DeepPolyA: A Convolutional Neural Network Approach for Polyadenylation Site Prediction
DeepPolyA: A Convolutional Neural Network Approach for Polyadenylation Site Prediction
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DOI：
10.1109/access.2018.2825996
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发表时间：
2018-01-01
期刊：
影响因子：
3.9
通讯作者：
Hakonarson, Hakon
中科院分区：
文献类型：
--
作者：
Gao, Xin;Zhang, Jie;Hakonarson, Hakon
多腺苷酸化 (Poly(A)) 在基因调控中发挥着至关重要的作用，尤其是在信使 RNA 代谢、蛋白质多样化和蛋白质定位中。准确预测多腺苷酸化位点和识别控制多腺苷酸化的基序对于解释基因表达模式、提高基因组注释的准确性和理解基因调控机制至关重要。尽管在使用机器学习技术解决这个问题方面取得了相当大的进步，但其效率仍然受到缺乏经验和领域知识的限制，无法仔细设计和生成有用的特征，尤其是对于植物而言。随着广泛的基因组数据集和领先的计算技术的不断增加，深度学习方法，特别是卷积神经网络，已被应用于直接从基因序列中自动识别和理解基因调控并预测未知的序列谱。在这里，我们提出了 DeepPolyA，一种基于深度卷积神经网络的新方法，用于预测植物拟南芥基因序列的聚腺苷酸化位点。我们研究了各种深度神经网络架构，并根据经典机器学习算法和几种流行的深度学习模型评估其性能。实验结果表明，在各种性能指标方面，DeepPolyA 明显优于竞争方法。我们进一步可视化 DeepPolyA 的学习基序，以提供对我们的模型和学习的聚腺苷酸化信号的见解。
Polyadenylation (Poly(A)) plays crucial roles in gene regulation, especially in messenger RNA metabolism, protein diversification, and protein localization. Accurate prediction of polyadenylation sites and identification of motifs that controlling polyadenylation are fundamental for interpreting the patterns of gene expression, improving the accuracy of genome annotation and comprehending the mechanisms that governing gene regulation. Despite considerable advances in using machine learning techniques for this problem, its efficiency is still limited by the lack of experiences and domain knowledge to carefully design and generate useful features, especially for plants. With the increasing availability of extensive genomic data sets and leading computational techniques, deep learning methods, especially convolutional neural networks, have been applied to automatically identify and understand gene regulation directly from gene sequences and predict unknown sequence profiles. Here, we present DeepPolyA, a new deep convolutional neural network-based approach, to predict polyadenylation sites from the plant Arabidopsis thaliana gene sequences. We investigate various deep neural network architectures and evaluate their performance against classical machine learning algorithms and several popular deep learning models. Experimental results demonstrate that DeepPolyA is substantially better than competing methods regarding various performance metrics. We further visualize the learned motifs of DeepPolyA to provide insights of our model and learned polyadenylation signals.