Effects of the method on estimation of DGE

Researchers at Chalmers University of Technology, Sweden set out to assess the contribution of the different analytical steps involved in the analysis of RNA-seq data generated with the Illumina platform, and to perform a cross-platform comparison based on the results obtained through Affymetrix microarray. They investigated: the use of three different aligners for read-mapping (Gsnap, Stampy and TopHat) on the genome, the capabilities of five different statistical methods to detect differential gene expression (baySeq, Cuffdiff, DESeq, edgeR and NOISeq) and they explored the consistency between RNA-seq analysis using reference genome and de novo assembly approach.

Results derived from different statistical methods of RNA-seq gave similar biological interpretations as is demonstrated by GO enrichment analysis. Their results strongly supports the robustness and reliability of different processing and analysis of RNA-seq data. Furthermore, we identified high consistency between microarray and RNA-seq platforms, thus encouraging the continual use of microarray as a versatile tool for differential gene expression analysis.

  • Nookaew I, Papini M, Pornputtpong N, Scalcinati G, Fagerberg L, Uhlén M, Nielsen J. (2012) A comprehensive comparison of RNA-Seq-based transcriptome analysis from reads to differential gene expression and cross-comparison with microarrays: a case study in Saccharomyces cerevisiae. Nucleic Acids Res [Epub ahead of print]. [article]
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