Creating the next generation of genome analysis tools with deep learning

Deep learning is fueling a revolution in genomics, enabling the development of a new generation of analysis tools that offer unprecedented accuracy. This talk presents a suite of deep learning models designed to address fundamental challenges in variant calling and generating high-quality genome assemblies. We begin with DeepVariant, a convolutional neural network that redefined the standard for germline variant calling, and its extension, DeepSomatic, which adapts this technology to the critical task of identifying low-frequency somatic mutations in cancer genomes. Moving from variant analysis to genome construction, we introduce DeepPolisher. This tool leverages a powerful Transformer-based architecture to significantly reduce errors in genome assemblies, providing a more accurate and reliable foundation for downstream research. Finally, we explore the future of variant calling by integrating these methods with emerging pangenome references. We demonstrate how a pangenome-aware approach allows for a more comprehensive survey of human genetic diversity, resolving variation in previously intractable regions of the genome. Together, these tools represent a cohesive framework that is building the next generation of genomic analysis, transforming our ability to accurately read and interpret the code of life.

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