Using multi-scale genomics to associate poorly annotated genes with rare diseases.

ABSTRACT:

Background

Next-generation sequencing (NGS) has significantly transformed the landscape of identifying disease-causing genes associated with genetic disorders. However, a substantial portion of sequenced patients remains undiagnosed. This may be attributed not only to the challenges posed by harder-to-detect variants, such as non-coding and structural variations but also to the existence of variants in genes not previously associated with the patient's clinical phenotype. This study introduces EvORanker, an algorithm that integrates unbiased data from 1,028 eukaryotic genomes to link mutated genes to clinical phenotypes.

Methods

EvORanker utilizes clinical data, multi-scale phylogenetic profiling, and other omics data to prioritize disease-associated genes. It was evaluated

SUBMITTER: Canavati C 

PROVIDER: S-EPMC10765705 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature