DeepPASTA: deep neural network based polyadenylation site analysis
DeepPASTA: deep neural network based polyadenylation site analysis
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DOI：
10.1093/bioinformatics/btz283
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发表时间：
2019-11-15
期刊：
影响因子：
5.8
通讯作者：
Jiang, Tao
中科院分区：
文献类型：
--
作者：
Arefeen, Ashraful;Xiao, Xinshu;Jiang, Tao
动机：前体 mRNA 3' 末端附近的替代聚腺苷酸化 (polyA) 位点会产生具有不同 3' 非翻译区 (3' UTR) 的多个 mRNA 转录物。 3' UTR 的序列元件对于许多生物活性至关重要，例如 mRNA 稳定性、亚细胞定位、蛋白质翻译、蛋白质结合和翻译效率。此外，文献中的大量研究报道了疾病与 3' UTR 缩短（或延长）之间的相关性。由于替代性多聚腺苷酸位点在哺乳动物基因中很常见，因此已经发布了几种机器学习工具来根据序列数据预测多聚腺苷酸位点。这些工具要么考虑有限的序列特征，要么使用相对较旧的算法进行多聚腺苷酸位点预测。此外，之前的工具都没有将RNA二级结构视为预测polyA位点的特征。结果：在本文中，我们提出了一种新的深度学习模型，称为DeepPASTA，用于根据序列和RNA二级结构数据预测polyA位点。然后将该模型扩展到预测组织特异性多聚腺苷酸位点。此外，该工具可以预测特定组织中基因的最显性（即经常使用的）polyA 位点，以及当给定同一基因的两个 polyA 位点时的相对优势。我们的大量实验表明，DeepPASTA 显着优于现有的多聚腺苷酸位点预测和组织特异性相对和绝对显性多聚腺苷酸位点预测工具。
Motivation: Alternative polyadenylation (polyA) sites near the 3' end of a pre-mRNA create multiple mRNA transcripts with different 3' untranslated regions (3' UTRs). The sequence elements of a 3' UTR are essential for many biological activities such as mRNA stability, sub-cellular localization, protein translation, protein binding and translation efficiency. Moreover, numerous studies in the literature have reported the correlation between diseases and the shortening (or lengthening) of 3' UTRs. As alternative polyA sites are common in mammalian genes, several machine learning tools have been published for predicting polyA sites from sequence data. These tools either consider limited sequence features or use relatively old algorithms for polyA site prediction. Moreover, none of the previous tools consider RNA secondary structures as a feature to predict polyA sites.Results: In this paper, we propose a new deep learning model, called DeepPASTA, for predicting polyA sites from both sequence and RNA secondary structure data. The model is then extended to predict tissue-specific polyA sites. Moreover, the tool can predict the most dominant (i.e. frequently used) polyA site of a gene in a specific tissue and relative dominance when two polyA sites of the same gene are given. Our extensive experiments demonstrate that DeepPASTA signisficantly outperforms the existing tools for polyA site prediction and tissue-specific relative and absolute dominant polyA site prediction.