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

Mandiracioglu, B.; Ozden, F.; Alkan, C.; Cicek, A. E.

doi:10.1101/2022.11.17.516880

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bioRxiv · 10.1101/2022.11.17.516880

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

Mandiracioglu, B.·Ozden, F.·Alkan, C.·Cicek, A. E.

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Abstract

Copy number variants (CNV) are shown to contribute to the etiology of several genetic disor{-}ders. 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 algo{-}rithms 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 ex{-}pert labeled 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.

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Mandiracioglu, B., Ozden, F., Alkan, C., Cicek, A. E.. 2022-11-17. ECOLE: Learning to call copy number variants on whole exome sequencing data. https://doi.org/10.1101/2022.11.17.516880

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