Home » RNA-Seq Expression Analysis

RNA-Seq Expression Analysis

The growing availability and reduced cost of next-generation sequencing (NGS) is making RNA-Seq an increasingly appealing tool for analyzing the expression of genes, alternatively spliced transcripts and other genomic elements. However, the additional quantity and complexity of the NGS data is preventing researchers from taking full advantage of this exciting new technology for RNA-Seq expression analysis. We would like to share with you this Case Study, in which we answer questions and provide researchers with the tools necessary to plan and execute an RNA-Seq expression analysis experiment:

  • We compare the results of several popular algorithms, including Cuffdiff, DESeq and edgeR.
  • We explain the difference between quantification algorithms that are based on standard counting approach and algorithms that utilize the normalizing (FPKM) approach.
  • We compare results produced from normalized (FPKM) data and standard counting data.
  • We compare results of two-sample (no replicate) expression analysis to results derived from analysis using biological replicates.
  • We compare results derived by using traditional gene expression analysis algorithms to those produced by algorithm designed specifically for NGS and RNA-Seq data.
  • We provide a step-by-step tutorial that allows researchers to easily perform their own RNA-Seq expression analysis utilizing multiple peer-reviewed algorithms and cloud computing technology.
  • Most importantly, we demonstrate that integrated tools exist that allow individual researchers to easily take advantage of modern algorithm and cloud-computing infrastructure, to effectively manage and analyze the giant NGS data sets.

The case study will cover each of these steps. For the complete results, download the white paper here.