Difference between revisions of "RNASeq and differential gene expression analysis"

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*Obtaining the read counts
*Obtaining the read counts
*Gene expression analysis
*Gene expression analysis
*Dealing with stranded sequencing data

Revision as of 15:02, 26 May 2015

Differential gene expression analysis using RNASeq implies obtaining RNA sequencing data for the conditions to be compared, mapping the RNA reads to the relevant genome (or transcriptome), counting the read coverage for features-of-interest, and using statistical procedures to infer whether the coverages vary in a systematic and statistical signifincant manner.

This section contaIns some technical information for the users of Abel, the UoO high-performance computing cluster. It is not in itself a gene expression analysis tutorial. However, such a tutorial (taken from the UoO course INFBIO9120) is available for download here. This tutorial uses the older "DESeq" R package to do the statistical analysis. The newer "DESeq2" package is used in the following tutorial:


  • Quality control of sequencing data
  • Mapping reads to a genome
  • Visualizing mapped reads
  • Obtaining the read counts
  • Gene expression analysis
  • Dealing with stranded sequencing data