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Mos as a tool for genome-wide insertional mutagenesis in Caenorhabditis elegans: results of a pilot study

Nucleic Acids Research
Oxford University Press
Publication Date
DOI: 10.1093/nar/gnh111
  • Nar Methods Online
  • Biology


OP-NARE120484 10084..10097 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 Intawat Nookaew1, Marta Papini1, Natapol Pornputtapong1, Gionata Scalcinati1, Linn Fagerberg2, Matthias Uhle´n2,3 and Jens Nielsen1,3,* 1Novo Nordisk Foundation Center for Biosustainability, Department of Chemical and Biological Engineering, Chalmers University of Technology, SE-41296, Gothenburg, Sweden, 2Novo Nordisk Foundation Center for Biosustainability, Department of Biotechnology, Royal Institute of Technology, SE-10691, Stockholm, Sweden and 3Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2970 Hørsholm, Denmark Received May 9, 2012; Revised and Accepted July 31, 2012 ABSTRACT RNA-seq, has recently become an attractive method of choice in the studies of transcriptomes, pro- mising several advantages compared with micro- arrays. In this study, we sought 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. As a case study for our work we, used the Saccharomyces cerevisiae strain CEN.PK 113-7D, grown under two different conditions (batch and chemostat). Here, we asses the influence of genetic variation on the estimation of gene expression level using three dif- ferent aligners for read-mapping (Gsnap, Stampy and TopHat) on S288c genome, the capabilities of five different statistical methods to detect differen- tial gene expression (baySeq, Cuffdiff, DESeq, edgeR and NOISeq) and we explored the consist- ency between RNA-seq analysis using reference genome and de novo assembly approach. High re- producibility among biological replicates (correl- ation �0.99) and high consistency between the two platforms for analysis of gene expression leve

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