Predicting Splicing from Primary Sequence with Deep Learning
- PMID: 30661751
- DOI: 10.1016/j.cell.2018.12.015
Predicting Splicing from Primary Sequence with Deep Learning
Abstract
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.
Keywords: artificial intelligence; deep learning; genetics; splicing.
Copyright © 2018 Elsevier Inc. All rights reserved.
Comment in
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The Splicing Code Goes Deep.Cell. 2019 Jan 24;176(3):414-416. doi: 10.1016/j.cell.2019.01.013. Cell. 2019. PMID: 30682368
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Learning the language of splicing.Nat Rev Genet. 2019 Mar;20(3):132-133. doi: 10.1038/s41576-019-0097-3. Nat Rev Genet. 2019. PMID: 30683891 No abstract available.
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