Genetics and Experimental Bioinformatics, Faculty of Biology, University of Freiburg, Schänzlestr. 1, 79104 Freiburg, Germany.
Department of Systems Biology & Bioinformatics, University of Rostock, Ulmenstr. 69, 18057 Rostock, Germany.
J Biotechnol. 2017 Nov 10;261:85-96. doi: 10.1016/j.jbiotec.2017.06.1203. Epub 2017 Jul 1.
RNA-Sequencing (RNA-Seq) has become a widely used approach to study quantitative and qualitative aspects of transcriptome data. The variety of RNA-Seq protocols, experimental study designs and the characteristic properties of the organisms under investigation greatly affect downstream and comparative analyses. In this review, we aim to explain the impact of structured pre-selection, classification and integration of best-performing tools within modularized data analysis workflows and ready-to-use computing infrastructures towards experimental data analyses. We highlight examples for workflows and use cases that are presented for pro-, eukaryotic and mixed dual RNA-Seq (meta-transcriptomics) experiments. In addition, we are summarizing the expertise of the laboratories participating in the project consortium "Structured Analysis and Integration of RNA-Seq experiments" (de.STAIR) and its integration with the Galaxy-workbench of the RNA Bioinformatics Center (RBC).
RNA 测序（RNA-Seq）已成为研究转录组数据定量和定性方面的常用方法。RNA-Seq 方案的多样性、实验研究设计以及所研究生物的特征属性极大地影响了下游和比较分析。在这篇综述中，我们旨在解释结构化预选、分类和最佳性能工具的集成在模块化数据分析工作流程和现成的计算基础设施中对实验数据分析的影响。我们强调了针对原核、真核和混合双 RNA-Seq（元转录组学）实验的工作流程和用例示例。此外，我们总结了参与“RNA-Seq 实验的结构化分析和整合”（de.STAIR）项目联盟的实验室的专业知识及其与 RNA 生物信息学中心（RBC）的 Galaxy 工作平台的整合。
