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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...
Ribozymes02:47

Ribozymes

The term ribozyme is used for RNA that can act as an enzyme. Ribozymes are mainly found in selected viruses, bacteria, plant organelles, and lower eukaryotes. Ribozymes were first discovered in 1982 when Tom Cech’s laboratory observed Group I introns acting as enzymes. This was shortly followed by the discovery of another ribozyme, Ribonulcease P, by Sid Altman’s laboratory. Both Cech and Altman received the Nobel Prize in chemistry in 1989 for their work on ribozymes.
Ribozymes can be...

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Related Experiment Video

Updated: May 21, 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

RobiNA: a user-friendly, integrated software solution for RNA-Seq-based transcriptomics.

Marc Lohse1, Anthony M Bolger, Axel Nagel

  • 1Max-Planck-Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany. lohse@mpimp-golm.mpg.de

Nucleic Acids Research
|June 12, 2012
PubMed
Summary
This summary is machine-generated.

Researchers can now analyze RNA sequencing (RNA-Seq) data more easily with RobiNA, a new software tool. This application simplifies complex gene expression analysis for individual researchers, offering a user-friendly solution.

Related Experiment Videos

Last Updated: May 21, 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

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Next-generation RNA sequencing (RNA-Seq) generates vast datasets, offering new transcriptomics insights.
  • RNA-Seq is versatile, applicable to non-model organisms without prior platform requirements.
  • Analyzing large RNA-Seq datasets presents challenges in quality control, filtering, and statistical analysis, demanding significant resources and expertise.

Purpose of the Study:

  • To develop an integrated, user-friendly software solution for RNA-Seq-based differential gene expression analysis.
  • To provide researchers with a tool that consolidates all analysis steps from raw data to biologically relevant information.
  • To support individual researchers by simplifying complex bioinformatics workflows.

Main Methods:

  • Development of RobiNA, a cross-platform application with a graphical user interface.
  • Integration of quality checking, flexible filtering, and differential gene expression analysis.
  • Implementation of state-of-the-art biostatistical methods from R/Bioconductor projects.
  • Support for various input formats including FastQ, SAM/BAM alignment files, and counts tables.

Main Results:

  • RobiNA offers a consolidated workflow for RNA-Seq differential gene expression analysis.
  • The software provides quality checking and flexible filtering capabilities.
  • RobiNA incorporates advanced statistical methods for robust gene expression analysis.
  • User-friendly interface with in-line help and a step-by-step manual enhances accessibility.

Conclusions:

  • RobiNA serves as an integrated solution to address the challenges of analyzing large RNA-Seq datasets.
  • The software empowers individual researchers by simplifying complex bioinformatics tasks.
  • RobiNA facilitates deeper insights into transcriptomics through accessible differential gene expression analysis.