Abstract
Active mobile elements in the human genome can create novel mobile element insertions (MEIs) in somatic tissues. Detection of somatic MEIs, particularly those with low mosaicism, remains a significant challenge due to sequencing artifacts and alignment errors. Existing methods lack sensitivity or require biased manual inspection. Here we present RetroNet, a deep learning algorithm that encodes sequencing reads into images to identify somatic MEIs with as few as two reads. Trained on diverse datasets, RetroNet outperforms previous methods and eliminates the need for manual examinations. RetroNet achieves high precision (0.885) and recall (0.579) on a cancer cell line, detecting insertions in just 1.79% of cells. RetroNet is also effective for degraded DNA, like circulating tumor DNA. This tool is applicable to the rapidly generated short-read sequencing data and has the potential to provide further insights into the functional and pathological implications of somatic retrotranspositions.