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2014 ; 30
(12
): i121-9
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Deep learning of the tissue-regulated splicing code
#MMPMID24931975
Leung MK
; Xiong HY
; Lee LJ
; Frey BJ
Bioinformatics
2014[Jun]; 30
(12
): i121-9
PMID24931975
show ga
MOTIVATION: Alternative splicing (AS) is a regulated process that directs the
generation of different transcripts from single genes. A computational model that
can accurately predict splicing patterns based on genomic features and cellular
context is highly desirable, both in understanding this widespread phenomenon,
and in exploring the effects of genetic variations on AS. METHODS: Using a deep
neural network, we developed a model inferred from mouse RNA-Seq data that can
predict splicing patterns in individual tissues and differences in splicing
patterns across tissues. Our architecture uses hidden variables that jointly
represent features in genomic sequences and tissue types when making predictions.
A graphics processing unit was used to greatly reduce the training time of our
models with millions of parameters. RESULTS: We show that the deep architecture
surpasses the performance of the previous Bayesian method for predicting AS
patterns. With the proper optimization procedure and selection of
hyperparameters, we demonstrate that deep architectures can be beneficial, even
with a moderately sparse dataset. An analysis of what the model has learned in
terms of the genomic features is presented.
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