DDN is changing how life science data is stored, shared, and analyzed. Our genomics research customers have documented massive throughput increases for their analysis pipelines with our simple, cost-effective way of scaling capacity and performance as data sets grow.

As the leading supplier of high-performance data-storage systems to the genomics research community, our platforms reduce the time to discovery by accelerating processes while mitigating day-to-day risks associated with the ingest, distribution, and analysis of large amounts of research data. Linking this crucial genetic data with clinical information empowers scientists to understand, diagnose, and treat a wide variety of cancers and genetic disorders much better.

For example, the University of Edinburgh’s Edinburgh Parallel Computing Centre turned to DDN when they needed an IT infrastructure that could meet the data ingest, processing, and storage challenges of a production genome sequencing center. The system, which was installed at the Edinburgh Genomics facility’s Roslin Institute, was configured with 800TB to support six Illumina HiSeq X sequencers for whole genome sequencing.

The level of automation and simple management provided by DDN helps Edinburgh Genomics to run a major 24/7 genome production facility with only two full-time staff members. Data generated from the sequencers goes directly to the DDN ES7K, where it is then processed by standard pipelines, including reference mapping and variant calling, dealing with 160 whole genomes per week, representing more than 30TB of read data.

The quick deployment of DDN’s system was appreciated by Edinburgh. “Instead of taking months to deploy, we were up and running in weeks. It was the fastest thing I’ve ever seen.”

High reliability and high throughput from the DDN systems enabled Edinburgh Genomics to achieve high utilization of their six Illumina HiSeq X sequencers, which was very important to them as a production sequencing center where they do not want expensive instruments sitting idle.

To read the full University of Edinburgh case study, click here. You can also learn more by visiting us at BioIT World or by attending our Best Practices in Big Data for Life Sciences Research Workshop on May 23, 2017.

  • George Vacek