Abstract

Many investigations of human disease require model systems such as non-human primates and their associated genome analyses. While DeepVariant excels in calling human genetic variations, its reliance on calibrating against known variants from previous population studies poses challenges for non-human species. To address this limitation, we introduce the Genome Variant Refinement Pipeline (GVRP), employing a machine learning-based approach to refine variant calls in non-human species. Rather than training separate variant callers for each species, we employ a machine learning model to accurately identify variations and filter out false positives from DeepVariant. In GVRP, we omit certain DeepVariant preprocessing steps and leverage the ground-truth Genome In A Bottle (GIAB) variant calls to train the machine learning model for non-human species genome variant refinement. We anticipate that GVRP will significantly expedite genome variation studies for non-human species,.