DeepM6ASeq: prediction and characterization of m6A-containing sequences using deep learning
DeepM6ASeq: prediction and characterization of m6A-containing sequences using deep learning
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DOI：
10.1186/s12859-018-2516-4
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发表时间：
2018-12-31
影响因子：
3
通讯作者：
Hamada, Michiaki
中科院分区：
文献类型：
--
作者：
Zhang, Yiqian;Hamada, Michiaki
研究背景N6-甲基腺苷（m6 A）是一种常见的RNA甲基化修饰，广泛存在于各种生物中。作为一种转录后甲基化类型，m6 A在多种RNA活动中发挥重要作用，如选择性剪接、与microRNA的相互作用和翻译效率。虽然现有的工具可以预测m6 A在单碱基分辨率，它仍然是具有挑战性的提取周围m6 A sites.ResultsWe的生物信息实现了一个深度学习框架，命名为DeepM 6ASeq，预测m6 A包含序列和周围的生物特征的基础上miCLIP-Seq数据，检测m6 A网站在单碱基分辨率。与其他机器学习分类器相比，DeepM 6ASeq表现出更好的性能。此外，对m6 A-Seq数据进行的独立测试（识别含m6 A的基因组区域）表明，我们的模型在预测含m6 A的序列方面具有竞争力。从DeepM 6ASeq中学习到的基序对应于已知的m6 A阅读器。值得注意的是，DeepM 6ASeq还识别了一个新识别的m6 A阅读器：FMR 1。此外，我们还发现深度学习模型中的显着图可以用来可视化m6 A位点的位置。结论我们开发了一个基于深度学习的框架来预测和表征含有m6 A的序列，希望能帮助研究者获得更多关于m6 A研究的见解。源代码可在https://github.com/rreybeyb/DeepM6ASeq上获得。
BackgroundN6-methyladensine (m6A) is a common and abundant RNA methylation modification found in various species. As a type of post-transcriptional methylation, m6A plays an important role in diverse RNA activities such as alternative splicing, an interplay with microRNAs and translation efficiency. Although existing tools can predict m6A at single-base resolution, it is still challenging to extract the biological information surrounding m6A sites.ResultsWe implemented a deep learning framework, named DeepM6ASeq, to predict m6A-containing sequences and characterize surrounding biological features based on miCLIP-Seq data, which detects m6A sites at single-base resolution. DeepM6ASeq showed better performance as compared to other machine learning classifiers. Moreover, an independent test on m6A-Seq data, which identifies m6A-containing genomic regions, revealed that our model is competitive in predicting m6A-containing sequences. The learned motifs from DeepM6ASeq correspond to known m6A readers. Notably, DeepM6ASeq also identifies a newly recognized m6A reader: FMR1. Besides, we found that a saliency map in the deep learning model could be utilized to visualize locations of m6A sites.ConculsionWe developed a deep-learning-based framework to predict and characterize m6A-containing sequences and hope to help investigators to gain more insights for m6A research. The source code is available at https://github.com/rreybeyb/DeepM6ASeq.