Suppr超能文献

RNA测序工作流程:基因水平的探索性分析和差异表达

RNA-Seq workflow: gene-level exploratory analysis and differential expression.

作者信息

Love Michael I, Anders Simon, Kim Vladislav, Huber Wolfgang

机构信息

Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, Massachusetts, USA.

Institute for Molecular Medicine Finland, Helsinki, Finland ; European Molecular Biology Laboratory, Heidelberg, Germany.

出版信息

F1000Res. 2015 Oct 14;4:1070. doi: 10.12688/f1000research.7035.1. eCollection 2015.

Abstract

Here we walk through an end-to-end gene-level RNA-Seq differential expression workflow using Bioconductor packages. We will start from the FASTQ files, show how these were aligned to the reference genome, and prepare a count matrix which tallies the number of RNA-seq reads/fragments within each gene for each sample. We will perform exploratory data analysis (EDA) for quality assessment and to explore the relationship between samples, perform differential gene expression analysis, and visually explore the results.

摘要

在这里,我们将使用Bioconductor软件包展示一个端到端的基因水平RNA测序差异表达工作流程。我们将从FASTQ文件开始,展示如何将这些文件与参考基因组进行比对,并准备一个计数矩阵,该矩阵统计每个样本中每个基因内RNA测序读数/片段的数量。我们将进行探索性数据分析(EDA)以进行质量评估并探索样本之间的关系,进行差异基因表达分析,并直观地探索结果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a998/4670015/7c190e56bb12/f1000research-4-7573-g0000.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验