Using internet search to find a way to get gene symbols to our gene bank accession IDs. So we can add this database of genes to our Tableau collection of EBV associated and non-associated pathologies for comparison.

library(rmarkdown)
Data <- read.csv("autismDB_41kgenes_notSamples_needs_symbolID.csv", header=T, na.strings=c("","NA"," ") )#41472X8

paged_table(Data)
data <- Data[!duplicated(Data$GB_ACC),] #39930X8

#remove that 1 NA as well
data2 <- data[!is.na(data$GB_ACC),] #39929X8

Here are internet instructions:

AI Overview

To make a list of gene synonyms from gene accession IDs in R, use Bioconductor’s organism-specific annotation packages (e.g., org.Hs.eg.db for humans). The mapIds() function allows you to directly query NCBI/RefSeq/Ensembl accessions to return ALIAS (synonym) values.Step-by-Step InstructionsInstall and Load Packages:Ensure you have the core AnnotationDbi and your species-specific package (e.g., org.Hs.eg.db) installed via Bioconductor.Rif

(!requireNamespace(“BiocManager”, quietly = TRUE)) install.packages(“BiocManager”)

#BiocManager::install("org.Hs.eg.db")
library(org.Hs.eg.db)
## Loading required package: AnnotationDbi
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library(AnnotationDbi)

Define Accession IDs and Keytype:Because accession numbers (e.g., NM_001369 or NP_001618) often include version numbers, it is best practice to strip them (e.g., using sub(“\..*“,”“, …)), as the database stores base accessions.Map and Retrieve Synonyms:Synonyms are stored under the”ALIAS” keytype. Because one accession ID can have multiple synonyms, use multivals = “list” to return a list of synonyms for each query.R#

  1. Your example accession IDs (stripping version numbers if necessary)

accession_ids <- c(“NM_004448”, “NM_005228”) clean_accessions <- sub(“\..*“,”“, accession_ids)

accession_ids <- data2$GB_ACC

clean_accessions <- sub("\\..*", "", accession_ids)

2. Map accessions to ALIAS (synonyms)

gene_synonyms <- mapIds(org.Hs.eg.db, keys = clean_accessions, column = “ALIAS”, keytype = “REFSEQ”, # or “ACCNUM”, “ENSEMBL”, etc. multivals = “list”)

gene_synonyms <- mapIds(org.Hs.eg.db,
                        keys = clean_accessions,
                        column = "ALIAS",
                        keytype = "ACCNUM", # or "ACCNUM", "ENSEMBL", etc.
                        multivals = "list")
## 'select()' returned 1:many mapping between keys and columns

3. View the results

print(gene_synonyms)

geneID <- unlist(gene_synonyms) #39929 length of string

str(geneID[1:50])
##  Named chr [1:50] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA ...
##  - attr(*, "names")= chr [1:50] "M86720" "AA486138" "N51018" "H65481" ...

Tips for Best ResultsCheck Your Keytype: The most common accession types are REFSEQ (for RNA/protein accessions), ACCNUM (GenBank accessions), and ENSEMBL. Use keytypes(org.Hs.eg.db) to view all supported accession types for your organism.

No Matches? If your accessions don’t map, check that your keytype matches the exact format of your IDs, and remove any .1, .2, etc., suffixes at the end of the accession string.For a full list of mapping keys available for your species, view the official Bioconductor org.Hs.eg.db documentation.

The unlist() removes null values, we probably have a null value in the data that the na.strings parameter reading it in didn’t find.

data2$Gene_ID <- geneID

paged_table(data2[1:100,])
data3 <- data2[!is.na(data2$Gene_ID),] #only 538 genes remained

paged_table(data3)

This is our table for Tableau. Lets write it out to csv.

write.csv(data2, 'dataMergedSymbols_NAs.csv', row.names=F)
write.csv(data3,"dataMerged_Symbols_GBacc_538X9.csv", row.names=F)

This documet of the 538 genes is available here.