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Related Experiment Video

Updated: Mar 26, 2026

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
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Measure transcript integrity using RNA-seq data.

Liguo Wang1, Jinfu Nie2, Hugues Sicotte3

  • 1Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, 55905, USA. wang.liguo@mayo.edu.

BMC Bioinformatics
|February 5, 2016
PubMed
Summary
This summary is machine-generated.

We developed the transcript integrity number (TIN) to measure RNA degradation. TIN calibration improves RNA sequencing analysis by reducing false positives and recovering meaningful biological pathways.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Archived clinical tissues are vital for translational research but often yield degraded RNA.
  • RNA degradation significantly impacts gene expression profiling and RNA-seq data.
  • This degradation poses challenges for accurate analysis of biological samples.

Purpose of the Study:

  • To develop a reliable method for quantifying RNA degradation.
  • To assess the effectiveness of this method in correcting gene expression data.
  • To improve the accuracy of differential gene expression analysis.

Main Methods:

  • Development of the transcript integrity number (TIN) as a measure of RNA degradation.
  • Application of TIN to three independent RNA-seq datasets.
  • Comparison of gene expression analysis with and without TIN calibration using prostate cancer samples and spike-in transcripts.

Main Results:

  • TIN reliably measures RNA degradation at both transcript and sample levels.
  • TIN calibration effectively neutralizes RNA degradation effects, reducing false positives.
  • TIN adjustment demonstrated improved sensitivity (0.89) and specificity (0.91) compared to uncorrected data.

Conclusions:

  • The transcript integrity number (TIN) is a dependable metric for RNA integrity.
  • TIN is a valuable tool for mitigating in vitro RNA degradation artifacts.
  • Utilizing TIN enhances the accuracy and biological relevance of gene expression analyses.