One of the biggest scientific achievements of the twenty-first century was the completion of the Human Genome Project and the publication of a draft human genome. The project took over 13 years to complete and remains one of the largest private-public international collaborations ever. Advances since in sequencing technologies, computational hardware, and novel algorithms reduced the time it takes to produce a human genome assembly to only a few days, at a fraction of the cost. This made using the human genome draft for precision and personalized medicine more achievable. In this blog, we demonstrate how to do a genome assembly in the cloud in a cost-efficient manner using ARM-based AWS Graviton2 instances.
A generalized approach to benchmarking genomics workloads in the cloud: Running the BWA read aligner on Graviton2
The AWS Cloud gives genomics researchers access to a wide variety of instance types and chip architectures and this elasticity allows us to rethink genomics workflows when running workloads in the cloud. Given the increased performance of the Graviton2 instances, we wanted to explore if they can be used for cost-effective and performant genomics workloads. Read on to learn about our generalized approach for determining the most effective instance type for running genomics workloads in the cloud.