NDLI: Chromatin and genomic determinants of alternative splicing

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Chromatin and genomic determinants of alternative splicing

Content Provider	ACM Digital Library
Author	Wang, Kun Hannenhalli, Sridhar Cao, Kan
Abstract	Alternative splicing significantly contributes to proteomic diversity and mis-regulation of splicing can cause diseases in human. Although both genomic and chromatin features have been shown to associate with splicing, the mechanisms by which various chromatin marks influence splicing is not clear for the most part. Moreover, it is not known whether the influence of specific genomic features on splicing is potentially modulated by the chromatin context. Here we report a deep neural network (DNN) model for predicting exon inclusion based on comprehensive genomic and chromatin features. Our analysis in three cell lines shows that, while both genomic and chromatin features can predict splicing to varying degrees, genomic features are the primary drivers of splicing, and the predictive power of chromatin features can largely be explained by their correlation with genomic features; chromatin features do not yield substantial independent contribution to splicing predictability. However, our model identified specific interactions between chromatin and genomic features suggesting that the effect of genomic elements may be modulated by chromatin context.
Starting Page	345
Ending Page	354
Page Count	10
File Format	PDF
ISBN	9781450338530
DOI	10.1145/2808719.2808755
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-09-09
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Alternative splicing Chromatin Deep neural network Exon skipping Machine learning
Content Type	Text
Resource Type	Article

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