Stage 1: GPU Genomics¶
From raw sequencing data to millions of variants
120 – 240 minutes on DGX Spark
What This Stage Does¶
When a patient's DNA is sequenced, the machine produces raw data — billions of short DNA fragments stored in FASTQ files, typically around 200 GB per patient.
Stage 1 transforms this raw data into actionable genetic information:
-
Alignment — Each DNA fragment is mapped back to its position on the human reference genome (like assembling a 3-billion-piece puzzle)
-
Variant Calling — The pipeline identifies where this patient's DNA differs from the reference — these differences are called variants
-
Quality Filtering — AI-powered models (DeepVariant) distinguish real variants from sequencing errors with >99% accuracy
By the Numbers¶
| Metric | Value |
|---|---|
| Input size | ~200 GB FASTQ |
| Reads aligned | 800M – 1.2B |
| Variants discovered | 11.7 million |
| High-quality variants | 3.5 million |
| Accuracy | >99% (DeepVariant) |
| Runtime | 120 – 240 minutes |
The Speed Advantage¶
| Step | Traditional (CPU) | HCLS AI Factory (GPU) |
|---|---|---|
| Alignment (BWA-MEM2) | 12 – 24 hours | 1 – 2 hours |
| Variant Calling | 8 – 12 hours | 1 – 2 hours |
| Total | 1 – 2 days | 2 – 4 hours |
GPU acceleration via NVIDIA Parabricks delivers 10–50x speedup over traditional CPU pipelines.
Technology Stack¶
How It Works¶
┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│ FASTQ │ ──▶ │ Alignment │ ──▶ │ Variant │ ──▶ │ VCF │
│ (200 GB) │ │ (BWA-MEM2) │ │ Calling │ │ (11.7M) │
└─────────────┘ └─────────────┘ └─────────────┘ └─────────────┘
│ │ │ │
│ │ │ │
Raw reads Mapped BAM DeepVariant Variants
from sequencer coordinates AI model ready for
Stage 2
Output¶
The stage produces a VCF file (Variant Call Format) containing:
- Chromosome position of each variant
- Reference allele (what the reference genome has)
- Alternate allele (what the patient has)
- Quality scores (confidence in the call)
- Genotype (heterozygous or homozygous)
This VCF file becomes the input for Stage 2: Evidence RAG, where we determine which variants actually matter.
Why GPU Acceleration Matters¶
Traditional genomics pipelines run on CPU clusters and take 1–2 days per patient. For a hospital processing hundreds of patients, this creates bottlenecks.
GPU acceleration changes the equation:
- Same accuracy — FDA-cleared Parabricks matches CPU results
- 10x faster — Hours instead of days
- Lower cost — One DGX Spark vs. a CPU cluster
- Real-time capability — Results while the patient is still in clinic