ECOLE: Learning to call copy number variants on whole exome sequencing data

Berk Mandiracioglu, Furkan Ozden, Gun Kaynar, Mehmet Alper Yilmaz, Can Alkan and A. Ercument Cicek ()
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Berk Mandiracioglu: EPFL
Furkan Ozden: Oxford University
Gun Kaynar: Bilkent University
Mehmet Alper Yilmaz: Bilkent University
Can Alkan: Bilkent University
A. Ercument Cicek: Bilkent University

Nature Communications, 2024, vol. 15, issue 1, 1-13

Abstract: Abstract Copy 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 .

Date: 2024
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DOI: 10.1038/s41467-023-44116-y

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