Deep learning for alternative splicing - Brown, R (2019)
Mutations that affect RNA splicing can have severe phenotypic consequences, and contribute to rare and sporadic human disease. Whole-genome sequencing promises to improve diagnosis, but it is often difficult to identify mutations that disrupt splicing, except when they affect canonical donor and acceptor sites. In particular, cryptic mutations that cause novel splice junctions to appear deep within introns of genes are very hard to identify. Accurate computational models are therefore crucial for effective diagnosis.