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RNA-Seq-Based Comparative Transcriptomics: RNA Preparation and Bioinformatics.

Antonio Rodríguez-García1,2, Alberto Sola-Landa3, Carlos Barreiro3,4

  • 1Instituto de Biotecnología de León (INBIOTEC), Parque Científico de León, Avenida Real 1, 24006, León, Spain. antonio.rodriguez@inbiotec.com.

Methods in Molecular Biology (Clifton, N.J.)
|July 16, 2017
PubMed
Summary

This study details RNA extraction and bioinformatics protocols for RNA sequencing (RNA-seq) to identify differentially expressed genes. These methods enable microbiology labs to leverage RNA-seq for advanced transcriptome analysis.

Keywords:
B-3805BioconductorDifferential expressionMycobacteriumNext-generation sequencingRNA extractionRNA-seqTranscriptomics

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Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Genomics

Background:

  • Differential gene expression analysis is crucial for understanding biological responses.
  • Next-generation sequencing of RNA (RNA-seq) offers superior sensitivity, accuracy, and dynamic range compared to microarrays.
  • Many microbiology labs have not yet adopted RNA-seq due to perceived complexity or cost.

Purpose of the Study:

  • To provide a detailed protocol for RNA extraction for RNA-seq.
  • To introduce a bioinformatics workflow for analyzing RNA-seq data.
  • To facilitate the identification of differentially expressed genes in microbiological studies.

Main Methods:

  • RNA extraction from biological samples.
  • Bioinformatic processing of next-generation sequencing data.
  • Statistical analysis to determine differential gene expression.

Main Results:

  • A comprehensive protocol for RNA extraction suitable for RNA-seq.
  • An introductory guide to bioinformatics analysis yielding differential expression results.
  • Demonstration of RNA-seq's utility in transcriptome analysis.

Conclusions:

  • RNA extraction and bioinformatics protocols can be readily implemented in microbiology labs.
  • RNA-seq provides a powerful tool for transcriptome analysis and gene discovery.
  • This protocol aims to lower the barrier for microbiology laboratories to adopt RNA-seq technologies.