Predicting Splicing from Primary Sequence with Deep Learning.

Autor:	Jaganathan K; Illumina Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA, USA., Kyriazopoulou Panagiotopoulou S; Illumina Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA, USA., McRae JF; Illumina Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA, USA., Darbandi SF; Department of Psychiatry, University of California, San Francisco, San Francisco, CA, USA., Knowles D; Department of Genetics, Stanford University, Stanford, CA, USA., Li YI; Department of Genetics, Stanford University, Stanford, CA, USA., Kosmicki JA; Illumina Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA., Arbelaez J; Department of Psychiatry, University of California, San Francisco, San Francisco, CA, USA., Cui W; Illumina Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA, USA., Schwartz GB; Department of Psychiatry, University of California, San Francisco, San Francisco, CA, USA., Chow ED; Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA., Kanterakis E; Illumina Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA, USA., Gao H; Illumina Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA, USA., Kia A; Illumina Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA, USA., Batzoglou S; Illumina Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA, USA., Sanders SJ; Department of Psychiatry, University of California, San Francisco, San Francisco, CA, USA., Farh KK; Illumina Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA, USA. Electronic address: kfarh@illumina.com.
Jazyk:	angličtina
Zdroj:	Cell [Cell] 2019 Jan 24; Vol. 176 (3), pp. 535-548.e24. Date of Electronic Publication: 2019 Jan 17.
DOI:	10.1016/j.cell.2018.12.015
Abstrakt:	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. (Copyright © 2018 Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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