PROJECT SUMMARY Whole exome sequencing (WES) and whole genome sequencing (WGS) have increasingly been used to identify variants, genes and regulatory regions that are associated with human diseases. As a result, we are witnessing a tsunami of DNA sequence data from both healthy human subjects and those with Mendelian or complex diseases. Identifying variants that are causal of a disease or associated with disease risks from a huge amount of DNA variants identified in sequencing is like looking for a needle in a hay stack. To accomplish this daunting task, investigators have relied on functional annotation to filter or prioritize variants based on our current knowledge or prediction models. We previously developed the dbNSFP database with deleteriousness prediction scores of all possible missense mutations in humans, as well as the WGS annotator (WGSA) software to facilitate functional annotation for both coding and non-coding variants which current contains > 1.5 Tb (compressed) resource data. These software tools are widely used by worldwide investigators. In the proposed study, to extend and harden our functional annotation tools and resources for handling the rapidly-increasing amount of WES and WGS data. Specifically, we will extend and improve the functional annotation resources of dbNSFP and WGSA, and improve the speed, user-interface and dissemination approach of dbNSFP and WGSA. Successful completion of these aims will accelerate the progress to study newly discovered variants for their involvement in human disease in the era of big data and precision medicine. This contribution will also benefit the human genomics and human biomedical sciences in general because DNA sequence analyses have become the essential approach in those areas and DNA variant functional annotation will certainly help us to understand and interpret the functions of the variants.

NARRATIVE We will significantly extend, improve and harden our functional annotation resources and tools for WES and WGS in response to the increasing big data challenge faced by medical geneticists. Successful completion of these aims will enable human geneticists to fully investigate the functional contents of the massive amounts of sequencing data.