![]() These advances have created a demand for user-friendly software that enables researchers to handle NGS data sets and extract biologically relevant information. Continual refinement of technologies and decreasing per-base sequencing costs will allow de novo sequencing approaches to be adopted by an increasing number of labs. As described in several recent studies ( 3–6), long Roche/454 and short Illumina/Solexa or SOLiD sequencing reads can be used to first assemble a reference transcriptome of a hitherto poorly sequenced species and subsequently assess differential gene expression (DGE). These advances have also greatly expanded the range of species amenable to transcriptomic analysis, by essentially providing a means to create new transcriptomes from the data itself. A vast volume of expression data is being made available to the research community via several public data repositories, e.g. Next-generation high-throughput sequencing (NGS) is leading to the accumulation of a wealth of genomic and high-throughput mRNA sequencing (RNA-Seq) data is enabling increasingly comprehensive transcriptomic studies. Installer packages for Mac OS X, Windows and Linux are available under the LGPL licence from. In-line help and a step-by-step manual guide users through the analysis. 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. Robi NA accepts raw FastQ files, SAM/BAM alignment files and counts tables as input. To aid the individual researcher, we have developed Robi NA as an integrated solution that consolidates all steps of RNA-Seq-based differential gene-expression analysis in one user-friendly cross-platform application featuring a rich graphical user interface. ![]() ![]() Given the sheer volume of data, this is no trivial task and requires a combination of considerable technical resources along with bioinformatics expertise. 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. 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. Recent rapid advances in next generation RNA sequencing (RNA-Seq)-based provide researchers with unprecedentedly large data sets and open new perspectives in transcriptomics. ![]()
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