University of Virginia researchers identified a widespread source of error in a popular genomics method and created a free machine-learning tool to correct it. The work, described as improving reliability for both conventional and single-cell data generated using the approach, is intended to produce a clearer picture of gene activity control in health and disease. By improving data accuracy, the tool could strengthen downstream research used to support diagnostics and drug development, the authors said, by reducing the chance that technical artifacts distort biological signals. The publication underscores how rapidly computing-based methods are reshaping genomics workflows—while also highlighting that measurement bias and modeling errors can become limiting factors without explicit correction tools.