In 2000 the Human Genome Project released the "rough draft" of the human genome, the first major milestone of a $3 billion project requiring more than a decade of work by researchers in seven countries. Barely a decade later, continuing advancements in genome sequencing technologies have made it possible to essentially replicate the entirety of the Human Genome Project project in a matter of days or weeks at less than one millionth of the cost. As a result, massive volumes of genomic data are being produced, the storage and analysis of which represents the primary challenge in computational genomics today.

The major focus of my work is to apply recent innovations in high performance computing – particularly distributed computing – to the challenges presented by working with the massive datasets produced by full genome sequencing. The initial steps have been the creation of the Jnomics, a Java-based API based on Google's MapReduce framework, that allows rapid development of parallelized genomic analysis pipelines. Subsequent steps include applying Jnomics to computationally challenging or time-consuming tasks, including the identification of structural variations and copy-number variations.

As of July 2011, Jnomics is in late stage development; it is targetted for release as an open-source project near the end of the summer.