# Expanding Genomic Data Science Access via Cloud Computing and Dynamic Learning Modules

> **NIH NIH UE5** · UNIVERSITY OF TEXAS SAN ANTONIO · 2024 · $474,136

## Abstract

Project Summary
 The advent of high-throughput genomics and associated data science technologies has enabled
genome-based decisions to improve human personal and public health. The advance of genomic medicine relies
on developing a diverse workforce in computational genomics and data science (CGDS). Critical gaps, however,
hamper the expansion of the genomics workforce: (1) a lack of diversity continues despite decades of effort.
Large sectors of the US population remain underrepresented in CGDS; (2) a lack of access to resources and
training opportunities limits student populations at the undergraduate and master’s levels to obtain the knowledge
and skills needed for a career in CGDS.
 The University of Texas at San Antonio (UTSA) is uniquely positioned to address these gaps. Being a
primarily minority-serving institution, with 67% of enrollment from under-represented minority (URM) groups,
UTSA is committed to promoting an inclusive community of learners and narrowing the nationwide gender and
racial gaps in the STEM field. In addition, UTSA has identified biomedical science and data science as
fundamental building blocks for developing its research base. CGDS, a discipline at the interface of biomedical
and data science, is a priority area receiving significant institutional support.
 This proposed UE5 program aims to develop, implement, and evaluate classroom educational content
and cloud-based hands-on analytical exercises in CGDS to serve students from diverse backgrounds, including
those underrepresented in the genomics workforce.
Aim 1. To develop cloud-based instruction materials utilizing existing NIH cloud resources for teaching CGDS
at undergraduate and master’s levels. These materials include lecture slides, video presentations,
demonstrations, hands-on practice problems, assignments, and project ideas, organized into flexible modules
suiting the needs of diverse student backgrounds and learning paths.
Aim 2. To iteratively refine the developed content and modules by teaching them in a hybrid mode across
different departments and collecting feedback from students and faculty.
Aim 3. To evaluate the effectiveness of these materials by conducting formal evaluation and analysis and sharing
with the broader CGDS community at large.

## Key facts

- **NIH application ID:** 10983626
- **Project number:** 1UE5HG013818-01
- **Recipient organization:** UNIVERSITY OF TEXAS SAN ANTONIO
- **Principal Investigator:** Jianhua Ruan
- **Activity code:** UE5 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $474,136
- **Award type:** 1
- **Project period:** 2024-09-05 → 2027-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10983626

## Citation

> US National Institutes of Health, RePORTER application 10983626, Expanding Genomic Data Science Access via Cloud Computing and Dynamic Learning Modules (1UE5HG013818-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10983626. Licensed CC0.

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