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Updated: May 11, 2026
05:07
Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
Published on: November 7, 2025
Exploring the sampling universe of RNA-seq.
Stefanie Tauber1, Arndt von Haeseler
1Center for Integrative Bioinformatics, Max F Perutz Laboratories, University of Vienna and Medical University of Vienna, Vienna, Austria. stefanie.tauber@univie.ac.at
Summary
Determining optimal RNA sequencing depth is crucial. This study models RNA sequencing as a sampling process using the Pitman Sampling Formula to estimate the number of detected genes with increased sequencing.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- RNA sequencing (RNA-seq) is a powerful tool for gene expression analysis.
- The optimal sequencing depth required to detect all expressed genes remains unknown.
- Understanding sampling processes is key to interpreting RNA-seq data.
Purpose of the Study:
- To model RNA sequencing as a sampling process.
- To characterize RNA-seq protocols and biases using sampling parameters.
- To estimate the number of newly detected genes with increased sequencing depth.
Main Methods:
- Application of the Pitman Sampling Formula to model RNA sequencing.
- Characterization of sampling by two parameters reflecting sequencing technologies and biases.
- Analysis of sampling at the gene level and at the position level within genes.
Main Results:
- The study provides a framework to model RNA sequencing as a sampling process.
- Two parameters are introduced to characterize sequencing depth and protocol biases.
- An estimator for detecting new genes based on pilot sequencing data is developed.
Conclusions:
- The Pitman Sampling Formula offers a robust model for RNA sequencing sampling.
- The developed parameters help evaluate sequencing uniformity and protocol performance.
- This approach enables better estimation of gene detection with varying sequencing depths.
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