxford, England)

|July 15, 2025

概括

深度学习模型Soffritto使用标准的两部分RT数据准确预测高分辨率DNA复制时间 (RT). 这种方法克服了当前技术的局限性,使得可以在各类细胞中详细检测RT模式.

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:

复制时间 (RT) 描述了S阶段DNA复制的顺序,是细胞类型的特异性,影响基因表达和细胞过程.
目前的全基因组RT量化 (例如,Repli-Seq) 使用两部分测试,提供有限的分辨率.
高分辨率的Repli-Seq (16个分数) 提供了更多的细节,但成本昂贵,技术要求高,数据稀缺.

研究的目的:

从易于获得的低分辨率 (二分辨率) RT 数据中开发一种用于预测高分辨率 (16 分) RT 数据的计算方法.
为了使各种细胞类型的详细RT模式的准确和可访问的分析.

主要方法:

开发了Soffritto,这是一个使用长短期记忆 (LSTM) 模块和预测模块的深度学习模型.
输入特征包括两个分数RT数据,基因组ChIP-seq数据,GC含量和基因密度.
在5个人类和小鼠细胞系上训练并测试了Soffritto,进行细胞内和跨细胞系分析.

主要成果:

索弗里托准确地预测了16分数RT信号,捕获了高分辨率的实验RT模式.
该模型在细胞内线和跨细胞线预测方面都表现出高准确度.
预测的RT信号有助于检测复杂的,高分辨率的复制时间特征.

结论:

索弗里托有效地从标准的两部分数据中预测高分辨率复制时间,克服了当前的局限性.
该模型为各种细胞类型的详细RT分析提供了一个强大的,易于使用的工具.
这种方法可以促进对复制时间在细胞过程和疾病中的功能性作用的研究.

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