Application of Deep Learning Models to MicroRNA Transcription Start Site Identification
Application of Deep Learning Models to MicroRNA Transcription Start Site Identification
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DOI：
10.1109/icbcb.2019.8854645
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发表时间：
2019-03
期刊：
影响因子：
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通讯作者：
Clayton Barham;Mingyu Cha;X. Li;Haiyan Hu
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文献类型：
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作者：
Clayton Barham;Mingyu Cha;X. Li;Haiyan Hu
MicroRNA (miRNA) 是约 22 个碱基对的长 RNA，在调节基因表达中发挥重要作用。了解 miRNA 的转录调控对于基因调控至关重要。然而，由于 miRNA 特异性的生物发生，精确识别 miRNA 转录起始位点 (TSS) 通常很困难。现有的计算方法无法有效预测 miRNA TSS。在这里，我们采用结合长短期记忆 (LSTM) 和卷积神经网络 (CNN) 技术的深度学习架构来检测可访问染色质区域中的 miRNA TSS。通过对基准实验数据的测试，我们证明深度学习模型的性能优于支持向量机，并且可以准确区分 miRNA TSS 与侧翼区域和基因间区域。
MicroRNAs (miRNA) are ~22 base pair long RNAs that play important roles in regulating gene expression. Understanding the transcriptional regulation of miRNA is critical to gene regulation. However, it is often difficult to precisely identify miRNA transcription start sites (TSSs) due to miRNA-specific biogenesis. Existing computational methods cannot effectively predict miRNA TSSs. Here, we employed deep learning architectures incorporating Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) techniques to detect miRNA TSSs in regions of accessible chromatin. By testing on benchmark experimental data, we demonstrated that deep learning models outperform support vector machine and can accurately distinguish miRNA TSSs from both flanking regions and intergenic regions.