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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...
DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

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Related Experiment Video

Updated: May 28, 2026

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
07:09

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

GENE-counter: a computational pipeline for the analysis of RNA-Seq data for gene expression differences.

Jason S Cumbie1, Jeffrey A Kimbrel, Yanming Di

  • 1Department of Botany and Plant Pathology, Oregon State University, Corvallis, Oregon, United States of America.

Plos One
|October 15, 2011
PubMed
Summary
This summary is machine-generated.

GENE-counter is a versatile pipeline for RNA-Sequencing (RNA-Seq) data analysis, enabling differential gene expression studies in diverse organisms. It offers robust statistical methods and transparent data management for reliable results.

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Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
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Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

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Last Updated: May 28, 2026

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
07:09

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
10:10

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • RNA-Sequencing (RNA-Seq) is crucial for transcriptomic analysis.
  • Analyzing differential gene expression requires robust computational tools.
  • Existing tools may have limitations with non-model organisms or small sample sizes.

Purpose of the Study:

  • To introduce GENE-counter, a comprehensive Perl-based computational pipeline for RNA-Seq data analysis.
  • To provide a flexible tool applicable to various organisms, including prokaryotes and non-model organisms.
  • To offer robust statistical methods for differential gene expression analysis and gene ontology enrichment.

Main Methods:

  • GENE-counter utilizes Perl for its computational pipeline.
  • Supports multiple alignment tools (CASHX, Bowtie, BWA) and any Sequence Alignment/Map (SAM)-compliant program.
  • Incorporates three statistical packages based on the negative binomial distribution, including a novel default method.
  • Includes three methods for gene ontology (GO) term enrichment analysis.
  • Stores results in a MySQL relational database for systematic data management and quality assessment.

Main Results:

  • GENE-counter successfully processed a small-scale RNA-Seq dataset from Arabidopsis thaliana.
  • Demonstrated support for analysis of microarrays alongside RNA-Seq data.
  • Showcased substantial overlap in results across different statistical packages, indicating robustness.
  • Validated suitability for handling small sample sizes and high gene count variability.

Conclusions:

  • GENE-counter is a well-suited computational pipeline for differential gene expression analysis of RNA-Seq data.
  • Its flexibility makes it applicable to a wide range of organisms and experimental conditions.
  • The pipeline ensures transparent results and facilitates further data analysis through systematic storage.