# 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](https://book.ncrnalab.org/teaching/part-iii.-ngs-data-analyses/2.rna-seq/2.1.expression-matrix)
* [2.2 Differential Expression with Cufflinks](https://book.ncrnalab.org/teaching/part-iii.-ngs-data-analyses/2.rna-seq/2.2.differential-expression-with-cufflinks)
* [2.2 Differential Expression with DEseq2 and edgeR](https://book.ncrnalab.org/teaching/part-iii.-ngs-data-analyses/2.rna-seq/2.3.differential_expression_with_deseq2-edger)
* [2.4 Alternative Splicing](https://book.ncrnalab.org/teaching/part-iii.-ngs-data-analyses/6.rna-regulation/alternative-splicing)

## **2) Recommended Tools for RNA-seq**

* Raw Data QC and preprocessing (请参考[mapping](https://book.ncrnalab.org/teaching/part-iii.-ngs-data-analyses/1.mapping)一节)
  * [fastqc](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/)
  * [cutadapt](https://cutadapt.readthedocs.io/en/stable/)
  * [trim\_galore](https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/)
  * [fastp](https://github.com/OpenGene/fastp)
  * [fastx\_toolkit](http://hannonlab.cshl.edu/fastx_toolkit/)
* Spliced Mapping/Alignment (请参考[mapping](https://book.ncrnalab.org/teaching/part-iii.-ngs-data-analyses/1.mapping)一节)
  * [hisat2](http://daehwankimlab.github.io/hisat2/)
  * [STAR](https://github.com/alexdobin/STAR)
* Differential analysis:
  * [DESeq2](https://bioconductor.org/packages/DESeq2/): 利用负二项分布广义线性模型进行差异分析
  * [edgeR](https://bioconductor.org/packages/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](https://bioconductor.org/packages/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](https://github.com/mgonzalezporta/TeachingMaterial) ([Best practices on the differential expression analysis of multi-species RNA-seq](https://pubmed.ncbi.nlm.nih.gov/33926528/) - *Genome Biology 2021*)
* [lncRNA analysis pipeline](http://webhome.weizmann.ac.il/home/igoru/PLAR/)

## 4) Teaching Videos

* see Videos in the [**Files needed** ](https://courses.ncrnalab.org/files)

## 5) References

* [A survey of best practices for RNA-seq data analysis](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0881-8#Abs1) -- *Genome Biology* 2016
* [Best practices on the differential expression analysis of multi-species RNA-seq](https://pubmed.ncbi.nlm.nih.gov/33926528/) - *Genome Biology 2021*

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