Exome sequencing to identify germline variation is a key tool for the diagnosis of genetic disease, population genome studies, and as a component of tumor-normal sequencing protocols used in precision oncology.
Traditional variant detection methods rely upon manually tuned, parameterized statistical models to achieve high accuracy. Recently, this paradigm has been challenged by DeepVariant, a method leveraging deep convolutional neural networks trained upon read pileup images to identify variants. DeepVariant models have been trained to achieve high accuracy with diverse sequence data types.
This poster from Singular Genomics presents a highly performant DeepVariant model optimized for germline exome analysis on the novel G4 Sequencing Platform.
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