Mandıracıoğlu, BerkeÖzden, FurkanKaynar, GünYılmaz, Mehmet AlperAlkan, CanÇiçek, A.Ercüment2025-02-222025-02-222024-01-02https://hdl.handle.net/11693/116652Copy number variants (CNV) are shown to contribute to the etiology of several genetic disorders. Accurate detection of CNVs on whole exome sequencing (WES) data has been a long sought-after goal for use in clinics. This was not possible despite recent improvements in performance because algorithms mostly suffer from low precision and even lower recall on expert-curated gold standard call sets. Here, we present a deep learning-based somatic and germline CNV caller for WES data, named ECOLE. Based on a variant of the transformer architecture, the model learns to call CNVs per exon, using high-confidence calls made on matched WGS samples. We further train and fine-tune the model with a small set of expert calls via transfer learning. We show that ECOLE achieves high performance on human expert labelled data for the first time with 68.7% precision and 49.6% recall. This corresponds to precision and recall improvements of 18.7% and 30.8% over the next best-performing methods, respectively. We also show that the same fine-tuning strategy using tumor samples enables ECOLE to detect RT-qPCR-validated variations in bladder cancer samples without the need for a control sample. ECOLE is available at https://github.com/ciceklab/ECOLE. Copy number variants (CNV) are shown to contribute to the etiology of various genetic disorders. Here, authors present ECOLE, a deep learning-based somatic and germline CNV caller for WES data. Utilising a variant of the transformer architecture, the model is trained to call CNVs per exon.EnglishCC BY 4.0 Deed (Attribution 4.0 International)https://creativecommons.org/licenses/by/4.0/Article10.1038/s41467-023-44116-y2041-1723