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DeepSplice: Deep classification of novel splice junctions revealed by RNA-seq

机译:DeepSplice:RNA-SEQ揭示的新型剪接结的深度分类

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Alternative splicing (AS) is a regulated process that enables the production of multiple mRNA transcripts from a single multi-exon gene. The availability of large-scale RNA-seq datasets has made it possible to predict splice junctions, as well as splice sites through spliced alignment to the reference genome. This greatly enhances the capability to decipher gene structures and explore the diversity of splicing variants. However, existing ab initio aligners are vulnerable to false positive spliced alignments as a result of sequence errors and random sequence matches. These spurious alignments can lead to a significant set of false positive splice junction predictions, confusing downstream analyses of splice variant detection and abundance estimation. In this work, we illustrate that splice junction sequence characteristics can be ascertained from experimental data with deep learning techniques. We employ deep convolutional neural networks for a novel splice junction classification tool named DeepSplice that (i) outperforms state-of-the-art methods for predicting splice sites, (ii) shows high computational efficiency and (iii) can be applied to self-defined training data by users.
机译:替代剪接(AS)是调节过程,其能够从单个多外显子基因产生多个mRNA转录物。大规模RNA-SEQ数据集的可用性使得可以通过与参考基因组的拼接对准来预测接头连接点,以及接头位点。这大大提高了破译基因结构的能力,并探索拼接变体的多样性。然而,由于序列误差和随机序列匹配,现有的AB Initio对准器容易受到假呈剪接对齐。这些杂散的对准可以导致一组很大一组错误的阳性剪接结转预测,令人困惑的剪接变体检测和丰度估计的下游分析。在这项工作中,我们说明了从具有深度学习技术的实验数据确定接头结序列特征。我们采用深度卷积神经网络的新型剪接结分类工具,命名为DeepSplice(i)优于预测接头位点的最先进方法,(ii)显示了高计算效率,并且可以应用于自我 - 用户定义培训数据。

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