How To Convert List of Entrez IDs Into Gene Names
January 2, 2025If you have a list of Entrez IDs and want to convert them into gene names, there are various methods using different programming languages and tools. Below are step-by-step instructions for converting Entrez IDs to gene names using multiple approaches.
Method 1: Using Bioconductor in R
- Install Necessary Packages
Install and load theorg.Hs.eg.db
andannotate
libraries for human gene data. - Load Your Entrez ID List Read your list of Entrez IDs into R.
- Convert Entrez IDs to Gene Names Use
getSYMBOL
function to map Entrez IDs to gene symbols. - Save Results Save the gene names to a new file.
Method 2: Using BiomaRt in R
- Install and Load BiomaRt Package
- Set Up BiomaRt Use the
biomaRt
package to map Entrez IDs to gene names. - Convert Entrez IDs to Gene Names Retrieve gene names by providing a list of Entrez IDs.
- Save Results Save the results to a CSV file.
Method 3: Using Perl with org.Hs.eg.db
- Install the Necessary Module Install the necessary Perl modules (
Bio::DB::EntrezGene
) and load them in your script. - Convert Entrez IDs Loop through the Entrez IDs and retrieve the gene names.
Method 4: Using UniProt ID Mapping Tool
- Go to UniProt
- Visit the UniProt website: UniProt ID Mapping Tool.
- Upload Entrez IDs
- Select “Entrez Gene” as your input and “Gene Name” as your output.
- Upload a file with your list of Entrez IDs (CSV format).
- Download Results
- Once the mapping is complete, download the results, which will include the corresponding gene names.
Method 5: Using Online Tool – MatchMiner
- Go to MatchMiner
- Visit MatchMiner.
- Upload Entrez IDs
- Upload a file containing the Entrez IDs.
- Convert and Download Results
- The tool will convert the IDs into corresponding gene names (HUGO gene names).
- Download the results as a CSV file.
Method 6: Using Python (Biopython)
Conclusion
You can convert Entrez IDs into gene names using various programming languages and tools such as R (with Bioconductor and BiomaRt), Perl, Python (Biopython), and online platforms like UniProt and MatchMiner. For large datasets, using Bioconductor or online tools would be most efficient.