There has been a great optimism for the usage of DNA sequence data in clinical practice, notably for diagnostics and developing personalized treatments tailored to an individual’s genome. This poster, presents a study of software tools used in identifying and characterizing mutations in a genome. We present IGen, a runtime framework which executes this workflow as a data-flow graph over a partitioned global address space. Preliminary results on the Blue Waters supercomputer show that IGen is able to accelerate single node performance (alignment - 1.2x, variant calling - 9x), as well as distribute the computation across several machines with near-linear scaling. Theoretical models for performance of the entire workflow suggest that IGen will have a 3x improvement in runtime on a single node with near linear scaling across multiple nodes.