google/deepvariant

Health v2 · maintenance only

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3818 stars · 805 forks observed · 2026-10-15

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

DeepVariant is a deep learning-based genomic variant caller that converts aligned DNA sequencing reads (BAM/CRAM) into pileup image tensors and classifies them with a convolutional neural network to produce VCF/gVCF outputs. It supports germline variant calling in diploid organisms across multiple sequencing platforms including Illumina, PacBio HiFi, Oxford Nanopore, and Complete Genomics.

Use cases

• call genetic variants from whole genome sequencing data
• identify SNPs and indels from PacBio HiFi long reads
• variant calling on Oxford Nanopore sequencing data
• analyze whole exome sequencing for germline variants
• run trio-based variant calling with DeepTrio
• process RNA-seq data for variant detection
• perform pangenome-aware variant calling

When to choose

• you need highly accurate germline variant calling from NGS or long-read sequencing data
• you want a deep learning approach that outperforms traditional statistical variant callers
• you work with human diploid genomes across multiple sequencing platforms
• you need reproducible, benchmark-proven variant calling (GIAB winner)

When to avoid

• you need somatic (cancer) variant calling - use DeepSomatic instead
• your organism is not diploid or has complex copy-number variation
• you lack computational resources for CNN inference on large genomes
• you need a lightweight statistical caller for quick exploratory analysis

Facets

application · maturity stable

machine-learning deep-learning data-science bioinformatics machine-learning deep-learning python variant-calling genomics dna-sequencing bioinformatics cnn tensorflow vcf ngs linux docker gpu

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For agents

markdown · JSON · MCP: product_card(name="google/deepvariant")