RobiNA: a user-friendly, integrated software solution for RNA-Seq-based transcriptomics.
RobiNA: a user-friendly, integrated software solution for RNA-Seq-based transcriptomics.
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DOI：
10.1093/nar/gks540
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发表时间：
2012-07
影响因子：
14.9
通讯作者：
Usadel B
中科院分区：
文献类型：
--
作者：
Lohse M;Bolger AM;Nagel A;Fernie AR;Lunn JE;Stitt M;Usadel B
最近基于下一代RNA测序(RNA-Seq)的快速进展为研究人员提供了前所未有的海量数据集，并打开了转录学的新视角。此外，基于RNA-Seq的转录谱分析可以应用于非模型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，这些新技术带来了新的挑战：在分析之前，需要对原始数据进行严格的质量检查和过滤，并且必须应用适当的统计方法来提取生物相关信息。鉴于数据量巨大，这不是一项微不足道的任务，需要大量的技术资源和生物信息学专业知识的结合。为了帮助个人研究人员，我们开发了Robina作为一个集成解决方案，将基于RNA-Seq的差异基因表达分析的所有步骤整合到一个具有丰富图形用户界面的用户友好的跨平台应用程序中。Robina接受原始FASTQ文件、SAM/BAM对齐文件，并将表格作为输入。它基于R/BioConductor项目开发的最先进的生物统计方法，支持对差异基因表达的质量检查、灵活过滤和统计分析。在线帮助和分步手册指导用户完成分析。适用于MacOSX、Windows和Linux的安装程序包可在http://mapman.gabipd.org/web/guest/robin.的LGPL许可下获得
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.
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影响因子：
5.8
作者：
Holland, R. C. G.;Down, T. A.;Pocock, M.;Prlic, A.;Huen, D.;James, K.;Foisy, S.;Draeger, A.;Yates, A.;Heuer, M.;Schreiber, M. J.
通讯作者：
Schreiber, M. J.
影响因子：
14.9
作者：
Hansen KD;Brenner SE;Dudoit S
通讯作者：
Dudoit S
影响因子：
12.3
作者：
Gentleman RC;Carey VJ;Bates DM;Bolstad B;Dettling M;Dudoit S;Ellis B;Gautier L;Ge Y;Gentry J;Hornik K;Hothorn T;Huber W;Iacus S;Irizarry R;Leisch F;Li C;Maechler M;Rossini AJ;Sawitzki G;Smith C;Smyth G;Tierney L;Yang JY;Zhang J
通讯作者：
Zhang J
影响因子：
14.9
作者：
Leinonen R;Akhtar R;Birney E;Bower L;Cerdeno-Tárraga A;Cheng Y;Cleland I;Faruque N;Goodgame N;Gibson R;Hoad G;Jang M;Pakseresht N;Plaister S;Radhakrishnan R;Reddy K;Sobhany S;Ten Hoopen P;Vaughan R;Zalunin V;Cochrane G
通讯作者：
Cochrane G
影响因子：
3
作者：
Risso D;Schwartz K;Sherlock G;Dudoit S
通讯作者：
Dudoit S