SvAnna: efficient and accurate pathogenicity prediction of coding and regulatory structural variants in long-read genome sequencing
Daniel Daniš, Julius O.B. Jacobsen, Parithi Balachandran, Qihui Zhu, Feyza Yilmaz, Justin Reese, Matthias Haimel, Gholson J. Lyon, Ingo Helbig, Chris Mungall, Christine R. Beck, Charles Lee, Damian Smedley, Peter N. Robinson
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 .