Genomic analyses have the potential to revolutionize the way inherited disease, infectious disease, and cancer are diagnosed and treated. However, existing genomic analysis tools have been optimized for processing whole genome or exome datasets from end to end, requiring bioinformatics training, expensive computer hardware and data storage, rendering them unavailable to many biomedical researchers who don't have access to such resources. These tools take hours or days to complete analysis, and produce large, static output files that require considerable expertise to interpret. This exhaustive ?top-down? approach does not provide individual researchers with the means to quickly examine and troubleshoot their datasets, or test hypotheses formulated at the bench or in the clinic. Thus, existing genomic analysis tools do not adequately reach the end users, e.g. research clinicians who need them most to affect major advances in genomic medicine, but because of their clinical duties have the least amount and most fragmented time for their research. We are developing iobio (, a novel genomic analysis system that will enable biomedical professionals without computational resources to access, and interactively analyze biomedical big data at the genome scale, using only a laptop computer. Instead of analyzing complete genomic datasets end to end, each iobio app performs focused genomic analyses (e.g. in the region of a gene) and returns the results in seconds. Results are displayed visually using a sophisticated and intuitive web interface, allowing scientists to quickly process their data using web server versions of the same powerful UNIX tools used in end- to-end genomic analyses but without the need for computing hardware and tool installation, visualize their results, expand or refine and immediately repeat to customize their analysis strategy. The iobio toolkit ( currently includes four full-featured web apps, already facilitating sophisticated inherited variant prioritization and metagenomic analyses. Our growing user base is already in the thousands, many of them returning ?customers? who have incorporated our apps into their analysis routine. Here, we propose to vastly expand our existing tool chest for supporting cancer genomic investigation. Cancer genomes are highly variable from patient to patient, requiring customizable analyses, tasks ideally suited for our interactive iobio web tools. Realizing that our team alone will not be able to develop and maintain tools for every task in every subdomain of genomics research, we will build extensive software libraries to support iobio app development by third-party developer groups. We will also develop flexible options for operating our tools efficiently and securely, on local server hardware or in computational cloud environments. iobio will grow into a rich and vibrant analysis ecosystem that will empower biomedical researchers at all levels of bioinformatics expertise, computationally skilled researchers and bench scientists, to carry out intuitive data analyses that are difficult to accomplish with existing genomic analysis tools.

Public Health Relevance

We are developing web-accessible software that will allow biomedical scientists to analyze genetic data more easily, intuitively, and without huge computer resources. These tools will accelerate genetic discovery by enabling all researchers to access and analyze vast biological datasets.

National Institute of Health (NIH)
National Human Genome Research Institute (NHGRI)
Research Project (R01)
Project #
Application #
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Sofia, Heidi J
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Utah
Schools of Medicine
Salt Lake City
United States
Zip Code
Ward, Alistair; Karren, Mary A; Di Sera, Tonya et al. (2017) Rapid clinical diagnostic variant investigation of genomic patient sequencing data with iobio web tools. J Clin Transl Sci 1:381-386