CNV-Hub: an integrated web-based platform for CNV classification and interpretation using multi-algorithm consensus.

Abstract

Copy number variations (CNVs) are genomic alterations that can cause rare genetic diseases. Their interpretation requires consulting multiple databases and following classification guidelines, such as those from the American College of Medical Genetics (ACMG) and the Clinical Genome Resource (ClinGen). In France, the Achro-Puce working group has also established recommendations to support CNV interpretation. Despite these resources, CNV analysis remains time-consuming, as it requires reviewing gene content, regulatory elements, and associated syndromes. To address this challenge, we developed CNV-Hub, a web-based platform that streamlines CNV classification and interpretation. CNV-Hub integrates five algorithms: two based on ACMG recommendations (AnnotSV, ClassifyCNV), two using machine learning (X-CNV, ISV), and one specifically developed according to French guidelines. In addition to automated pathogenicity predictions, CNV-Hub provides annotations for each CNV, including gene dosage sensitivity scores (pHaplo, pTriplo), syndrome associations, and direct links to databases such as OMIM and PubMed. The platform's user-friendly interface enables rapid, evidence-based CNV evaluation. By incorporating machine learning among its classification algorithms, CNV-Hub improves the interpretation of uncertain variants by integrating additional parameters. This tool reduces the time required for CNV analysis while maintaining accuracy and reliability, representing a significant advance in molecular cytogenetics and supporting geneticists in clinical decision-making.