SBIR Phase I: Whole Genome Sequencing Data to Insight in One Hour

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) project will be to provide deep insights into the DNA of patients in one hour at one-fourth the cost. This will allow hospitals, clinics and research centers to delve faster into the genetic information of the patients and return essential insights to physicians, leading to faster decisions on therapy. Analyzing DNA data holds the promise of detecting several diseases and can also help in pinpointing their genetic origins, which will be key for treatment of vulnerable cases such as newborn babies, people with rare diseases, and pregnant women. By providing the analysis of whole DNA data in one hour as compared to several days, DNA tests can become mainstream, thereby reducing anxiety among patients and their relatives. As the number of patients for which deep DNA analysis will be required is doubling every year, this project aims to meet the exploding demands of large scale computational genomics of the future and enable deep DNA analysis for all patients. This SBIR Phase I project proposes to use the power of state of the art cloud computing platforms to provide analysis for Whole Genome Sequencing (WGS) data in one hour. Several key researchers have shown that data from WGS is a critical requirement for accurate insights and detailed analysis of underlying diseases for various diseases including leukemia, breast Cancer, ADHD, Alzheimer's, congenital heart disease, HIV susceptibility, as well as others as information in the non-coding region is required. However, the computational analysis for WGS data takes several days and will be the major bottleneck for utilizing key WGS data to personalize the treatment for the affected patient. This project aims to use several high performance computing techniques on the cloud that will be tailored for NGS analyses and can accelerate the process by more than 40 times. This project uses a disruptive technology that breaks algorithms to work independently on nodes on the cloud and the team has created a collection of software optimizations to improve the utilization of cloud resources. This toolbox of optimizations is being applied to commonly used software tools in computational genomics for faster analysis.