Integrating regulatory DNA sequence and gene expression to predict genome-wide chromatin accessibility across cellular contexts
Integrating regulatory DNA sequence and gene expression to predict genome-wide chromatin accessibility across cellular contexts
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DOI：
10.1093/bioinformatics/btz352
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发表时间：
2019-07-15
期刊：
影响因子：
5.8
通讯作者：
Kundaje, Anshul
中科院分区：
文献类型：
--
作者：
Nair, Surag;Kim, Daniel S.;Kundaje, Anshul
在不同的细胞环境中，全基因组范围内的染色质可及性和基因表达谱对于破译转录调控的动力学是至关重要的。最近，卷积神经网络已被用于学习预测顺式调控DNA序列模型的上下文特异性染色质可及性景观。然而，这些上下文特定的调控序列模型不能概括预测跨细胞types.Results我们引入多模态，残留的神经网络架构，整合顺式调控序列和上下文特异性表达的反式调节，以预测全基因组染色质可及性档案跨细胞上下文。我们表明，在整个训练环境中，基因组区域的平均可访问性可以是一个令人惊讶的强大预测因子。我们利用这一特征，并采用新的策略来训练模型，以增强对跨细胞类型的共享和背景特异性染色质可访问位点的全基因组预测。我们解释的模型，以揭示在123个不同的细胞context.Availability和实施的染色质动态的顺式和反式调节的见解代码可在https://github.com/kundajelab/ChromDragoNN.Supplementary信息补充数据可在生物信息学在线。
Motivation Genome-wide profiles of chromatin accessibility and gene expression in diverse cellular contexts are critical to decipher the dynamics of transcriptional regulation. Recently, convolutional neural networks have been used to learn predictive cis-regulatory DNA sequence models of context-specific chromatin accessibility landscapes. However, these context-specific regulatory sequence models cannot generalize predictions across cell types.Results We introduce multi-modal, residual neural network architectures that integrate cis-regulatory sequence and context-specific expression of trans-regulators to predict genome-wide chromatin accessibility profiles across cellular contexts. We show that the average accessibility of a genomic region across training contexts can be a surprisingly powerful predictor. We leverage this feature and employ novel strategies for training models to enhance genome-wide prediction of shared and context-specific chromatin accessible sites across cell types. We interpret the models to reveal insights into cis- and trans-regulation of chromatin dynamics across 123 diverse cellular contexts.Availability and implementation The code is available at https://github.com/kundajelab/ChromDragoNN.Supplementary informationSupplementary data are available at Bioinformatics online.