By Scott Megill and Dan Pelino
The genomics revolution people talked about 10 years ago is not only happening now, it’s just the tip of the iceberg.
Sequencing the human genome has become an increasingly faster and cheaper task. While simplification of this process is welcome, it also creates some challenges regarding delivery and analysis of sequencing data — which can be solved in the cloud.
The genomic data generated from sequencing machines doesn’t amount to much more than alphabet soup if it’s not subjected to significant computational processing and statistical analytics. For the data to be useful, the challenge is to turn those A’s, T’s, G’s, and C’s into a manageable description of disease risks and other genetic predispositions.
This data crunching requires a lot of computational power and time — already a significant bottleneck for some in the industry. But technological advancements have greatly simplified the process of sequencing genomic data at speeds that are orders of magnitude faster than previous processes.
New “big DNA data” genome interpretation companies are looking to the cloud to provide a platform for hospitals, smaller labs and other organizations that don’t have their own computing infrastructure to support genomic research to apply analytics and help them make sense of the data to arrive at some actionable medical information.
Nowadays, sequence processing can be completed in 24 hours for $1,000, generating an exponential growth in genomic data, but introducing storage and delivery challenges.
Some genome centers are set up to deal with such gargantuan files. But most academic laboratories have no large central computing pool or the data storage capacity to handle this amount of data. They are more likely to generate data in an ad hoc manner, rather than in a steady stream that is amenable to an automated data management pipeline.
As the cloud is now considered the assumed infrastructure for taking genomic analysis into the mainstream, we are seeing the possibilities of using genomic data to cure cancer, diabetes and heart disease, or in other words, revolutionize the ways doctors diagnose and treat diseases.
The cloud approach is likely to be particularly valuable for smaller laboratories that lack the software development resources and provide the opportunity to adopt a powerful and rapidly-advancing technology that would otherwise remain out of reach. As a result, this will accelerate the course for genomic research to ultimately impact the delivery of patient care.
But there’s a larger benefit to moving large research data sets onto the cloud. We are in a progression where data that was once only accessible to a select few, is available to a greater number. This expanded accessibility enables more collaboration from a wider variety of sources, which will create more complex maps of how genes interact with each other and their environment and get us closer to solving the mysteries of the human body.
The opportunities surrounding health-related data have only just begun.