Convert to ENTREZGENE

Question

[sansan_96]

Hello everyone, I have a list of differential corn genes by their symbol and I would like to know if there is a package that helps me convert the symbols to ENTREZGENE, let's say something like this:

initial                                    converted
Zm00001eb000370             103630483
Zm00001eb000450             100285831

I have my list something like this:

colnames(diff_genes)[1] <- "genes"
diff_genes <- diff_genes[, c("genes", "log2FoldChange")]
head(diff_genes)
# A tibble: 6 × 2
  genes           log2FoldChange
  <chr>                    <dbl>
1 Zm00001eb000370           2.58
2 Zm00001eb000450           1.12
3 Zm00001eb000790           1.25
4 Zm00001eb000850           8.84
5 Zm00001eb000900           1.59
6 Zm00001eb001080           2.78

Answer 1

[Kevin Blighe]

Hello,

There is an annotation table for corn / maize (Zea mays) at ensembl, accessible via biomaRt.

What I would do is to first pull a complete annotation table from ensembl, which can actually be quciker than doing specific lookups:

require(biomaRt)
mart <- useMart('plants_mart', 'zmays_eg_gene',
  host = 'https://plants.ensembl.org')
annot <- getBM(
  attributes = c('ensembl_gene_id', 'entrezgene_id', 'gene_biotype'),
  mart = mart)

head(annot)
  ensembl_gene_id entrezgene_id    gene_biotype
1 Zm00001eb442760            NA misc_non_coding
2 Zm00001eb393960            NA misc_non_coding
3 Zm00001eb113450            NA misc_non_coding
4 Zm00001eb437000            NA misc_non_coding
5 Zm00001eb441340            NA misc_non_coding
6 Zm00001eb437720            NA misc_non_coding

head(annot[!is.na(annot$entrezgene_id),])
     ensembl_gene_id entrezgene_id   gene_biotype
4542 Zm00001eb321680     100502366 protein_coding
4543 Zm00001eb323640     100501883 protein_coding
4545 Zm00001eb080260     100381510 protein_coding
4547 Zm00001eb281360     100275601 protein_coding
4551 Zm00001eb155150        542087 protein_coding
4552 Zm00001eb144800     100277214 protein_coding

Then, you can do a simple lookup locally like this:

```r
lookup <- data.frame(genes = c('Zm00001eb000370', 'Zm00001eb000450'))
merge(
  x = as.data.frame(lookup),
  y =  annot,
  by.y = 'ensembl_gene_id',
  all.x = TRUE,
  by.x = 'genes')
            genes entrezgene_id   gene_biotype
1 Zm00001eb000370     103630483 protein_coding
2 Zm00001eb000450     100285831 protein_coding

Using your own diff_genes variable, this could be run as:

merge(
  x = as.data.frame(diff_genes),
  y =  annot,
  by.y = 'ensembl_gene_id',
  all.x = TRUE,
  by.x = 'genes')

You can check for further attributes that you may want to retrieve from ensembl via: listAttributes(mart)

Kevin

[sansan_96]

Kevin, thank you very much for your valuable help. I am using this output for a KEGG analysis, although I recover very few genes for my analysis (6) of the more than 1000 that I enter. So I was wondering if you could guide me on how to do a GO enrichment analysis. I was trying with clusterProfiler but there is no support for corn in organism = "org.XXX.eg.db", could you guide me?

I will greatly appreciate your help.

#Usando tabla de diferenciales :
list_diff<-merge(
  x = as.data.frame(diff_genes),
  y =  annot,
  by.y = 'ensembl_gene_id',
  all.x = TRUE,
  by.x = 'genes')



list_diff_final<-head(list_diff[!is.na(list_diff$entrezgene_id),])
list_diff_final
#write.csv(list_diff_final, "final.csv")



#Extraer los genes y los valores de expresión (fold change) de list_diff_final
genes <- list_diff_final$entrezgene_id
fold_change <- list_diff_final$log2FoldChange

#Asignar los nombres de genes para cada resultado de expresión
names(fold_change)<-genes

#Resultado final
fold_change

103630483 100285831 107403162 107548113 100286177 100384769 
 2.580437  1.118575  8.837193  1.594464  1.999971  1.407178 

#write.csv(fold_change, "punto.csv")

# Obtener las enriquecimientos KEGG usando los datos de la tabla mapeada
KEGG_genes <- enrichKEGG(gene = genes, organism = "zma", pvalueCutoff = 0.05)

# Generar el gráfico 
dotplot(KEGG_genes)

This script has worked for GO enrichment in arabidopsis but I have not been able to adapt it for maize:

ora_analysis_bp <- enrichGO(
    gene = diff_arabidopsis_genes_annotated$entrezgene_id,
    universe = all_arabidopsis_genes_annotated$entrezgene_id,
    OrgDb = org.At.tair.db,
    keyType = "ENTREZID",
    ont = "BP",
    pAdjustMethod = "BH",
    qvalueCutoff = 0.05,
    readable = TRUE,
    pool = FALSE
)

ora_analysis_bp_simplified <- clusterProfiler::simplify(ora_analysis_bp)