human-protein-atlas-database

name: human-protein-atlas-database category: science version: "1.0.0" kind: python description: > Use when you want to retrieve semi-quantitative protein expression and spatial localisation data from the Human Protein Atlas (HPA).

Human Protein Atlas (HPA) Database Integration

This skill provides semi-quantitative protein expression and spatial localisation data from the Human Protein Atlas (HPA). While RNA-seq (e.g., GTEx) tells us if a gene is being transcribed, HPA confirms if the protein product actually exists, where it is located within the cell (e.g. nucleus vs cytoplasm), and its concentration in systemic blood circulation. The data is based on Immunohistochemistry (IHC) across normal human tissues and cancer types.

Prerequisites

1. uv: Read the uv skill and follow its Setup instructions to ensure uv is installed and on PATH.
2. User Notification: If LICENSE_NOTIFICATION.txt does not already exist in this skill directory then (1) prominently notify the user to check the terms at https://www.proteinatlas.org/about/licence, then (2) create the file recording the notification text and timestamp.

When to Use

Use this skill when you need to:

• Map a gene symbol to its Ensembl ID for HPA queries.
• Retrieve the semi-quantitative protein abundance in normal human tissues and cancer types based on IHC staining (High, Medium, Low, or Not Detected).
• Find the specific organelles or subcellular structures where a protein has been localized (e.g., nucleoplasm, mitochondria).
• Check the consistency/agreement between RNA-seq consensus and protein expression levels.
• Search for genes based on specific protein expression criteria (e.g., "elevated in amygdala" or "secreted proteins").

Do NOT use when you need to:

• Query eQTLs, pQTLs, or any variant-level associations. HPA provides wild-type expression data and knows nothing about QTLs.
• Query gene expression in non-human species. HPA is strictly for human proteins.
• Retrieve purely quantitative RNA expression without interest in the protein product (consider using the GTEx skill instead).

Command Selection Guide

Pick the right command on the first try. Match the user's input to the correct subcommand below.

• Map a gene symbol to Ensembl ID: resolve-ensembl-id
• Get tissue protein expression levels: get-tissue-expression
• Get subcellular location of a protein: get-subcellular-location
• Get the full HPA metadata entry for a gene: get-atlas-entry
• Search HPA for genes matching specific criteria: search-hpa

Quick Start

# Map the ERBB2 gene symbol to its Ensembl ID
uv run scripts/hpa_cli.py resolve-ensembl-id ERBB2 --output /tmp/erbb2_id.json

# Get subcellular location by Ensembl ID
uv run scripts/hpa_cli.py get-subcellular-location ENSG00000141736 --output /tmp/erbb2_location.json

All subcommands write JSON to disk. Always save output in the /tmp/ directory. The default output file is /tmp/hpa_output.json if --output is not specified.

Commands

1. resolve-ensembl-id — Gene Symbol → Ensembl ID

Maps a common gene symbol (e.g., "TP53", "ERBB2") to its Ensembl gene ID. HPA endpoints are strictly Ensembl-based.

uv run scripts/hpa_cli.py resolve-ensembl-id TP53 --output /tmp/tp53_id.json

Arguments:

• gene_symbol (positional): The standard gene symbol (e.g., "TP53").
• --output: Output file path (default: /tmp/hpa_output.json).

2. get-tissue-expression — Get Tissue Protein Levels

Returns a list of tissues and their corresponding protein expression levels (High, Medium, Low, or Not Detected) based on IHC staining.

uv run scripts/hpa_cli.py get-tissue-expression ENSG00000130234 \
  --tissues "duodenum,thyroid gland" --output /tmp/tissue_expr.json

Arguments:

• ensembl_id (positional): The Ensembl Gene ID.
• --tissues: Comma-separated list of tissues to filter by (optional, defaults to all available tissues).
• --output: Output file path (default: /tmp/hpa_output.json).

3. get-subcellular-location — Get Subcellular Location

Retrieves the specific organelles or cellular structures where the protein has been localized.

uv run scripts/hpa_cli.py get-subcellular-location ENSG00000141736 \
  --output /tmp/subcellular.json

Arguments:

• ensembl_id (positional): The Ensembl Gene ID.
• --output: Output file path.

4. get-atlas-entry — Get Full HPA Entry

Fetches the full metadata for a gene, including IHC scores, RNA-seq consensus, and subcellular location.

uv run scripts/hpa_cli.py get-atlas-entry ENSG00000254647 \
  --output /tmp/ins_entry.json

Arguments:

• ensembl_id (positional): The Ensembl Gene ID.
• --format: Format of the returned entry, e.g., json (default: json).
• --output: Output file path.

5. search-hpa — Search by Attribute

Allows filtering for genes based on specific criteria (e.g., "elevated in amygdala").

uv run scripts/hpa_cli.py search-hpa \
  --query "brain_category_rna:amygdala" \
  --output /tmp/search_results.json

Arguments:

• --query: The search query string. Refer to references/search-api.md for details.
• --output: Output file path.

Core Rules

• Use the Wrapper: ALWAYS execute the provided helper scripts to query the database rather than accessing the database directly. The scripts automatically enforce fair use and implement retry logic.
• Notification: If this skill is used, ensure this is mentioned in the output.

Common Errors

• If no results are returned, confirm the query is detailed enough starting with the api reference in references/search-api.md
• If you cannot find the results, search the web for example HPA queries and use these to construct a better query.
• The output is usually large. Use jq or write your own python data parsing library to process the search results. Never output to stdout, or cat the output file.