Unknown

Dataset Information

EDiVA-Classification and prioritization of pathogenic variants for clinical diagnostics.


ABSTRACT: Mendelian diseases have shown to be an and efficient model for connecting genotypes to phenotypes and for elucidating the function of genes. Whole-exome sequencing (WES) accelerated the study of rare Mendelian diseases in families, allowing for directly pinpointing rare causal mutations in genic regions without the need for linkage analysis. However, the low diagnostic rates of 20-30% reported for multiple WES disease studies point to the need for improved variant pathogenicity classification and causal variant prioritization methods. Here, we present the exome Disease Variant Analysis (eDiVA; http://ediva.crg.eu), an automated computational framework for identification of causal genetic variants (coding/splicing single-nucleotide variants and small insertions and deletions) for rare disea

SUBMITTER: Bosio M 

PROVIDER: S-EPMC6767450 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature