DNAnexus, Baylor Partner on Large-Scale Genomic Analysis

(NIST.gov)

Genomics and bioinformatics researchers at Baylor College of Medicine in Houston and analytical services company DNAnexus in Mountain View, California are collaborating on large-scale genomic sequencing for research and clinical applications, with a cloud computing platform. The partnership already processed genomic data from more than 14,000 individuals for a genetics analysis on heart disease and aging.

DNAnexus offers a cloud-based computing platform that applies bioinformatics to computational biology and genetics. The company provides genomic analysis services for sequencing providers, diagnostic test companies, and research labs.

In this collaboration, DNAnexus is working with Baylor’s Human Genome Sequencing Center, one of three large-scale sequencing facilities supported by NIH; the others are at Broad Institute in Cambridge, Massachusetts and Washington University in St. Louis. Baylor’s sequencing center offers a set of software called the Mercury pipeline that identifies mutations and analyzes gene sequencing variations, and runs that software with DNAnexus cloud computing platform.

The need for a cloud computing power became evident in a project Baylor’s sequencing center undertook for the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium, an initiative to better understand the connection between genetics and heart disease, as well as other conditions related to aging. CHARGE combines five major longitudinal studies representing some 300 researchers in the U.S. and Europe.

Baylor’s researchers discovered early on the scope and size of the effort would outstrip the computing capacities of individual institutions. As a result, they called on DNAnexus to provide computing and analytical capacity through the cloud, including the resources of Amazon Web Services that provides cloud-based computing services on demand.

DNAnexus says the project became the largest genomic analysis attempted with cloud computing. The company analyzed 3,751 whole genomes and 10,771 exomes — small subsets of whole genomes, but representing the vast majority of disease-causing mutations. The work required some 860 terabytes of storage, generating 430 terabytes of results.

Read more:

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• Faster Whole Genome Sequencing Developed for Infants
• Techniques Developed for Deeper Single-Cell Gene Sequencing

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