Prediction of histone post-translational modifications using deep learning
Prediction of histone post-translational modifications using deep learning
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DOI：
10.1093/bioinformatics/btaa1075
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发表时间：
2020-12
期刊：
影响因子：
5.8
通讯作者：
D. Baisya;S. Lonardi
中科院分区：
文献类型：
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作者：
D. Baisya;S. Lonardi
组蛋白翻译后修饰（PTMs）参与细胞的多种基本调控过程，包括转录控制。最近的研究表明，组蛋白ptm可以通过转录因子结合或dna酶超敏性数据准确预测。同样，研究表明，人们可以从潜在的DNA初级序列预测ptm。在这项研究中，我们引入了一个名为DeepPTM的深度学习架构，用于从转录因子结合数据和初级DNA序列预测组蛋白ptm。大量的实验结果表明，我们的深度学习模型的预测精度优于Benveniste等人（PNAS 2014）和DeepHistone （BMC Genomics 2019）提出的模型。我们的框架的竞争优势在于深度学习与有效预处理步骤的协同使用。我们的分类框架还发现，对一小部分转录因子（组蛋白- ptm和细胞类型特异性）的了解可以提供与使用所有转录因子数据几乎相同的预测准确性。可用性https://github.com/dDipankar/DeepPTM。
MOTIVATION Histone post-translational modifications (PTMs) are involved in a variety of essential regulatory processes in the cell, including transcription control. Recent studies have shown that histone PTMs can be accurately predicted from the knowledge of transcription factor binding or DNase hypersensitivity data. Similarly, it has been shown that one can predict PTMs from the underlying DNA primary sequence. RESULTS In this study, we introduce a deep learning architecture called DeepPTM for predicting histone PTMs from transcription factor binding data and the primary DNA sequence. Extensive experimental results show that our deep learning model outperforms the prediction accuracy of the model proposed in Benveniste et al. (PNAS 2014) and DeepHistone (BMC Genomics 2019). The competitive advantage of our framework lies in the synergistic use of deep learning combined with an effective pre-processing step. Our classification framework has also enabled the discovery that the knowledge of a small subset of transcription factors (which are histone-PTM and cell-type specific) can provide almost the same prediction accuracy that can be obtained using all the transcription factors data. AVAILABILITY https://github.com/dDipankar/DeepPTM.