Normalized claim
Quantified impact: 10 seconds
Returned variant-analysis query results in less than 10 seconds even with 500 genomes and millions of variants.
Stanford Center for Genomics and Personalized Medicine: Building a mega-scale genetic variation analysis pipeline
Use case typePrecision medicineUpdated Jun 13, 2026
Stanford Center for Genomics and Personalized Medicine (SCGPM) at Stanford University built a mega-scale genetic variation analysis pipeline on Google Cloud Platform using Google Genomics and BigQuery to analyze large DNA sequencing datasets faster than on-premises clusters and enable secure sharing of genomic data. The team processed hundreds of whole genomes for the Million Veteran Program pilot and established security best practices for storing and sharing genomic data in the cloud.
- Industry
- Healthcare
- Location
- United States
- Published
- June 2016
Reported outcomes
10 seconds
quantified impactOther quantified impact
Strategic outcomes
New product / capabilityBuilt mega-scale variant analysis pipelineRisk & complianceEstablished secure genomic data sharing practicesScale & capacityExpanded access to cloud genomics resourcesBetter decisions & insightEnabled rapid variant-analysis queries
Why do we believe this?Outcome claims, sources, and evidence checks
Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
- Customer
- Stanford Center for Genomics and Personalized Medicine, Stanford University
- Provider
- GCP
- Maturity
- Pilot
- Linked source
- Google Cloud Customer Story
The team processed hundreds of whole genomes for the Million Veteran Program pilot and established security best practices for storing and sharing genomic data in the cloud
Customer identity supportedSource describes one deploymentMaturity supported
Primary read
Use case focus
Showing 2 of 2
- 1Genomics Analytics
- 2Data Platform Modernization
- Process and analyze extremely large DNA sequencing datasets faster than on-premises clusters.
- Enable secure storage and sharing of sensitive genomic data.
- Scale variant analysis to hundreds of genomes without long local-cluster turnaround times.
- Built a genetic variation, or variant, analysis pipeline on Google Cloud Platform using Google Genomics.
- Used Google BigQuery to mine variants and compare them against publicly accessible databases.
- Worked with the Google Genomics team and implemented security best practices such as encryption, user permissions, and limiting data access.
- Processed 500 genomes from raw data to variant calls for the Million Veteran Program pilot data in days.
- Returned variant-analysis query results in less than 10 seconds even with 500 genomes and millions of variants.
- Established security best practices to help genomics labs confidently store and share data in the cloud.
- Expanded access to cloud resources for Stanford genomics researchers, with over a petabyte of genomic data stored and analyzed on Google Cloud Platform.
Architecture
SCGPM built a genetic variation analysis pipeline on Google Cloud Platform with Google Genomics to process large DNA sequencing datasets, using BigQuery for fast variant queries and cloud-based security controls for data protection and sharing.
Implementation partners1
Sources & evidence1
Evidence: Medium65/100Evidence strength
- Customer explicitly identified
- Deployment status explicitly supported
- Primary source available
- Quantified outcome available
- Technical implementation details available
Type: Customer StoryPublished: Jun 1, 2016Publisher: Google CloudEvidence: VendorConfidence: Medium
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