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SUMMARY:An Integrative RNA-Seq Pipeline for Linking microRNA and mRNA Diffe
 rential Expression
LOCATION:Biology 134
TZID:America/Denver
DTSTART:20260508T000000
UID:2026-06-07-22-59-11@natsci.colostate.edu
DTSTAMP:20260607T225911
Description:We are excited to announce that Paige Lillibridge\, a Master\\'
 s candidate in the CSU Department of Biology\, will present her thesis def
 ense\, titled\, \"An Integrative RNA-Seq Pipeline for Linking microRNA and
  mRNA Differential Expression.\"\n\nPaige\\'s research focuses on developi
 ng innovative bioinformatics approaches to better understand the relations
 hip between microRNA and mRNA differential expression. Her work aims to pr
 ovide a robust framework for analyzing RNA sequencing data\, which has imp
 ortant implications for advancing molecular biology and understanding gene
  regulation.\n\nEvent Details\nSpeaker: Paige Lillibridge\nTitle: An Integ
 rative RNA-Seq Pipeline for Linking microRNA and mRNA Differential Express
 ion\nDate: May 8th\, 2026\nTime: 1:00 PM - 4:00 PM\nLocation: Biology 134\
 n\nCan\\'t make it in-person? Join us online!\nZoom: col.st/mvg4g\n\nAdvis
 ed by Dr. Tai Montgomery\, Professor\, CSU Department of Biology\n\nJoin u
 s as Paige shares her findings and celebrates this milestone achievement!\
 nVisit our website for more information on our seminars and follow us on 
 social media for more announcements from Biology.\n\n 	\nInstagram: @csubi
 o\n 	\nTwitter/X: @csubiology\n 	\nFacebook: Department of Biology at Colo
 rado State University\n\nAbstract\nHigh-throughput sequencing technologies
  enable simultaneous measurement of small RNA and messenger RNA (mRNA) exp
 ression\; however\, most differential pipelines treat these data types ind
 ependently. This separation limits the ability to directly evaluate regula
 tory relationships between small RNA and mRNA targets. In this project\, I
  present an integrative RNA-seq workflow that performs DESeq2-based differ
 ential expression analysis for both small RNA and mRNA datasets and then l
 inks them using a user-defined gene table. The pipeline is fully parameter
 ized through external YAML files\, which allows for reproducible and flexi
 ble application across datasets and organisms. Following differential expr
 ession analysis\, the workflow generates integrative visualizations\, incl
 uding 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 co
 rrelation 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 &lt\; 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 wor
 kflow improves reproducibility\, enables cross-dataset analysis\, and prov
 ides a flexible framework for studying RNA regulatory networks.\n\n12:00 a
 m
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