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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 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...
Chromatin Structure Regulates pre-mRNA Processing02:41

Chromatin Structure Regulates pre-mRNA Processing

In eukaryotic cells, nascent mRNA transcripts need to undergo many post-transcriptional modifications to reach the cell cytoplasm and translate into functional proteins. For a long time, transcription and pre-mRNA processing were considered two independent events that occur sequentially in the cell. However, it has now been well established that transcription and pre-mRNA processing are two simultaneous processes that are precisely regulated inside the cell.
The chromatin structure, especially...

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Related Experiment Video

Updated: May 29, 2026

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
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Published on: March 7, 2018

Local and global factors affecting RNA sequencing analysis.

Edward Sendler1, Graham D Johnson, Stephen A Krawetz

  • 1Department of Obstetrics and Gynecology, CS Mott Center for Human Growth and Development, Wayne State University School of Medicine, Detroit, MI 48201, USA.

Analytical Biochemistry
|September 6, 2011
PubMed
Summary

High-throughput RNA sequencing (RNA-seq) is a powerful tool for studying gene expression. However, factors like GC content and fragmentation can bias results, necessitating careful analysis strategies.

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Last Updated: May 29, 2026

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
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Published on: March 7, 2018

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Published on: February 2, 2024

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

Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • High-throughput RNA sequencing (RNA-seq) is the gold standard for global transcript level assessment.
  • RNA-seq offers significant advantages for understanding transcription and posttranscriptional regulation.
  • Despite its power, RNA-seq is susceptible to confounding factors that can bias results.

Purpose of the Study:

  • To identify and describe confounding factors affecting RNA-seq sequencing coverage.
  • To provide strategies for recognizing and controlling common RNA-seq artifacts.
  • To improve the accuracy and reliability of RNA-seq data analysis.

Main Methods:

  • Identification of sequencing coverage biases in RNA-seq data.
  • Analysis of factors including regional GC content, fragmentation sites, and primer effects.
  • Review of transcript end effects and read pile-up phenomena.

Main Results:

  • Several previously unreported factors significantly impact RNA-seq sequencing coverage.
  • Regional GC content, fragmentation preferences, and primer affinity cause biased read distribution.
  • Transcript end effects contribute to read pile-up, affecting accurate quantification.

Conclusions:

  • Understanding the causes of RNA-seq artifacts is crucial for accurate data interpretation.
  • Implementing strategies to mitigate these factors is essential for reliable transcriptome analysis.
  • This work provides guidance to avoid common complicating factors in RNA-seq experiments.