Max-Planck-Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany.
Nucleic Acids Res. 2012 Jul;40(Web Server issue):W622-7. doi: 10.1093/nar/gks540. Epub 2012 Jun 8.
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 combination 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 application featuring a rich graphical user interface. RobiNA accepts raw FastQ files, SAM/BAM alignment 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 under the LGPL licence from http://mapman.gabipd.org/web/guest/robin.
近年来,基于新一代 RNA 测序(RNA-Seq)的技术取得了快速进展,为研究人员提供了前所未有的大数据集,并在转录组学领域开辟了新的视角。此外,基于 RNA-Seq 的转录谱分析可以应用于非模式生物和新发现的生物,因为它不需要预定义的测量平台(如微阵列)。然而,这些新的技术带来了新的挑战:在进行分析之前,需要严格检查和过滤原始数据,并应用适当的统计方法来提取有生物学意义的信息。鉴于数据量之大,这并非易事,需要结合大量的技术资源和生物信息学专业知识。为了帮助单个研究人员,我们开发了 RobiNA,它是一个集成的解决方案,将基于 RNA-Seq 的差异基因表达分析的所有步骤整合到一个用户友好的跨平台应用程序中,该应用程序具有丰富的图形用户界面。RobiNA 接受原始的 FastQ 文件、SAM/BAM 对齐文件和计数表作为输入。它支持基于最新的生物统计学方法的质量检查、灵活的过滤和差异基因表达的统计分析,这些方法是在 R/Bioconductor 项目中开发的。在线帮助和分步手册指南引导用户完成分析。Mac OS X、Windows 和 Linux 的安装程序包可根据 LGPL 许可证从 http://mapman.gabipd.org/web/guest/robin 获得。