학술논문

SvAnna: efficient and accurate pathogenicity prediction of coding and regulatory structural variants in long-read genome sequencing
Document Type
article
Source
Genome Medicine. 14(1)
Subject
Biological Sciences
Bioinformatics and Computational Biology
Genetics
Human Genome
Biotechnology
Generic health relevance
Good Health and Well Being
Base Sequence
Chromosome Mapping
Genomics
Humans
Sequence Analysis
DNA
Virulence
Long-read sequencing
Structural variant
Whole genome sequencing
Clinical Sciences
Language
Abstract
Structural variants (SVs) are implicated in the etiology of Mendelian diseases but have been systematically underascertained owing to sequencing technology limitations. Long-read sequencing enables comprehensive detection of SVs, but approaches for prioritization of candidate SVs are needed. Structural variant Annotation and analysis (SvAnna) assesses all classes of SVs and their intersection with transcripts and regulatory sequences, relating predicted effects on gene function with clinical phenotype data. SvAnna places 87% of deleterious SVs in the top ten ranks. The interpretable prioritizations offered by SvAnna will facilitate the widespread adoption of long-read sequencing in diagnostic genomics. SvAnna is available at https://github.com/TheJacksonLaboratory/SvAnn a .