AnnotationForge包构建非模式物种Orgdb包

通常在对物种做GO富集分析时，我们会遇到2种情况模式物种 & 非模式物种；针对模式物种专门的Orgdb包，但是目前针对模式物种的包只有20种,针对非模式职位另一种解决方案是通过AnnotationForge包来创建Orgdb包，本节来介绍如何构建非模式物种的Orgdb来做GO富集分析

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原文链接 AnnotationForge包构建非模式物种Orgdb包

1.准备注释文件

注释文件可以通过eggnog网站上传序列文件获得

2.安装AnnotationForge包

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("AnnotationForge")

3.加载R包

library(tidyverse)
library(AnnotationForge)

4.导入数据

名称由makeOrgPackage函数规定请不要更改

emapper <- read.delim("emapper.annotations.xls") %>%
  dplyr::select(GID=query_name,Gene_Symbol=Preferred_name, 
                GO=GOs,KO=KEGG_ko,Pathway =KEGG_Pathway, 
                OG =X.3,Gene_Name =X.4)
gene_info <- dplyr::select(emapper,GID,Gene_Name) %>%
  dplyr::filter(!is.na(Gene_Name))

gene2go <- dplyr::select(emapper,GID,GO) %>%
  separate_rows(GO, sep = ',', convert = F) %>%
  filter(GO!="",!is.na(GO)) %>% 
  mutate(EVIDENCE = 'A')

5.构建0rgde包

AnnotationForge::makeOrgPackage(gene_info=gene_info,
                                go=gene2go,
                                maintainer='YJA<yanjunan@gmail.com>',
                                author='YJA',
                                version="0.1" ,
                                outputDir=".", 
                                tax_id="59729",
                                genus="M",
                                species="A",
                                goTable = "go")

6.封装R包

终端执行如下命令对生成的org.MA.eg.db进行封装

R CMD build org.MA.eg.db 

{21:27}~/Desktop/GO ➭ R CMD build org.MA.eg.db 
* checking for file ‘org.MA.eg.db/DESCRIPTION’ ... OK
* preparing ‘org.MA.eg.db’:
* checking DESCRIPTION meta-information ... OK
* checking for LF line-endings in source and make files and shell scripts
* checking for empty or unneeded directories
* building ‘org.MA.eg.db_0.1.tar.gz’

7.安装R包

install.packages("org.MA.eg.db_0.1.tar.gz"，repos=NULL)

8.GO富集分析

library(clusterProfiler)
library(org.MA.eg.db)

gene <- read.delim("genes.counts.DESeq2.xls") %>%
  filter(abs(log2FoldChange)>1 & padj < 0.05) %>% 
  pull(id)

ego <- enrichGO(gene=gene,OrgDb=org.MA.eg.db,keyType="GID",
         ont="ALL",qvalueCutoff = 0.05,pvalueCutoff =0.05)
         
ego %>% as.data.frame()

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