Pathogenicity assessment of Germline Variants

Dataset to reproduce results shown in paper Nicora, G., Zucca, S., Limongelli, I. et al. A machine learning approach based on ACMG/AMP guidelines for genomic variant classification and prioritization. Sci Rep 12, 2517 (2022). https://doi.org/10.1038/s41598-022-06547-3.Variants associated with a broad set of mendelian diseases are collected from the CLINVITAE public dataset (http://clinvitae.invitae.com) and they are annotated according to the ACMG/AMP guidelines through the eVai software (https://www.engenome.com).Variants associated with hereditary cancer (reported as pathogenic in the ICR639 study) are also annotated and uploaded. Code: https://github.com/GiovannaNicora/MLVar