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DeepChIA-PET:准确地预测来自Hi-C和ChIP-seq的ChIA-PET,使用深度扩展网络

Tong Liu¹, Zheng Wang¹

¹Department of Computer Science, University of Miami, Coral Gables, Florida, United States of America.

PLoS computational biology

|July 13, 2023

概括

深度学习方法DeepChIA-PET可以从Hi-C和ChIP-seq数据中准确预测色素相互作用. 这种计算方法减少了对广泛的湿实验室实验的需求,使得对染色质折叠和基因调节的更广泛研究成为可能.

科学领域:

基因组学就是基因组学.
计算生物学计算生物学
分子生物学分子生物学

背景情况:

通过对结尾标签测序 (ChIA-PET) 的染色质相互作用分析揭示了由特定的DNA结合蛋白调解的全基因组相互作用.
对于理解染色质折叠和转录调节,ChIA-PET实验至关重要.
由于ChIA-PET数据集的可用性有限,因此需要计算方法来预测使用更常见的数据类型 (如Hi-C和ChIP-seq.) 的交互.

研究的目的:

开发一种计算方法,DeepChIA-PET,用于准确预测ChIA-PET相互作用.
利用Hi-C和ChIP-seq数据推断ChIA-PET相互作用,减少对湿实验室实验的需求.
为了验证模型在不同细胞类型和蛋白质因子中的性能.

主要方法:

开发了一种监督深度学习方法,DeepChIA-PET.
该模型使用Hi-C和ChIP-seq数据作为输入来预测ChIA-PET相互作用.
深度模型使用CTCF介导的GM12878细胞的ChIA-PET数据进行训练,其中包括40个扩展的剩余卷积块.

主要成果:

在只使用高温数据时,DeepChIA-PET显著超过了基于森林的随机方法Peakachu.
整合ChIP-seq数据提高了分类性能,尽管Hi-C数据发挥了更主导的作用.
该模型准确地预测了GM12878和HeLa细胞中的CTCF介导的ChIA-PET,以及各种细胞类型的非CTCF相互作用 (RNAPII,RAD21).

结论:

DeepChIA-PET是一个准确的计算工具,用于预测ChIA-PET相互作用.
该方法有效地预测了不同细胞类型中各种染色质相关蛋白质介导的相互作用.
DeepChIA-PET通过利用广泛可用的Hi-C和ChIP-seq数据,促进了对色素相互作用的研究.

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相关概念视频

Chromatin Immunoprecipitation- ChIP

Chromatin Immunoprecipitation- ChIP

Chromatin immunoprecipitation, or ChIP, is an antibody-based technique used to identify sites on DNA that bind to transcription factors of interest or histone proteins. It also helps determine the type of histone modifications such as acetylation, phosphorylation, or methylation.
Types of ChIP
ChIP can be divided into two types - X-ChIP and N-ChIP. X-ChIP involves in vivo cross-linking of histones and regulatory proteins to DNA, fragmenting the DNA by sonication, and isolating the protein-DNA...