这个软件不仅仅能做QC，而且可以统计各个基因的RPKM值！尤其是TCGA计划里面的都是用它算的

一、程序安装

直接在官网下载java版本软件即可使用：http://www.broadinstitute.org/cancer/cga/tools/rnaseqc/RNA-SeQC_v1.1.8.jar

箭头所指的文件，一个都不少，只有那个rRNA.tar我没有用， 因为这个软件有两种使用方式，我用的是第一种

软件的官网给力例子，很容易学习：

RNA-SeQC can be run with or without a BWA-based rRNA level estimation mode. To run without (less accurate, but faster) use the command:
java -jar RNASeQC.jar -n 1000 -s "TestId|ThousandReads.bam|TestDesc" -t gencode.v7.annotation_goodContig.gtf -r Homo_sapiens_assembly19.fasta -o ./testReport/ -strat gc -gc gencode.v7.gc.txt 

代码如下：

To run the more accurate but slower, BWA-based method :
java -jar RNASeQC.jar -n 1000 -s "TestId|ThousandReads.bam|TestDesc" -t gencode.v7.annotation_goodContig.gtf -r Homo_sapiens_assembly19.fasta -o ./testReport/ -strat gc -gc gencode.v7.gc.txt -BWArRNA human_all_rRNA.fasta
Note: this assumes BWA is in your PATH. If this is not the case, use the -bwa flag to specify the path to BWA

运行要点时间，就那个一千条reads的测试数据都搞了10分钟！

出来一大堆突变，具体解释，官网上面很详细，不过，比较重要的当然是RPKM值咯，还有QC的信息

TCGA数据里面都会提供由RNA-SeQC软件处理得到的表达矩阵！

Expression

• RPKM data are used as produced by RNA-SeQC.
• Filter on >=10 individuals with >0.1 RPKM and raw read counts greater than 6.
• Quantile normalization was performed within each tissue to bring the expression profile of each sample onto the same scale.
• To protect from outliers, inverse quantile normalization was performed for each gene, mapping each set of expression values to a standard normal.