A simple, rapid, high-fidelity and cost-effective PCR-based two-step DNA synthesis method for long gene sequences

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A simple, rapid, high-fidelity and cost-effective PCR-based two-step DNA synthesis method for long gene sequences

Oxford University Press
Publication Date
Jan 01, 2004
  • Biology
  • Computer Science


gks540 622..627 RobiNA: a user-friendly, integrated software solution for RNA-Seq-based transcriptomics Marc Lohse1,*, Anthony M. Bolger1, Axel Nagel1, Alisdair R. Fernie1, John E. Lunn1, Mark Stitt1 and Bjo¨rn Usadel1,2,3 1Max-Planck-Institute of Molecular Plant Physiology, Am Mu¨hlenberg 1, 14476 Potsdam-Golm, 2RWTH Aachen University, Worring Weg 1, 52074 Aachen and 3Institute of Bio- and Geosciences, IBG-2: Plant Sciences, Forschungszentrum Ju¨lich, Leo-Brandt-Straße, 52425 Ju¨lich, Germany Received March 3, 2012; Revised May 3, 2012; Accepted May 12, 2012 ABSTRACT Recent rapid advances in next generation RNA sequencing (RNA-Seq)-based provide researchers with unprecedentedly large data sets and open new perspectives in transcriptomics. Furthermore, RNA-Seq-based transcript profiling can be applied to non-model and newly discovered organisms because it does not require a predefined measuring platform (like e.g. microarrays). However, these novel technologies pose new challenges: the raw data need to be rigorously quality checked and filtered prior to analysis, and proper statistical methods have to be applied to extract biologically relevant information. Given the sheer volume of data, this is no trivial task and requires a combin- ation of considerable technical resources along with bioinformatics expertise. To aid the individual researcher, we have developed RobiNA as an integrated solution that consolidates all steps of RNA-Seq-based differential gene-expression analysis in one user-friendly cross-platform applica- tion featuring a rich graphical user interface. RobiNA accepts raw FastQ files, SAM/BAM align- ment files and counts tables as input. It supports quality checking, flexible filtering and statistical analysis of differential gene expression based on state-of-the art biostatistical methods developed in the R/Bioconductor projects. In-line help and a step-by-step manual guide users through the analysis. Installer packages for Mac OS X, Windows and Linux are available und

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