Thursday, November 21, 2024
11/21/2024
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.
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