Predicting Splicing from Primary Sequence with Deep Learning
Predicting Splicing from Primary Sequence with Deep Learning
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DOI：
10.1016/j.cell.2018.12.015
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发表时间：
2019-01-24
期刊：
影响因子：
64.5
通讯作者：
Farh, Kyle Kai-How
中科院分区：
文献类型：
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作者：
Jaganathan, Kishore;Panagiotopoulou, Sofia Kyriazopoulou;Farh, Kyle Kai-How
将pre- mrna剪接成成熟转录本的精确度值得注意，但细胞机制实现这种特异性的机制尚不完全清楚。在这里，我们描述了一个深度神经网络，可以准确地预测任意mrna前转录序列的剪接连接，从而精确预测导致隐剪接的非编码遗传变异。具有预测剪接改变后果的同义突变和内含子突变在RNA-seq上的验证率很高，并且在人类群体中具有很强的危害性。与健康对照组相比，自闭症和智力残疾患者中具有预期剪接改变后果的新生突变显著丰富，并且在28名患者中的21名患者中验证了RNA-seq。我们估计，在罕见遗传疾病患者中，9%-11%的致病性突变是由这类以前未被重视的疾病变异引起的。
The splicing of pre-mRNAs into mature transcripts is remarkable for its precision, but the mechanisms by which the cellular machinery achieves such specificity are incompletely understood. Here, we describe a deep neural network that accurately predicts splice junctions from an arbitrary pre-mRNA transcript sequence, enabling precise prediction of noncoding genetic variants that cause cryptic splicing. Synonymous and intronic mutations with predicted splice-altering consequence validate at a high rate on RNA-seq and are strongly deleterious in the human population. De novo mutations with predicted splice-altering consequence are significantly enriched in patients with autism and intellectual disability compared to healthy controls and validate against RNA-seq in 21 out of 28 of these patients. We estimate that 9%-11% of pathogenic mutations in patients with rare genetic disorders are caused by this previously underappreciated class of disease variation.