在真核生物里，基因转录出来的是 pre-mRNA（前体 mRNA），里面包含外显子和内含子。在变成成熟 mRNA 的过程中，内含子会被切掉，外显子会被拼接起来，这个过程就叫 剪切（splicing）。而可变剪切（Alternative Splicing, AS） 就是：同一个基因，在不同条件、不同组织、不同处理下，可以选择不同的外显子组合，拼接成多条不同的 mRNA，最终翻译成不同的蛋白质。通常在转录组分析中，会进行可变剪切的分析。

1.可变剪切基本类型

rMATS 最经典的就是把可变剪切分成 5 种基本模式：

1.SE：外显子跳跃（Skipped Exon）某个外显子直接被跳过，不包含在最终 mRNA 里。

2.RI：内含子保留（Retained Intron）本该切掉的内含子被保留下来。

3.A5SS：可变 5’ 剪切位点（Alternative 5' Splice Site）同一个外显子，5’端有两个剪切位点可选。

4.A3SS：可变 3’ 剪切位点（Alternative 3' Splice Site）同一个外显子，3’端有两个剪切位点可选。

5.MXE：互斥外显子（Mutually Exclusive Exons）两个相邻外显子，永远只出现一个，不会同时出现。

这 5 种基本类型，基本覆盖了绝大多数可变剪切事件。

2.分析流程

rMATS是目前转录组可变剪切分析最常用、最稳定的软件之一，专门用于两组样本之间的可变剪切差异分析

1.rMATS

python rmats.py --b1 A.txt --b2 B.txt \ --gtf ref.gtf --nthread 线程数 --od 输出文件夹名称 \ -t paired --variable-read-length \ --readLength 150 --cstat 0.0001  \ --libType fr-unstranded --novelSS

其中A.txt和B.txt里面是A组和B组样本的bam文件位置：

A.txt：a1.bam,a2.bam,a3.bam 
B.txt：b1.bam,b2.bam,b3.bam

其他参数如下：

python rmats/bin/rmats.py -h
usage: rmats.py [options]
options:
  -h, --help            show this help message and exit
  --version             show program's version number and exit
  --gtf GTF             An annotation of genes and transcripts in GTF format
  --b1 B1               A text file containing a comma separated list of the BAM files for sample_1. (Only if using BAM)
  --b2 B2               A text file containing a comma separated list of the BAM files for sample_2. (Only if using BAM)
  --s1 S1               A text file containing a comma separated list of the FASTQ files for sample_1. If using paired reads the format is ":" to separate pairs and "," to separate replicates. (Only if using fastq)
  --s2 S2               A text file containing a comma separated list of the FASTQ files for sample_2. If using paired reads the format is ":" to separate pairs and "," to separate replicates. (Only if using fastq)
  --od OD               The directory for final output from the post step
  --tmp TMP             The directory for intermediate output such as ".rmats" files from the prep step
  -t {paired,single}    Type of read used in the analysis: either "paired" for paired-end data or "single" for single-end data. Default: paired
  --libType {fr-unstranded,fr-firststrand,fr-secondstrand} Library type. Use fr-firststrand or fr-secondstrand for strand-specific data. Only relevant to the prep step, not the post step. Default: fr-unstranded
  --readLength READLENGTH .The length of each read
  --variable-read-length Allow reads with lengths that differ from --readLength to be processed. --readLength will still be used to determine IncFormLen and SkipFormLen
  --anchorLength ANCHORLENGTH . The "anchor length" or "overhang length" used when counting the number of reads spanning splice junctions. A minimum number of "anchor length" nucleotides must be mapped to each end of a given junction. The minimum value is 1 and the default value is set to  1 to make use of all possible splice junction reads.
  --tophatAnchor TOPHATANCHOR. The "anchor length" or "overhang length" used in the aligner. At least "anchor length" NT must be mapped to each end of a given junction. The default is 1. (Only if using fastq)
  --bi BINDEX           The directory name of the STAR binary indices (name of the directory that contains the SA file). (Only if using fastq)
  --nthread NTHREAD     The number of threads. The optimal number of threads should be equal to the number of CPU cores. Default: 1
  --tstat TSTAT         The number of threads for the statistical model. If not set then the value of --nthread is used
  --cstat CSTAT         The cutoff splicing difference. The cutoff used in the null hypothesis test for differential splicing. The default is 0.0001 for 0.01% difference. Valid: 0 <= cutoff < 1. Does not apply to the paired stats model
  --task {prep,post,both,inte,stat}
                        Specify which step(s) of rMATS to run. Default: both. prep: preprocess BAMs and generate a .rmats file. post: load .rmats file(s) nto memory, detect and count alternative splicing events, and calculate P value (if not --statoff). both: prep + post. inte(integrity): check that the BAM filenames recorded by the prep task(s) match the BAM filenames for the current command line. stat: run statistical test on existing output files
  --statoff             Skip the statistical analysis
  --paired-stats        Use the paired stats model
  --novelSS             Enable detection of novel splice sites (unannotated splice sites). Default is no detection of novel splice sites
  --mil MIL             Minimum Intron Length. Only impacts --novelSS behavior. Default: 50
  --mel MEL             Maximum Exon Length. Only impacts --novelSS behavior. Default: 500
  --allow-clipping      Allow alignments with soft or hard clipping to be used
  --fixed-event-set FIXED_EVENT_SET  A directory containing fromGTF.[AS].txt files to be used instead of detecting a new set of events

最后得到的结果如下：

rMATS中，JC是JunctionCounts的缩写，表示跨越剪切位点的reads数量。JCEC是JunctionCounts和ExonCounts的缩写合并，Exon Counts表示不跨越剪切位点的reads数量，JCEC可以理解为所有比对上的reads。

其中一个重要结果SE.MATS.JC.txt结果：

2.rmats2sashimiplot绘图

2.1 rmats2sashimiplot下载安装

git clone https://gitcode.com/gh_mirrors/rm/rmats2sashimiplot
cd rmats2sashimiplotpython2 setup.py install
# 可以提前安装需要的依赖： 
pip install numpy scipy matplotlib pysam
# 如果安装不成功怎么使用：
#rmats2sasmimiplot can be run without installing:
python ./src/rmats2sashimiplot/rmats2sashimiplot.py

安装好了之后存在很多问题，主要是库导入的问题，还有python2到3的一个问题，建议使用python2运行，然后手动修改部分脚本解决库导入。

2.2 rmats2sashimiplot绘图

python2 /share/work/biosoft/rmats2sashimiplot/rmats2sashimiplot/src/rmats2sashimiplot/rmats2sashimiplot.py \
--b1  a1.bam,a2.bam,a3.bam \
--b2  b1.bam,b2.bam,b3.bam  \
--event-type SE -e SE.MATS.JC.txt  \
--l1 One --l2 Two --exon_s 1 --intron_s 5 -o test_events_output

这里用到的是bam文件所以输入文件的参数是--b1和--b2，如果是sam文件记得改成--s1和--s2。其他参数解释如下

--event-type 事件类型，从01里面的五个类型选择，也可以根据*.JC.txt的前缀写
-e rMATs结果文件
-l1 第一个组的lable
-l2 第二个组的lable
--exon_s How much to scale down exons.
--intron_s How much to scale down introns

2.3 结果展示

结果位于./test_events_output/Sashimi_plot文件夹里面

References：

https://www.jianshu.com/p/d09b95a98c64

https://github.com/Xinglab/rmats2sashimiplot

https://github.com/Xinglab/rmats-turbo