# Web tools for physician-driven diagnostic interpretation of genomic patient data

> **NIH NIH R01** · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · 2020 · $685,451

## Abstract

SUMMARY/ABSTRACT
Genomic sequencing provides definitive disease diagnoses for many patients with suspected genetic disease,
ending or preventing lengthy and costly diagnostic odysseys. However, despite extensive efforts of research
clinicians and all current computational analysis technologies, the genetic cause of disease remains
unresolved for over half of the sequenced patients in genetics clinics today. All too often, diagnosis from whole-
exome or genome sequencing data remains elusive even for patients suffering from diseases with well-
understood clinical presentation and genetic architecture. Although diagnostic failure can have multiple causes,
we hypothesize that two reasons contribute significantly. First, current variant prioritization tools work by
reductive filtering on annotations and inheritance patterns to reduce sets of exonic or genomic variants to
small, prioritized lists of candidates. This approach works when clear causative variants are present, but offers
minimal capacity to remove highly ranked but false positive candidates, and provides little guidance when
causative variants have been missed, typically because of unrecognized data quality problems such as low
sequence coverage or exon dropouts. When the first round of analysis yields no plausible candidates, current
tools don't have the ability to suggest a sensible “next step”, e.g. to deepen or expand the search for causative
variants in the data, and the result is analysis dead-end. Second, because of onerous IT expertise and
bioinformatic skill requirements, physicians currently rely on bioinformatics experts to analyze genomic data.
However, the bioinformatician does not possess the physician's clinical expertise or detailed knowledge of
disease presentation, clinical phenotype, and time course of the disease, all of which can be critical in making
a diagnosis. This gap between clinical and computational expertise hinders diagnostic success and disease
discovery. Here we propose to build a set of web tools that offer novel functionality for deeper, systematic re-
examination of the data for disease-causing variants, but are also intuitive and easy to use so clinicians can
themselves analyze their patients' genomic datasets. These tools will be based on our already popular IOBIO
system available at http://iobio.io, and will offer diagnostic analysts the ability to rapidly examine the quality of
their genomic datasets, and visually and in real time search the patient's data for disease causing variants. A
unique aspect of this development is that it will be physician-driven from the outset: a large team of clinical
Investigators will help design, prioritize, and evaluate software features, and integrate the tools into physician
practice and training, ensuring these tools will be usable by clinicians, and they address the most relevant
analysis steps for successful clinical diagnosis. Our tools and training materials will be made widely available,
drastically lowering...

## Key facts

- **NIH application ID:** 9957182
- **Project number:** 5R01HG009712-04
- **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:** $685,451
- **Award type:** 5
- **Project period:** 2017-09-01 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9957182

## Citation

> US National Institutes of Health, RePORTER application 9957182, Web tools for physician-driven diagnostic interpretation of genomic patient data (5R01HG009712-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9957182. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*