# Enhancing clinical diagnostic analysis with a robust de novo mutation detection tool

> **NIH NIH R01** · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · 2022 · $230,222

## Abstract

PROJECT SUMMARY
This application proposes to supplement software development in our parent grant R01HG012286, entitled “Calypso: a web
software system supporting team-based, longitudinal genomic diagnostic care”. We are developing Calypso to meet
diagnostic analysis needs in clinical settings where a large fraction of patients remain non-diagnostic for an extended period
of time, i.e. undiagnosed disease clinics, neonatal intensive care units, and pediatric subspecialty clinics. Our cloud-based
platform will provide the capacity for long-term storage and periodic automated reanalysis of the patient’s genomic data; a
suite of intuitive IOBIO webtools will enable diagnostic analysis; and a case-focused communication and collaboration
interface will coordinate diagnostic teamwork. However, even the best-orchestrated diagnostic variant analysis process
cannot succeed if the disease-causing variant remains undetected. Whereas established computational pipelines exist for
highly accurate and sensitive detection of inherited variations, current tools still underperform for detecting de novo disease-
causing mutations, especially structural variant events. To address this bottleneck, we have developed a kmer-based
mutation detection software tool, RUFUS, and demonstrated its ability to substantially improve the detection of causative
DNMs in a variety of diseases. In accordance with the aims of funding opportunity NOT-OD-22-068 “Enhancing Software
Tools for Open Science and the Cloud”, here we propose to enhance the impact of the currently research-grade RUFUS tool
by improving its implementation and cloud-readiness to accelerate its adoption by the broader genomic medicine
community. First, we will re-engineer the core RUFUS code base to produce a robust, production-ready, and easily
maintainable software package, without altering its already effective algorithmic behavior. We will replace RUFUS’s
currently ad hoc input/output handling with the de facto community standard HTSlib library; restructure logging to produce
informative runtime messages; and implement automated code testing (both unit and integration testing) to ease future
development. Second, we will enable cloud-native adoption of the RUFUS package which was originally designed to
operate in a Linux environment. We will improve scalability by adapting RUFUS for distributed computing, and thereby
achieving a higher level of parallelization and execution speed than possible with the current, multi-threaded,
implementation; and institute containerization to enable RUFUS’s incorporation into cloud-native runtime environments
and workflow language-base pipelines. Finally, third, we will enhance user and developer community engagement, by
adopting standard versioning practices to provide the prerequisite software provenance for incorporation into clinical
diagnostic pipelines; enrolling our software into standard container registry services so users can easily find our tool; and
expanding current...

## Key facts

- **NIH application ID:** 10608743
- **Project number:** 3R01HG012286-01S1
- **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:** 2022
- **Award amount:** $230,222
- **Award type:** 3
- **Project period:** 2022-02-01 → 2023-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10608743

## Citation

> US National Institutes of Health, RePORTER application 10608743, Enhancing clinical diagnostic analysis with a robust de novo mutation detection tool (3R01HG012286-01S1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10608743. Licensed CC0.

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