# Transdisciplinary Predoctoral Training in Biomedical Science and Engineering

> **NIH NIH T32** · UNIVERSITY OF MAINE ORONO · 2024 · $70,699

## Abstract

ABSTRACT: Cloud-Based Learning Modules for Biomedical Data Science
 PARENT AWARD T32GM132006 (PI: Henry, Clarissa)
The current T32 award supports an innovative, evidence-based training program that includes a co-mentorship
framework and transdisciplinary research opportunities. Bioinformatics and biomedical data science are
fundamental to biomedical research and our transdisciplinary research training program. The proposed
supplement will facilitate bioinformatics and data science research training in our T32 program by creating
reusable training materials for analysis workflows using Cloud-based computing platforms. Cloud-based
computing and data storage resources provide opportunities to broaden the application of bioinformatics and
data science in biomedical research. Our faculty created one of 12 training modules that are featured in the
National Institute of General Medical Sciences (NIGMS) Cloud Learning Modules. This module guides users
on the analysis of prokaryotic bulk RNA-Seq workflows and features four submodules. It was created in
collaboration with the Maine Institutional Development Award (IDeA) Networks of Biomedical Research
Excellence (INBRE) program. The module has been and is currently used in one of our graduate bioinformatics
courses for the T32 program. The module is also used in two undergraduate courses at the University of
Maine. Among the 12 NIGMS Cloud Learning Modules, it is the most highly used as measured by the number
of times the repository has been cloned from GitHub. Given the success of this training module, we propose to
create three new modules, and expand the scope of the current bulk RNA-Seq module. The new modules will
demonstrate workflows for: 1) analyzing the expression of mature microRNAs using small RNA-Seq data from
mouse, zebrafish and C. elegans; 2) analyzing single cell RNA-Seq data from mouse and zebrafish; and 3)
modeling transcription factor networks from RNA-Seq data from human and mouse. The current bulk RNA-Seq
module will be expanded to demonstrate the analysis of mouse, zebrafish and C. elegans data. These
resources will be utilized by our T32 trainees in courses that provide instruction for running sophisticated tools
with increasingly large data sets for a variety of analysis workflows. Importantly, the modules feature datasets
from the same model organisms used by our T32 trainees. These example datasets can be used as templates
for analyses of data from their thesis research projects. The proposed project will also broaden the scope of
the existing NIGMS Cloud Learning Modules and be made available to other T32, T34, INBRE, NARCH and
R25 trainees, and the broader scientific community.

## Key facts

- **NIH application ID:** 11035659
- **Project number:** 3T32GM132006-05S1
- **Recipient organization:** UNIVERSITY OF MAINE ORONO
- **Principal Investigator:** Clarissa A Henry
- **Activity code:** T32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $70,699
- **Award type:** 3
- **Project period:** 2019-07-01 → 2024-10-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11035659

## Citation

> US National Institutes of Health, RePORTER application 11035659, Transdisciplinary Predoctoral Training in Biomedical Science and Engineering (3T32GM132006-05S1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/11035659. Licensed CC0.

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