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Related Concept Videos

RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...
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Ribosome Profiling

Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
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Related Experiment Video

Updated: May 7, 2026

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
10:10

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

DEXUS: identifying differential expression in RNA-Seq studies with unknown conditions.

Günter Klambauer1, Thomas Unterthiner, Sepp Hochreiter

  • 1Institute of Bioinformatics, Johannes Kepler University, A-4040 Linz, Austria.

Nucleic Acids Research
|September 20, 2013
PubMed
Summary

DEXUS identifies differential gene expression in RNA-Seq data even when sample conditions are unknown. This method models read counts using negative binomial distributions, enabling robust analysis of complex biological datasets.

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Last Updated: May 7, 2026

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Differential gene expression analysis in RNA-Seq is typically limited to studies with predefined sample conditions.
  • Many large-scale biological datasets (e.g., HapMap, ENCODE) lack a priori condition information, hindering differential expression detection.
  • Existing methods are insufficient for analyzing RNA-Seq data from cohort, cross-sectional, or nonrandomized studies with unknown biological groups.

Purpose of the Study:

  • To develop a novel method, DEXUS, for detecting differential gene expression in RNA-Seq data when sample conditions are unknown.
  • To provide a robust statistical framework for identifying differentially expressed transcripts in complex, unannotated datasets.
  • To enable the discovery of biologically relevant variations in large-scale genomic projects.

Main Methods:

  • DEXUS models RNA-Seq read counts using a finite mixture of negative binomial distributions, where each component represents a biological condition.
  • It quantifies differential expression by decomposing read count variation into noise and condition-specific effects.
  • The informative/noninformative (I/NI) value is used to measure evidence of differential expression, allowing adjustable specificity and sensitivity.

Main Results:

  • DEXUS demonstrated excellent performance in identifying differentially expressed transcripts in datasets with unknown conditions.
  • Simulations showed high specificity (up to 99%) and sensitivity (up to 76%) across various I/NI thresholds.
  • Real-world data analysis successfully detected differential expression related to sex, species, tissue, structural variants, and quantitative trait loci.

Conclusions:

  • DEXUS provides a powerful and flexible approach for differential gene expression analysis in RNA-Seq data lacking known sample conditions.
  • The method effectively handles complex biological variation and facilitates the discovery of biologically significant transcriptomic differences.
  • The DEXUS R package is publicly available, promoting its application in diverse genomic research areas.