Splice2Deep: An ensemble of deep convolutional neural networks for improved splice site prediction in genomic DNA.

ABSTRACT:

Background

The accurate identification of the exon/intron boundaries is critical for the correct annotation of genes with multiple exons. Donor and acceptor splice sites (SS) demarcate these boundaries. Therefore, deriving accurate computational models to predict the SS are useful for functional annotation of genes and genomes, and for finding alternative SS associated with different diseases. Although various models have been proposed for the in silico prediction of SS, improving their accuracy is required for reliable annotation. Moreover, models are often derived and tested using the same genome, providing no evidence of broad application, i.e. to other poorly studied genomes.

Results

With this in mind, we developed the Splice2Deep models for SS detection. Each model is a

SUBMITTER: Albaradei S 

PROVIDER: S-EPMC7285987 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature