GVRP: Genome Variant Refinement Pipeline for variant analysis in non-human species using machine learning

Choi, J.; Zhou, B.; Song, G.

doi:10.1101/2024.01.14.575595

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bioRxiv · 10.1101/2024.01.14.575595

GVRP: Genome Variant Refinement Pipeline for variant analysis in non-human species using machine learning

Choi, J.·Zhou, B.·Song, G.

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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.

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Choi, J., Zhou, B., Song, G.. 2024-01-15. GVRP: Genome Variant Refinement Pipeline for variant analysis in non-human species using machine learning. https://doi.org/10.1101/2024.01.14.575595

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