Faculty mentor/PI email address
pestovdg@rowan.edu
Keywords
RNA, code, computational, cardiovascular
Date of Presentation
5-6-2026 12:00 AM
Poster Abstract
Background: Ischemia-reperfusion injury (IRI) contributes significantly to tissue damage in cardiovascular disease. During IRI, stressed and dying cells release ribosomal RNA, which exhibits distinct fragmentation patterns that may serve as potential diagnostic biomarkers. Although these patterns have been observed using low-resolution biochemical techniques, there is currently no standardized computational approach to quantify and visualize RNA fragmentation for clinical interpretation.
Hypothesis: A computational pipeline can be developed to visualize and analyze ribosomal RNA fragmentation patterns, enabling identification of distinct fragmentation hotspots associated with ischemic conditions and potential biomarker discovery.
Methods: RNA fragmentation datasets generated from ribosomal RNA experiments in a Saccharomyces cerevisiae yeast model of oxidative stress-induced cell damage were analyzed using R. Comparative analyses between experimental conditions were conducted to identify fragmentation hotspots.
Results: The generated script was used to visualize RNA fragmentation patterns and identify distinct hotspots across ribosomal regions under different experimental conditions. The resulting visualizations effectively highlighted regions of interest and facilitated interpretation of complex RNA sequencing data.
Conclusions: This project developed a computational tool for analyzing RNA fragmentation patterns. This work can be further used to support the identification of potential RNA-based biomarkers for ischemia-reperfusion injury, laying the groundwork for future clinical and translational applications.
Disciplines
Cardiovascular Diseases | Health Information Technology | Medicine and Health Sciences
Developing a Computational Pipeline for Biomarker Discovery in Ischemic Reperfusion Injury
Background: Ischemia-reperfusion injury (IRI) contributes significantly to tissue damage in cardiovascular disease. During IRI, stressed and dying cells release ribosomal RNA, which exhibits distinct fragmentation patterns that may serve as potential diagnostic biomarkers. Although these patterns have been observed using low-resolution biochemical techniques, there is currently no standardized computational approach to quantify and visualize RNA fragmentation for clinical interpretation.
Hypothesis: A computational pipeline can be developed to visualize and analyze ribosomal RNA fragmentation patterns, enabling identification of distinct fragmentation hotspots associated with ischemic conditions and potential biomarker discovery.
Methods: RNA fragmentation datasets generated from ribosomal RNA experiments in a Saccharomyces cerevisiae yeast model of oxidative stress-induced cell damage were analyzed using R. Comparative analyses between experimental conditions were conducted to identify fragmentation hotspots.
Results: The generated script was used to visualize RNA fragmentation patterns and identify distinct hotspots across ribosomal regions under different experimental conditions. The resulting visualizations effectively highlighted regions of interest and facilitated interpretation of complex RNA sequencing data.
Conclusions: This project developed a computational tool for analyzing RNA fragmentation patterns. This work can be further used to support the identification of potential RNA-based biomarkers for ischemia-reperfusion injury, laying the groundwork for future clinical and translational applications.