DeepChIA-PET: Accurately predicting ChIA-PET from Hi-C and ChIP-seq with deep dilated networks.
DeepChIA-PET: Accurately predicting ChIA-PET from Hi-C and ChIP-seq with deep dilated networks.
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DOI：
10.1371/journal.pcbi.1011307
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发表时间：
2023-07
影响因子：
4.3
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中科院分区：
文献类型：
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通过配对末端标签测序（ChIA-PET）的染色质相互作用分析可以捕获由特定DNA相关蛋白介导的全基因组染色质相互作用。ChIA-PET实验已被应用于探索不同的蛋白质因子在染色质折叠和转录调控中的关键作用。然而，与广泛可用的Hi-C和ChIP-seq数据相比，文献中可用的ChIA-PET数据集并不多。需要一种用于从Hi-C和ChIP-seq数据准确预测ChIA-PET相互作用的计算方法，该方法可以节省进行湿实验室实验的努力。在这里，我们介绍了DeepChIA-PET，这是一种有监督的深度学习方法，可以通过学习ChIA-PET与两种广泛使用的数据类型（Hi-C和ChIP-seq）之间的潜在关系来准确预测ChIA-PET相互作用。我们用GM 12878的CTCF介导的ChIA-PET作为基础事实训练了我们的深度模型，深度网络包含40个膨胀的残余卷积块。我们首先表明，仅使用Hi-C作为输入的DeepChIA-PET显着优于Peakachu，Peakachu是另一种使用随机森林从Hi-C预测ChIA-PET的计算方法。我们接下来证明了添加ChIP-seq作为一个额外的输入确实提高了DeepChIA-PET的分类性能，但Hi-C在DeepChIA-PET中的作用比ChIP-seq更突出。我们的评估结果表明，我们学习的模型不仅可以准确预测GM 12878和HeLa中CTCF介导的ChIA-ET，还可以准确预测非CTCF ChIA-PET相互作用，包括GM 12878的RNA聚合酶II（RNAPII）ChIA-PET，GM 12878的RAD 21 ChIA-PET和K562的RAD 21 ChIA-PET。总的来说，DeepChIA-PET是预测来自不同细胞类型的各种染色质相关蛋白介导的ChIA-PET相互作用的准确工具。各种技术已被广泛用于建模和研究三维（3D）基因组，如Hi-C，ChIA-PET和HiChIP。与Hi-C不同，ChIA-PET可以捕获由预定义的DNA相关蛋白介导的全基因组染色体接触。与从不同物种的不同细胞系捕获的足够的Hi-C数据资源相比，只有有限数量的ChIA-PET数据集是公开可用的。在这里，我们开发了一种深度学习方法，使用Hi-C和ChIP-seq数据直接预测ChIA-PET。我们的评估结果表明，我们的学习模型可以成功地预测由不同的DNA相关蛋白介导的ChIA-PET的一个单独的细胞系。我们的方法是从湿实验室获得ChIA-PET接触的有效替代方案。
Chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) can capture genome-wide chromatin interactions mediated by a specific DNA-associated protein. The ChIA-PET experiments have been applied to explore the key roles of different protein factors in chromatin folding and transcription regulation. However, compared with widely available Hi-C and ChIP-seq data, there are not many ChIA-PET datasets available in the literature. A computational method for accurately predicting ChIA-PET interactions from Hi-C and ChIP-seq data is needed that can save the efforts of performing wet-lab experiments. Here we present DeepChIA-PET, a supervised deep learning approach that can accurately predict ChIA-PET interactions by learning the latent relationships between ChIA-PET and two widely used data types: Hi-C and ChIP-seq. We trained our deep models with CTCF-mediated ChIA-PET of GM12878 as ground truth, and the deep network contains 40 dilated residual convolutional blocks. We first showed that DeepChIA-PET with only Hi-C as input significantly outperforms Peakachu, another computational method for predicting ChIA-PET from Hi-C but using random forests. We next proved that adding ChIP-seq as one extra input does improve the classification performance of DeepChIA-PET, but Hi-C plays a more prominent role in DeepChIA-PET than ChIP-seq. Our evaluation results indicate that our learned models can accurately predict not only CTCF-mediated ChIA-ET in GM12878 and HeLa but also non-CTCF ChIA-PET interactions, including RNA polymerase II (RNAPII) ChIA-PET of GM12878, RAD21 ChIA-PET of GM12878, and RAD21 ChIA-PET of K562. In total, DeepChIA-PET is an accurate tool for predicting the ChIA-PET interactions mediated by various chromatin-associated proteins from different cell types. Various techniques have been widely used to model and investigate three-dimensional (3D) genomes, such as Hi-C, ChIA-PET, and HiChIP. Unlike Hi-C, ChIA-PET can capture genome-wide chromosomal contacts mediated by a predefined DNA-associated protein. Compared with an adequate resource of Hi-C data captured from different cell lines for various species, only a limited number of ChIA-PET data sets are publicly available. Here we developed a deep learning method using Hi-C and ChIP-seq data to predict ChIA-PET directly. Our evaluation results demonstrate that our learned models can successfully predict ChIA-PETs mediated by a different DNA-associated protein for a separate cell line. Our method is an effective alternative to obtaining ChIA-PET contacts from a wet lab.
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