Cancer is a genetic disease driven by mutations in cell division control. Identifying these mutations is crucial for understanding and treating cancer effectively. Researchers developed DeepSomatic, a machine learning tool, to accurately identify somatic variants in tumor cells. DeepSomatic utilizes convolutional neural networks and works across various sequencing platforms and sample types. The tool and its training dataset are openly available to the research community for broader use. The development of DeepSomatic involved creating a comprehensive dataset, CASTLE, from sequenced breast and lung cancer samples. DeepSomatic outperforms existing methods in identifying tumor variants, especially insertions and deletions. The tool demonstrates the ability to generalize its learning on different cancer types such as glioblastoma and pediatric leukemia. This tool can potentially help tailor existing treatments or lead to the development of novel therapies. DeepSomatic can analyze lower-quality or historical tumor samples, and even work with tumor-only samples. This advancement is a step towards precision medicine, aiming to deliver the most effective treatments for patients.