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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...
Cis-regulatory Sequences02:02

Cis-regulatory Sequences

Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
Cis-regulatory Sequences02:02

Cis-regulatory Sequences

Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
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.
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Sanger Sequencing01:57

Sanger Sequencing

DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...

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Related Experiment Video

Updated: May 26, 2026

Introductory Analysis and Validation of CUT&RUN Sequencing Data
04:58

Introductory Analysis and Validation of CUT&RUN Sequencing Data

Published on: December 13, 2024

GC-content normalization for RNA-Seq data.

Davide Risso1, Katja Schwartz, Gavin Sherlock

  • 1Division of Biostatistics and Department of Statistics, University of California, Berkeley, USA.

BMC Bioinformatics
|December 20, 2011
PubMed
Summary
This summary is machine-generated.

RNA-Seq gene expression analysis is prone to GC-content bias. New normalization methods reduce this bias, improving accuracy in differential expression analysis for RNA-Seq data.

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Last Updated: May 26, 2026

Introductory Analysis and Validation of CUT&RUN Sequencing Data
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Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
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Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Published on: November 7, 2025

Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • RNA-Seq is a key technology for gene expression studies.
  • RNA-Seq data can be affected by technology-related biases, such as GC-content.
  • Normalization is critical for accurate gene expression measurement and analysis.

Purpose of the Study:

  • To investigate and address GC-content bias in RNA-Seq read counts.
  • To develop and evaluate novel normalization methods for RNA-Seq data.
  • To improve the accuracy of differential gene expression analysis.

Main Methods:

  • Focus on sample-specific GC-content effects on RNA-Seq read counts.
  • Propose three within-lane gene-level GC-content normalization approaches.
  • Compare proposed methods against state-of-the-art procedures using two diverse RNA-Seq datasets.
  • Implement methods in the open-source Bioconductor R package EDASeq.

Main Results:

  • Demonstrated substantial sample-specific GC-content effects on RNA-Seq read counts.
  • Showed that proposed normalization methods reduce GC-content bias.
  • Evaluated performance based on bias, mean squared error, Type I error, and p-value distributions.

Conclusions:

  • Within-lane and between-lane normalization effectively reduce GC-content bias in RNA-Seq.
  • Normalization leads to more accurate estimates of expression fold-changes.
  • Improved accuracy is crucial for reliable biological interpretation of RNA-Seq experiments.