PDIVAS: Pathogenicity predictor for Deep-Intronic Variants causing Aberrant Splicing.

ABSTRACT:

Background

Deep-intronic variants that alter RNA splicing were ineffectively evaluated in the search for the cause of genetic diseases. Determination of such pathogenic variants from a vast number of deep-intronic variants (approximately 1,500,000 variants per individual) represents a technical challenge to researchers. Thus, we developed a Pathogenicity predictor for Deep-Intronic Variants causing Aberrant Splicing (PDIVAS) to easily detect pathogenic deep-intronic variants.

Results

PDIVAS was trained on an ensemble machine-learning algorithm to classify pathogenic and benign variants in a curated dataset. The dataset consists of manually curated pathogenic splice-altering variants (SAVs) and commonly observed benign variants within deep introns. Splicing features and a spl

SUBMITTER: Kurosawa R 

PROVIDER: S-EPMC10563346 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature