HGNC Gene Symbols Scraper

HGNC Gene Symbols Scraper

Scrape HGNC gene symbols and IDs from the official HUGO Gene Nomenclature Committee database. Get approved symbols, previous symbols, aliases, locus types, and cross-references to Entrez, UniProt, and Ensembl. No API key. Export to CSV, JSON, Excel, or XML.

HGNC is the worldwide authority that assigns standardized names to human genes. Their database holds every approved gene symbol, alias, previous symbol, and cross-reference, but the web interface is built for one-at-a-time lookups. This Actor reads the public HGNC search and fetch endpoints directly, so you can pull hundreds or thousands of gene records in a single run and get them back as clean, flat rows.

Who uses it	What they scrape HGNC for
Bioinformaticians	Building a local gene reference table for pipeline annotation
Clinical genomics analysts	Resolving gene symbols to canonical HGNC IDs for variant reporting
Data engineers	Syncing HGNC gene metadata into a warehouse or knowledge graph
Researchers	Fetching approved symbols and aliases for a gene list before publication

What it does

This Actor collects HGNC gene records by approved symbol, HGNC ID, Entrez Gene ID, UniProt accession, or free-text search, and returns each gene as one flat row.

• 🔎 Search mode: free-text search across all HGNC fields, returns every matching gene record.
• 🧬 Fetch by approved symbol: look up one or many symbols like BRCA1, TP53, or EGFR in a single run.
• 🆔 Fetch by HGNC ID: resolve numeric IDs or HGNC: prefixed IDs to full gene records.
• 🔗 Fetch by Entrez Gene ID: map NCBI Gene IDs to HGNC approved symbols and metadata.
• 🧪 Fetch by UniProt accession: convert UniProt accessions like P38398 to HGNC gene records.

Results export to CSV, JSON, Excel, or XML, or straight from the API.

What you can do with HGNC data

🧬 Build a gene reference table.

A bioinformatician runs the Actor with a list of 500 gene symbols and gets back approved symbols, aliases, and cross-references to feed a variant annotation pipeline.

🩺 Standardize clinical reports.

A clinical analyst fetches HGNC records for a panel of cancer genes to ensure every report uses the current approved symbol and HGNC ID.

🔁 Sync gene metadata.

A data engineer schedules a weekly run to pull all HGNC records and upsert them into a warehouse table that other teams query.

📚 Resolve legacy identifiers.

A researcher pastes a list of old gene symbols and gets back the current approved symbol plus previous symbols, so a literature search covers all names.

Why choose this scraper

	What you get
Approved symbols	The current official HGNC symbol for every gene, exactly as the committee maintains it
Aliases and previous symbols	Every alternative name and retired symbol, so you can map legacy data to current nomenclature
Cross-references	Entrez Gene IDs, UniProt accessions, Ensembl gene IDs, and more, all in one row
Locus type and location	Gene type, chromosomal location, and locus group for filtering and annotation

How it compares

No other Store actor targets HGNC the same way, so the honest comparison is with the alternatives teams actually weigh.

	HGNC Gene Symbols Scraper	Build it in-house	By hand
Setup	Run it now, zero config	Days of engineering	None, but hours per pull
When HGNC changes	Maintained for you	You fix it	You re-learn the page
Proxies, retries, anti-bot	Built in	Your problem	Browser only
Output	Fixed JSON schema, CSV/Excel export	Whatever you build	Copy-paste
Cost	Pay per result	Engineering time	Analyst hours

Configure the run

Drive the Actor from a single mode and a list of values. Each value triggers one lookup, and every matching gene is returned as a row. Set a maximum number of genes to cap the run. The Input tab lists every parameter.

