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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...

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Related Experiment Video

Updated: May 29, 2026

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens
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Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Published on: June 28, 2018

DEB: A web interface for RNA-seq digital gene expression analysis.

Ji Qiang Yao1, Fahong Yu

  • 1Interdisciplinary Center for Biotechnology Research, University of Florida, Gainesville, FL 32610.

Bioinformation
|September 10, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces DEB, a pipeline that automates digital expression analysis using RNA-seq data. DEB simplifies comparing results from edgeR, DESeq, and bayseq for transcriptomic analysis.

Keywords:
DEBDESeqRNA-seqbaySeqdigital expressionedgeRnextGen sequencing

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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
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Published on: June 28, 2018

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Digital expression (DE) analysis quantifies transcriptomes using RNA-seq data.
  • Comparing gene expression across conditions requires statistical algorithms like edgeR, DESeq, and bayseq.
  • Current methods necessitate manual installation and execution of individual R packages.

Purpose of the Study:

  • To present DEB, an automated pipeline for digital expression analysis.
  • To streamline the process of file preparation, computation, and result comparison.
  • To facilitate the comparison of results from different digital expression analysis algorithms.

Main Methods:

  • Development of a novel pipeline named DEB.
  • Automation of RNA-seq data processing for digital expression analysis.
  • Integration of multiple statistical algorithms (edgeR, DESeq, bayseq) for comparative analysis.

Main Results:

  • DEB automates the entire workflow of digital expression analysis.
  • The pipeline enables seamless comparison of results from different analytical approaches.
  • User-friendly automation of complex transcriptomic data analysis.

Conclusions:

  • DEB simplifies and standardizes digital expression analysis.
  • The pipeline enhances the efficiency and comparability of RNA-seq based gene expression studies.
  • DEB provides a valuable tool for researchers in transcriptomics.