We are excited to announce that Paige Lillibridge, a Master’s candidate in the CSU Department of Biology, will present her thesis defense, titled, “An Integrative RNA-Seq Pipeline for Linking microRNA and mRNA Differential Expression.”
Paige’s research focuses on developing innovative bioinformatics approaches to better understand the relationship between microRNA and mRNA differential expression. Her work aims to provide a robust framework for analyzing RNA sequencing data, which has important implications for advancing molecular biology and understanding gene regulation.
Event Details
Speaker: Paige Lillibridge
Title: An Integrative RNA-Seq Pipeline for Linking microRNA and mRNA Differential Expression
Date: May 8th, 2026
Time: 1:00 PM – 4:00 PM
Location: Biology 134
Can’t make it in-person? Join us online!
Zoom: col.st/mvg4g
Advised by Dr. Tai Montgomery, Professor, CSU Department of Biology
Join us as Paige shares her findings and celebrates this milestone achievement!
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Instagram: @csubio
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Twitter/X: @csubiology
Abstract
High-throughput sequencing technologies enable simultaneous measurement of small RNA and messenger RNA (mRNA) expression; however, most differential pipelines treat these data types independently. This separation limits the ability to directly evaluate regulatory relationships between small RNA and mRNA targets. In this project, I present an integrative RNA-seq workflow that performs DESeq2-based differential expression analysis for both small RNA and mRNA datasets and then links them using a user-defined gene table. The pipeline is fully parameterized through external YAML files, which allows for reproducible and flexible application across datasets and organisms. Following differential expression analysis, the workflow generates integrative visualizations, including cosmic plots and slope plots, to characterize relationships between small RNAs and their predicted mRNA targets. In addition, I implemented statistical methods to quantify regulatory patterns, including a binomial sign test to evaluate enrichment of inverse relationships and Spearman correlation to assess global monotonic trends. Application of this pipeline to a pasha mutant versus wild-type dataset revealed a strong enrichment of inverse relationships (proportion = 0.865, p < 1 × 10⁻¹⁴⁵), consistent with small RNA-mediated repression. In contrast, correlation analysis showed minimal global association (ρ = -0.012), suggesting that regulatory interactions were highly target specific. This integrative workflow improves reproducibility, enables cross-dataset analysis, and provides a flexible framework for studying RNA regulatory networks.
