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Cell. 2019 Jan 24;176(3):535-548.e24. doi: 10.1016/j.cell.2018.12.015. Epub 2019 Jan 17.

Predicting Splicing from Primary Sequence with Deep Learning.

Author information

1
Illumina Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA, USA.
2
Department of Psychiatry, University of California, San Francisco, San Francisco, CA, USA.
3
Department of Genetics, Stanford University, Stanford, CA, USA.
4
Illumina Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
5
Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA.
6
Illumina Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA, USA. Electronic address: kfarh@illumina.com.

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

PMID:
30661751
DOI:
10.1016/j.cell.2018.12.015

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