CADD-Splice-improving genome-wide variant effect prediction using deep learning-derived splice scores.

ABSTRACT:

Background

Splicing of genomic exons into mRNAs is a critical prerequisite for the accurate synthesis of human proteins. Genetic variants impacting splicing underlie a substantial proportion of genetic disease, but are challenging to identify beyond those occurring at donor and acceptor dinucleotides. To address this, various methods aim to predict variant effects on splicing. Recently, deep neural networks (DNNs) have been shown to achieve better results in predicting splice variants than other strategies.

Methods

It has been unclear how best to integrate such process-specific scores into genome-wide variant effect predictors. Here, we use a recently published experimental data set to compare several machine learning methods that score variant effects on splicing. We integr

SUBMITTER: Rentzsch P 

PROVIDER: S-EPMC7901104 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature