Shiny-Seq: advanced guided transcriptome analysis
Shiny-Seq: advanced guided transcriptome analysis
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DOI：
10.1186/s13104-019-4471-1
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发表时间：
2019-07-18
影响因子：
1.8
通讯作者：
Ulas, Thomas
中科院分区：
文献类型：
--
作者：
Sundararajan, Zenitha;Knoll, Rainer;Ulas, Thomas
目的：对RNA-Seq数据的全面分析使用了各种不同的工具和算法，这些工具和算法通常仅限于R用户。虽然有一些工具和高级分析管道可用，但其中一些需要编程技能，而另一些则缺乏对许多重要特性的支持，而这些特性无法实现更全面的数据分析。因此，需要一个指导和易于使用的全面RNA-Seq数据平台，它集成了最先进的分析工作流程。结果：我们提出了shine - seq工具，它提供了一个指导和易于使用的全面RNA-Seq数据分析管道。它具有许多功能，如批效应估计和去除，使用多个可视化选项进行质量检查，使用多个生物数据库进行富集分析，使用加权基因共表达网络分析等先进方法识别模式，将分析总结为幻灯片演示，并通过一键功能将所有结果显示为表格。源代码发布在GitHub （https://github.com/schultzelab/Shiny-Seq）上，并获得GPLv3许可。Shiny- seq是使用Shiny框架用R编写的。此外，该应用程序托管在由shinyapps托管的公共网站上。io服务器（https://schultzelab.shinyapps.io/Shiny-Seq/）和Docker镜像https://hub.docker.com/r/makaho/shiny-seq。
Objective: A comprehensive analysis of RNA-Seq data uses a wide range of different tools and algorithms, which are normally limited to R users only. While several tools and advanced analysis pipelines are available, some require programming skills and others lack the support for many important features that enable a more comprehensive data analysis. There is thus, a need for a guided and easy to use comprehensive RNA-Seq data platform, which integrates the state of the art analysis workflow.Results: We present the tool Shiny-Seq, which provides a guided and easy to use comprehensive RNA-Seq data analysis pipeline. It has many features such as batch effect estimation and removal, quality check with several visualization options, enrichment analysis with multiple biological databases, identification of patterns using advanced methods such as weighted gene co-expression network analysis, summarizing analysis as power point presentation and all results as tables via a one-click feature. The source code is published on GitHub (https://github.com/schultzelab/Shiny-Seq) and licensed under GPLv3. Shiny-Seq is written in R using the Shiny framework. In addition, the application is hosted on a public website hosted by the shinyapps.io server (https://schultzelab.shinyapps.io/Shiny-Seq/) and as a Docker image https://hub.docker.com/r/makaho/shiny-seq.