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Neural network based systems designed to call SNPs and indels in genomic data.
Distinct from Deep Learning: Specific application of deep learning to genotyping, not general frameworks or attribution tools
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DeepVariant is a deep learning genotyping tool and DNA sequence analysis pipeline used to identify single nucleotide polymorphisms and indels from next-generation sequencing data. It functions as a convolutional neural network genetic variant caller that treats genomic read alignments as multi-channel image tensors to determine genotypes. The system supports specialized analysis workflows including long-read variant calling for circular consensus sequencing and trio-based variant calling to identify inherited or de novo mutations. It enables model optimization for new species or genome contex
Provides a neural network based system for calling single nucleotide polymorphisms and insertions or deletions in genomic data.