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
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1.2 bedtools and samtools

2.1.Expression Matrix

Last modified 4mo ago

2.RNA-seq

1) Table of Contents

2) Recommended Tools for RNA-seq

3) Pipelines for RNA-seq

4) Teaching Videos

5) References

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