Case Study: Stanford University’s Center for Genomics and Personalized Medicine achieves mega-scale genome analysis in days and sub-10-second variant queries with Google Cloud Platform

Stanford Center for Genomics and Personalized Medicine: Building a mega-scale genetic variation analysis pipeline

The Stanford Center for Genomics and Personalized Medicine (SCGPM) needed to scale analysis of massive next‑generation sequencing datasets—work that was bottlenecked by limited on‑premises compute and strict DNA‑data security requirements as part of large studies like the Million Veteran Program. Researchers required a way to process hundreds to thousands of genomes in parallel and get interactive query performance without compromising data protection.

Working with Google Genomics and BigQuery on Google Cloud Platform, SCGPM implemented a cloud variant‑analysis pipeline that processed 500 genomes from raw data to variant calls in days and returns queries in under 10 seconds, even across millions of variants. The team also established published security best practices, made the protocol open source, and now stores and analyzes over a petabyte of genomic data—enabling far faster, scalable, and secure genomics research.

Stanford University’s Center of Genomics and Personalized Medicine

Somalee Datta

Bioinformatics Director