Back to Search Start Over

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

Authors :
Kurosawa R
Iida K
Ajiro M
Awaya T
Yamada M
Kosaki K
Hagiwara M
Source :
BMC genomics [BMC Genomics] 2023 Oct 10; Vol. 24 (1), pp. 601. Date of Electronic Publication: 2023 Oct 10.
Publication Year :
2023

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.<br />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 splicing constraint metric were used to maximize the predictive sensitivity and specificity, respectively. PDIVAS showed an average precision of 0.92 and a maximum MCC of 0.88 in classifying these variants, which were the best of the previous predictors. When PDIVAS was applied to genome sequencing analysis on a threshold with 95% sensitivity for reported pathogenic SAVs, an average of 27 pathogenic candidates were extracted per individual. Furthermore, the causative variants in simulated patient genomes were more efficiently prioritized than the previous predictors.<br />Conclusion: Incorporating PDIVAS into variant interpretation pipelines will enable efficient detection of disease-causing deep-intronic SAVs and contribute to improving the diagnostic yield. PDIVAS is publicly available at https://github.com/shiro-kur/PDIVAS .<br /> (© 2023. BioMed Central Ltd., part of Springer Nature.)

Details

Language :
English
ISSN :
1471-2164
Volume :
24
Issue :
1
Database :
MEDLINE
Journal :
BMC genomics
Publication Type :
Academic Journal
Accession number :
37817060
Full Text :
https://doi.org/10.1186/s12864-023-09645-2