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Related Concept Videos

RNA-seq03:21

RNA-seq

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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...
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Ribosome Profiling02:24

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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
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Related Experiment Video

Updated: Feb 27, 2026

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

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Customized workflow development and data modularization concepts for RNA-Sequencing and metatranscriptome

Steffen C Lott1, Markus Wolfien2, Konstantin Riege3

  • 1Genetics and Experimental Bioinformatics, Faculty of Biology, University of Freiburg, Schänzlestr. 1, 79104 Freiburg, Germany.

Journal of Biotechnology
|July 6, 2017
PubMed
Summary
This summary is machine-generated.

This review explains how structured data analysis workflows improve RNA sequencing (RNA-Seq) results. It details best practices for prokaryotic, eukaryotic, and meta-transcriptomics experiments using integrated bioinformatics tools.

Keywords:
MetatranscriptomicsRegulatory RNATranscriptomicsWorkflow development

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • RNA sequencing (RNA-Seq) is crucial for transcriptome analysis.
  • Diverse protocols and designs complicate data interpretation.
  • Standardized analysis is needed for reliable RNA-Seq results.

Purpose of the Study:

  • To explain how structured data analysis workflows enhance RNA-Seq.
  • To present best practices for tool selection and integration.
  • To showcase modular workflows for diverse RNA-Seq experiments.

Main Methods:

  • Review of RNA-Seq protocols and experimental designs.
  • Development of modular data analysis workflows.
  • Integration of bioinformatics tools within computing infrastructures.
  • Case studies for prokaryotic, eukaryotic, and meta-transcriptomics.

Main Results:

  • Structured pre-selection and classification of tools improve RNA-Seq analysis.
  • Modular workflows and ready-to-use infrastructures facilitate data analysis.
  • Examples provided for various RNA-Seq experiment types.

Conclusions:

  • Standardized, modular RNA-Seq analysis workflows are essential.
  • Integration with platforms like Galaxy enhances accessibility.
  • The de.STAIR project provides expertise and tools for robust RNA-Seq analysis.