ExactCN: Predicting Exact Copy Numbers on Whole Exome Sequencing Data

The quantification of the precise copy number variations (CNVs) is crucial to understandingthe effects of gene dosage, disease severity, and therapeutic response. Although whole-exome sequencing(WES) offers a cost-effective solution for CNV detection in a clinical setting, it introduces several biases,including those related to sequence length, GC content, and the use of targeting probes. Consequently,estimating exact copy numbers remains challenging, especially for WES data. Here, we present Ex-actCN, a deep learning–based method for estimation of exact copy numbers from WES data per exon.The architecture integrates convolutional layers that extract local read-depth patterns with transformerencoder blocks that capture genomic context and handle sequencing noise. ExactCN is trained on WESsamples from the 1000 Genomes Project, using matching WGS-based calls as semi–ground truth. Inbenchmarks, ExactCN improves the state-of-the-art integer CNV calling performance by reducing themacro-averaged mean absolute error (MAE) from 0.91 to 0.62 and the macro-averaged root meansquared error (RMSE) from 1.31 to 0.78. It also achieves an overall Pearson correlation of 0.669 andSpearman correlation of 0.550, improving the second-best method by 0.641 and 0.482, respectively.Furthermore, a fine-tuned version of ExactCN demonstrated an overall F1-score of 0.657 for ag-gregate CNV detection performance on the clinically important duplicated genes SMN1/2, demon-strating its applicability to both research and clinical genomic analyses.