A first run with the defaults:

{
  "maxItems": 10,
  "mode": "fetchBySymbol",
  "values": [
    "BRCA1",
    "TP53",
    "EGFR",
    "MYC",
    "AKT1"
  ]
}

A larger pull:

{
  "maxItems": 200,
  "mode": "fetchBySymbol",
  "values": [
    "BRCA1",
    "TP53",
    "EGFR",
    "MYC",
    "AKT1"
  ]
}

Pricing

Pay-per-result: $0.02 per result collected. You pay only for the results written to your dataset.

Results collected	Approximate cost
100 results	$2.00
1,000 results	$20.00
10,000 results	$200.00

New Apify accounts start with $5 in free credit.

Free users

Free-plan runs return up to 10 results as a preview. Upgrade your Apify plan to collect up to 1,000,000 results per run.

Run it

1. Create a free Apify account with $5 in credit.
2. Open the HGNC Gene Symbols Scraper.
3. Set your inputs and any filters, then click Start.
4. Export the results as CSV, Excel, JSON, or XML from the Dataset tab.

Run it programmatically through the Apify API (run-sync-get-dataset-items) or the ApifyClient for JavaScript and Python.

Use with AI agents (MCP)

Give an AI agent live access to HGNC through the Model Context Protocol. Add the Actor to Claude, Cursor, or any MCP client:

claude mcp add --transport http apify "https://mcp.apify.com?tools=parseforge/hgnc-gene-symbols-scraper"

Then prompt it in plain language to run the scraper and read back the results.

Troubleshooting

Why am I getting no results?

Check that the mode matches the values you supplied. For example, fetchBySymbol expects approved symbols, not Entrez IDs. Also verify the values are spelled correctly and do not include extra spaces.

Why did the run stop before processing all my values?

The maximum genes input caps the total number of records collected. Increase it or remove the cap to process every value.

Why is a gene I searched for missing from the results?

HGNC search may rank results differently than expected. Try fetching by a specific identifier such as the approved symbol or HGNC ID to get an exact match.

Why do I see an error for a UniProt accession?

Make sure the accession is a valid human UniProt entry. Some accessions are obsolete or belong to other species, and HGNC will not return a record for them.

FAQ

Question	Answer
What is HGNC?	HGNC is the HUGO Gene Nomenclature Committee, the body that assigns unique, standardized names and symbols to human genes. Their database is the authoritative source for gene nomenclature.
Do I need an API key or login?	No. This Actor reads the public HGNC search and fetch endpoints directly. You only need an Apify account to run it.
Can I fetch more than one gene per run?	Yes. The values input accepts a list, so you can supply many symbols, IDs, or accessions in one run. Each value triggers one lookup.
What identifiers can I use to fetch a gene?	You can fetch by approved symbol, HGNC ID, Entrez Gene ID, or UniProt accession. You can also use search mode to query across all fields.
Does the Actor return aliases and previous symbols?	Yes. Each gene record includes aliases and previous symbols as returned by HGNC, so you can map legacy names to the current approved symbol.
What cross-references are included?	The Actor returns cross-references such as Entrez Gene ID, UniProt accession, Ensembl gene ID, and others that HGNC provides for each gene.
Can I limit how many genes are collected?	Yes. Set the maximum genes input to cap the number of records per run. The Actor stops after reaching that count.
What output formats are supported?	You can export the dataset as CSV, JSON, Excel, or XML from the Apify platform.
Is the data live or cached?	The Actor queries HGNC at run time, so you get the current records as maintained by the committee.
Can I schedule this Actor to run regularly?	Yes. Use Apify schedules to run it daily, weekly, or on any cron expression, and store the results in a dataset or key-value store.

Related actors

Browse the full ParseForge collection for more scrapers.

🆘 Need help? Email parseforge@protonmail.com with your run ID, your input, and what you expected.

⚠️ Disclaimer. This Actor is unofficial and is not affiliated with, endorsed by, or sponsored by HUGO Gene Nomenclature Committee. It collects only publicly available data. You are responsible for using the collected data in compliance with the source's terms of service and applicable data-protection laws, including GDPR, CCPA, and PIPL. Do not use it to collect personal data unlawfully.