Genetik tanı için otomatik dizi değişkeni sınıflandırma aracı

Advancements in DNA sequencing rapidly improved our understanding of the genome in re-cent years. Today, these advances are revealing thousands of genetic variants that are still wait-ing to be deciphered. Establishing the association between genetic variation and diseases ena-bles us to better appreciate the biology of diseases and to develop effective therapeutical solu-tions. To this end, clinical organizations such as American College of Medical Genetics and Ge-nomics (ACMG) developed standards and guidelines to interpret sequence variants. Clinical Genomics Resource (ClinGen) provided further specifications to the guidelines to improve the interpretations. However, implementation of the guidelines takes considerable time and re-quires substantial expertise in clinical genetics. Available computational tools to automate the process (i) do not comprehensively describe how their frameworks function, (ii) fail to com-pletely follow the latest specifications and (iii) lack high consistency with variant classifications manually performed by experts. Here, this work presents automated ACMG-based variant clas-sifier (AAVC), which computationally interprets sequence variants based on the ACMG Guide-lines and the ClinGen Specifications by aggregating information from large public databases and in silico prediction tools including BayesDel, ClinVar, Ensembl, gnomAD, PhyloP, Re-peatMasker, SpliceAI and UniProt. The tool demonstrates a high concordance (99.67%) with FDA-approved variant classification database, reveals more than two hundred novel variants in clinically actionable genes in the Turkish Variome and reclassifies at least 57,000 inconclusive variants in ClinVar as pathogenic or likely pathogenic. The work provides a comprehensive framework to enable rapid and accurate interpretation of sequence variants by the ACMG Standards.