by Seth Feder

Genomics is no longer solely the domain of university research labs and clinical trials. Commercial entities such as tertiary care hospitals, cancer centers, and large diagnostics labs are now sequencing genomes. Perhaps ahead of the science, consumers are seeing direct marketing messages about genomic tumor assessments on TV.  Not surprising, venture capitalists are looking for their slice of the pie, last year investing approximately $248 million in personalized medicine startups. 

So how can health IT professionals get involved? As in the past, technology coupled with innovation (and the right use-case) can drive new initiatives to widespread adoption. In this case, genomic medicine has the right use-case and IT innovation is driving adoption.   

While the actual DNA and RNA sequencing takes place inside very sophisticated instrumentation, sequencing is just one step in the process. The raw data has to be processed, analyzed, interpreted, reported, shared, and then stored for later use.  Sound familiar?  It should, because we have seen this before in such fields as digital imaging which drove the wide spread deployment of Picture Archiving and Communicating Systems (PACS) in just about every hospital and imaging clinic around the world.  

As in PACS, those in clinical IT must implement, operationalize, and support the workflow. The processing and analysis of genomic data is essentially a big data problem, solved by immense amounts of computing power.  In the past, these resources were housed inside large exotic supercomputers only available to elite institutions. But today HPC built on scale-out x86 architectures with multi core processors have made this power attainable to the masses – and thus democratized.  Parallel file systems that support HPC are much easier to implement and support, as are standard high bandwidth InfiniBand and Ethernet networks. Further, public cloud is emerging as a supplement to on-premise computing power.  Some organizations are exploring off-loading part of the work beyond their own firewall, either for added compute resources or as a location for long term data storage.

For example, in 2012 myself and others at Dell worked with the Translational Genomics Research Institute (TGen) to tune its system for genomics input/output demands by scaling its existing HPC cluster to include more servers, storage and networking bandwidth. This allowed researchers to get the IT resources they needed faster without having to depend on shared systems. TGen worked with the Neuroblastoma and Medulloblastoma Translational Research Consortium (NMTRC) to develop methodology for fast sequencing of childhood cancer tumors, allowing NMTRC doctors to quickly identify appropriate treatments for young patients. 

You can now get pre-configured HPCs to work with genomic software toolsets, which enabled clinical and translational research centers like TGen to do large-scale sequencing projects. The ROI and price per performance is compelling for anyone doing heavy genomic workloads.  Essentially, with one rack of gear, any clinical lab now has all the compute power needed to process and analyze multiple genome sequences per day, which is a clinically relevant pace. 

Genomic medicine is here, and within a few years will become standard care to sequence many diseases in order to determine proper treatment.  As the science advances, the HPC community will be ready contribute in making this a reality. You can learn more here.