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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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Molecular Chaperones and Protein Folding03:00

Molecular Chaperones and Protein Folding

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A Workflow Guide to RNA-Seq Analysis of Chaperone Function and Beyond.

Kristina M Holton1,2,3, Richard M Giadone4,5,6, Benjamin J Lang7

  • 1Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA. kmholton@fas.harvard.edu.

Methods in Molecular Biology (Clifton, N.J.)
|August 4, 2023
PubMed
Summary
This summary is machine-generated.

RNA sequencing (RNA-seq) is a powerful tool for analyzing gene expression and identifying novel genes. This method aids in understanding biological processes and diseases, particularly those involving chaperone proteins.

Keywords:
ChaperonesDifferential gene expression analysisGene set enrichment analysisOverrepresentation analysisRNA-seq

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Area of Science:

  • Molecular Biology
  • Genomics
  • Bioinformatics

Background:

  • RNA sequencing (RNA-seq) is a key technology for transcriptome-wide analysis.
  • It enables the identification and quantification of cellular transcripts.
  • Applications include differential gene expression, fusion detection, and variant analysis.

Purpose of the Study:

  • To outline an example RNA-sequencing workflow for differential gene expression analysis.
  • To discuss experimental design considerations for RNA-seq studies.
  • To highlight RNA-seq's potential in studying chaperone proteins in biology and disease.

Main Methods:

  • RNA sequencing (RNA-seq) for transcriptional profiling.
  • Differential gene expression (DGE) analysis between sample conditions.
  • Bioinformatic analysis for transcript quantification and identification.

Main Results:

  • The chapter provides a practical workflow for RNA-seq data analysis.
  • It details steps for identifying differentially expressed genes between biological conditions.
  • Considerations for robust experimental design are presented.

Conclusions:

  • RNA-seq is a versatile tool for comprehensive transcriptomic analysis.
  • It offers valuable insights into biological processes and disease mechanisms.
  • The described workflow and design considerations facilitate the study of gene expression, including that of chaperone proteins.