2.RNA-seq

• 常规RNA-seq最直接的作用在于对基因表达进行定量。在本章中我们主要来讨论和基因表达定量相关的RNA-seq数据分析。

1) Table of Contents

• 2.1 Expression Matrix

• 2.2 Differential Expression with Cufflinks

• 2.2 Differential Expression with DEseq2 and edgeR

• 2.4 Alternative Splicing

2) Recommended Tools for RNA-seq

• Raw Data QC and preprocessing (请参考mapping一节)

  • fastqc

  • cutadapt

  • trim_galore

  • fastp

  • fastx_toolkit

• Spliced Mapping/Alignment (请参考mapping一节)

  • hisat2

  • STAR

• Differential analysis:

  • DESeq2: 利用负二项分布广义线性模型进行差异分析

  • edgeR: 和DESeq2类似，利用负二项分布广义线性模型进行差异分析。

  • limma: 最早针对microarray数据开发的差异分析工具。开发者后来又进行了多次改进使其也适用于RNA-seq的分析。

3) Pipelines for RNA-seq

• RNA-seq analysis pipeline (Best practices on the differential expression analysis of multi-species RNA-seq - Genome Biology 2021)

• lncRNA analysis pipeline

4) Teaching Videos

• see Videos in the Files needed

5) References

• A survey of best practices for RNA-seq data analysis -- Genome Biology 2016

• Best practices on the differential expression analysis of multi-species RNA-seq - Genome Biology 2021