Darren C. Ames

TL;DR: US genome centers and NIH programs define WGS data processing standards and a flexible validation method, facilitating collaboration in human genetics research.

TL;DR: Analysis commons, a team approach to discovery in a big-data environment for genetic epidemiology, shows the potential of big data to improve the quality of research in the rapidly changing environment.

TL;DR: This study accomplished integration of structured genomic results into multiple electronic health record (EHR) systems, setting the stage for clinical decision support to enable genomic medicine.

TL;DR: This work surveyed the capabilities and needs of different network participants, established a common transfer format, and implemented transfer mechanisms based on this format that serve as a case example for both standards bodies and organizations working to build the infrastructure required to provide better electronic clinical decision support for clinicians.

TL;DR: WGS data processing standards are defined that allow different groups to produce “functionally equivalent” results suitable for joint variant calling with minimal batch effects, which alleviates a key technical bottleneck for genome aggregation and helps lay the foundation for broad data sharing and community-wide “big-data” human genetics studies.