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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...
Ribosome Profiling02:24

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.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique helps...
RNA Splicing01:32

RNA Splicing

Splicing is the process by which eukaryotic RNA is edited before its translation into protein. The RNA strand transcribed from eukaryotic DNA is called the primary transcript. The primary transcripts that become mRNAs are called precursor messenger RNAs (pre-mRNAs). Eukaryotic pre-mRNA contains alternating sequences of exons and introns. Exons are nucleotide sequences that code for proteins, whereas introns are the non-coding regions. In RNA splicing, introns are removed and exons are bonded...
RNA Stability01:53

RNA Stability

Intact DNA strands can be found in fossils, while scientists sometimes struggle to keep RNA intact under laboratory conditions. The structural variations between RNA and DNA underlie the differences in their stability and longevity. Because DNA is double-stranded, it is inherently more stable. The single-stranded structure of RNA is less stable but also more flexible and can form weak internal bonds. Additionally, most RNAs in the cell are relatively short, while DNA can be up to 250 million...
Genome Size and the Evolution of New Genes03:21

Genome Size and the Evolution of New Genes

While every living organism has a genome of some kind (be it RNA, or DNA), there is considerable variation in the sizes of these blueprints. One major factor that impacts genome size is whether the organism is prokaryotic or eukaryotic. In prokaryotes, the genome contains little to no non-coding sequence, such that genes are tightly clustered in groups or operons sequentially along the chromosome. Conversely, the genes in eukaryotes are punctuated by long stretches of non-coding sequence.

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Related Experiment Video

Updated: Jun 23, 2026

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
05:07

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Published on: November 7, 2025

Transcript length bias in RNA-seq data confounds systems biology.

Alicia Oshlack1, Matthew J Wakefield

  • 1Walter and Eliza Hall Institute of Medical Research, Parkville, Vic, Australia. oshlack@wehi.edu.au

Biology Direct
|April 18, 2009
PubMed
Summary

Deep sequencing for transcriptome analysis (RNA-seq) is becoming more common. However, transcript length bias affects the identification of differentially expressed genes, impacting systems biology analyses.

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Last Updated: Jun 23, 2026

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
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Area of Science:

  • Genomics
  • Bioinformatics

Background:

  • Deep sequencing for transcriptome analysis (RNA-seq) is increasingly utilized in mammals.
  • The affordability of RNA-seq positions it as a preferred method for whole genome transcriptional profiling in species with available genomic sequences.
  • Current methodologies for RNA-seq data analysis are still under development, with ongoing exploration of data characteristics.

Purpose of the Study:

  • To investigate the impact of transcript length bias on RNA-seq data analysis.
  • To assess how transcript length influences the identification of differentially expressed genes.

Main Methods:

  • Analysis of three distinct published RNA-sequencing datasets.
  • Evaluation of standard analysis approaches using aggregated tag counts per gene.

Main Results:

  • A strong association was observed between transcript length and the ability to identify differentially expressed genes between samples.
  • Transcript length bias is a significant factor in standard RNA-seq analyses.

Conclusions:

  • Transcript length bias is an inherent characteristic of current RNA-sequencing protocols.
  • This bias has implications for gene expression ranking and can introduce inaccuracies in gene set testing for pathway analysis and other systems biology approaches.