# IOBIO: Web-based, interactive tools for real-time analysis in genomic big data

> **NIH NIH R01** · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · 2020 · $645,743

## Abstract

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 (http://iobio.io), 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
(http://iobio.io) 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 experti...

## Key facts

- **NIH application ID:** 9934875
- **Project number:** 5R01HG009000-04
- **Recipient organization:** UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH
- **Principal Investigator:** Gabor T Marth
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $645,743
- **Award type:** 5
- **Project period:** 2017-08-01 → 2022-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9934875

## Citation

> US National Institutes of Health, RePORTER application 9934875, IOBIO: Web-based, interactive tools for real-time analysis in genomic big data (5R01HG009000-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9934875. Licensed CC0.

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