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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...
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: Jun 20, 2026

Gene Expression Profiling of Infecting Microbes Using a Digital Bar-coding Platform
09:13

Gene Expression Profiling of Infecting Microbes Using a Digital Bar-coding Platform

Published on: January 13, 2016

Analyzing gene expression data from microarray and next-generation dna sequencing transcriptome profiling assays

Sandra Porter1, N Eric Olson, Todd Smith

  • 1Digital World Biology, Seattle, Washington, USA.

Current Protocols in Bioinformatics
|September 4, 2009
PubMed
Summary
This summary is machine-generated.

This chapter details microarray and next-generation sequencing protocols for gene expression analysis. It compares methods for analyzing transcription profiling data from various experimental conditions and platforms.

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Last Updated: Jun 20, 2026

Gene Expression Profiling of Infecting Microbes Using a Digital Bar-coding Platform
09:13

Gene Expression Profiling of Infecting Microbes Using a Digital Bar-coding Platform

Published on: January 13, 2016

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations
11:52

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Published on: August 4, 2016

Area of Science:

  • Molecular Biology
  • Genomics
  • Bioinformatics

Background:

  • Microarray analysis is a standard for gene expression profiling.
  • Next-generation sequencing (NGS) offers advanced capabilities for transcriptome analysis.
  • NGS provides a powerful, complementary tool to microarrays with low background and high dynamic range.

Purpose of the Study:

  • To describe protocols for analyzing gene expression data from microarrays.
  • To discuss alternative protocols for transcriptome analysis using NGS data.
  • To compare microarray and NGS methods for transcription profiling.

Main Methods:

  • Detailed protocols for analyzing microarray data from paired and multiple samples.
  • Discussion of protocols for next-generation sequencing data analysis.
  • Comparison of data from Illumina GA and Applied Biosystems SOLiD platforms.

Main Results:

  • Established protocols for effective microarray data analysis.
  • Presented alternative NGS protocols for transcriptome studies.
  • Highlighted the complementary nature and advantages of NGS over microarrays.

Conclusions:

  • Both microarray and NGS technologies are valuable for gene expression studies.
  • NGS offers enhanced data quality and capacity for transcriptome analysis.
  • The choice of technology depends on experimental design and specific research questions.