• Hiast2 比对
• samtools sam 转bam
• stringtie组装转录本
• gffcompare将stringtie输出的gtf文件与参考基因组的注释文件做比较得到一- 个merged.combine.gtf
• 使用merged.combine.gtf 这个文件对每个样本计算表达量，输出文件存储到ballgown文件夹下，这一步用到的命令是     stringtie -e -B -p 8 -G merged.combined.gtf -o ballgown/L01/L01.gtf output_bam/L01.sorted.bam

 

• 接下来是R语言的ballgown包读入数据获取基因和转录本的表达量 代码是

library(ballgown)
library(genefilter)
library(dplyr)

pheno_data<-read.csv("pheno_data.txt",header=T)
grape_expr <- ballgown(dataDir = "ballgown",
                    samplePattern = "L0",
                    pData = pheno_data)

 

 

这一步可以拿到gene_id还有gene_name ,FPKM的表达量，cov对用的应该是reads count吧。

接下来是差异表达分析

代码是

grape_expr_filter<-subset(grape_expr,
                          "rowVars(texpr(grape_expr))>1",
                          genomesubset=T)
results_genes <- stattest(grape_expr_filter,
                          feature = "gene",
                          covariate = "time_point",
                          getFC = TRUE,
                          meas = "FPKM")
#results_genes <- arrange(results_genes,pval)
results_genes%>%
  filter(abs(fc)>=2&pval<0.05) -> results_genes_diff

dim(results_genes_diff)
head(results_genes_diff)

现在有了基因id

 

接下来是使用clusterProfiler做go注释

这里参考 https://guangchuangyu.github.io/cn/2017/07/clusterprofiler-maize/#disqus_thread

首先把这个基因id对应着转换成 ENTREZID ，这里需要借助对应的gtf注释文件

这里只关注蛋白编码基因

grep 'gene_biotype "protein_coding"' 12X_genomic.gtf > 12X_protein_coding.gtf
#python
from gtfparse import read_gtf
known_proteincoding = read_gtf("12X_protein_coding.gtf")
known_proteincoding.to_csv("all_protein_coding.csv")

GO富集分析的R语言代码

require(AnnotationHub)
hub<-AnnotationHub() #这一步对网路有要求
# aa<-query(hub,'zea')
# aa$title
# query(hub,'Malus domestica')
bb<-query(hub,"Vitis vinifera")
#bb$title
grape<-hub[['AH85815']]
# length(keys(grape))
# columns(grape)
protein_coding_all<-read.csv("all_protein_coding.csv",header=T)
df<-merge(results_genes_diff,grape_expr_filter@expr$trans,by.x="id",by.y="gene_id")
df1<-merge(df,protein_coding_all,by.x="gene_name",by.y="gene_id")
dim(df1)
gene_ids<- 
  df1$db_xref[!duplicated(df1$db_xref)]
gene_ids<-stringr::str_replace(gene_ids,"GeneID:","")


library(clusterProfiler)
bitr(keys(grape)[2],'ENTREZID',c("REFSEQ","GO",
                                 "ONTOLOGY","GENENAME",
                                 "SYMBOL"),grape)
res = enrichGO(gene_ids, 
               OrgDb=grape, pvalueCutoff=0.05, qvalueCutoff=0.05)
help(package="clusterProfiler")
dotplot(res)

最后的结果是