University of Virginia School of Medicine researchers identified a widespread source of error in a popular genomics method and built a machine-learning tool to correct it. The free method is designed to improve the reliability of both conventional and single-cell datasets generated using the approach. The work aims to produce clearer readouts of gene activity regulation in health and disease, strengthening downstream use in diagnostic development and drug discovery workflows. By addressing an accuracy bottleneck, the tool is positioned as a foundational improvement for researchers relying on the underlying experimental method. The release reflects a continued focus in genomics on quality control, reproducibility, and computational correction layers that can materially affect interpretation across studies.