Schmid Marc W
Department of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, 8057, Zürich, Switzerland.
Department of Plant and Microbial Biology, University of Zurich, Zollikerstrasse 107, 8008, Zürich, Switzerland.
Methods Mol Biol. 2017;1669:309-335. doi: 10.1007/978-1-4939-7286-9_24.
Comparing gene expression profiles measured in a wide range of different tissue types, at different developmental stages, or under different environmental conditions can yield valuable insights into the mechanisms of cell/tissue specification and differentiation, or identify cell/tissue-type specific responses to environmental stimuli. Critical for such comparisons is the identical processing of data from different sources. This may also include the integration of a novel data set into an existing collection of data sets (e.g., in-house and publicly available data). Here, I describe a complete workflow for RNA-Seq data, from data processing steps to the comparison of gene expression profiles measured with RNA-Seq. I use publicly available data for demonstration purposes, but I also describe how to integrate your own data sets. The workflow runs on all three major operating systems (Linux, MacOS, and Windows). The scripts and the tutorial can be accessed on github.com/MWSchmid/RNAseq_protocol .
比较在广泛的不同组织类型、不同发育阶段或不同环境条件下测量的基因表达谱，能够深入了解细胞/组织特化和分化的机制，或者识别细胞/组织类型对环境刺激的特异性反应。对于此类比较而言，关键在于对来自不同来源的数据进行相同的处理。这可能还包括将新的数据集整合到现有的数据集集合中（例如，内部和公开可用的数据）。在此，我描述了一个完整的RNA-Seq数据工作流程，从数据处理步骤到用RNA-Seq测量的基因表达谱的比较。我使用公开可用的数据进行演示，但我也描述了如何整合自己的数据集。该工作流程可在所有三个主要操作系统（Linux、MacOS和Windows）上运行。脚本和教程可在github.com/MWSchmid/RNAseq_protocol上获取。
