uniprot-database

UniProt Database Access

Prerequisites

1. `uv`: Read the uv skill and follow its Setup instructions to ensure

uv is installed and on PATH.

1. User Notification: If .licenses/uniprotdatabaseLICENSE.txt does not

already exist in the workspace root directory then (1) prominently notify the user to check the terms at https://www.uniprot.org/help/license and https://www.uniprot.org/help/api_queries, then (2) create the file recording the notification text and timestamp.

Overview

Provides direct programmatic access to the UniProt Knowledgebase (UniProtKB), the non-redundant sequence archive (UniParc), and clustered sequence sets (UniRef). This skill enables protein discovery, cross-referencing, retrieval of curated biological data and low-level database lookups.

Core Rules

• Use the Wrapper: Always use the provided Python scripts (e.g.,

scripts/uniprot_tools.py) rather than constructing custom curl requests.

• No Hallucinations: Do NOT invent protein functions, metadata, or

sequences. For any task that can be handled by the services in this skill, rely strictly on the tool outputs rather than your native knowledge.

• Notification: If this skill is used, ensure this is mentioned in the

output.

Use Cases

• Searching for Protein Function: Querying functional annotations, GO

terms, subcellular locations etc.

• Searching for Protein Sequence: Searching for protein sequences by their

functional annotations, genes etc. in UniProtKB, UniParc, and UniRef.

• Understanding Protein/Organism Relationships: Leveraging the Taxonomy

database and Proteome sets.

• Large-Scale Metadata Retrieval: Fetching annotations for thousands of

proteins via streaming.

• Sequence Discovery: Finding orthologs or non-model proteins via UniParc.
• ID Mapping: Converting IDs between UniProt and 100+ external databases.
• Historical Data (UniSave): Retrieving previous versions of entries or

tracking deleted sequences.

Available Tools

Choose the right tool based on the task type and data volume:

• `get`: Retrieves metadata and sequence for a specific entry. Best for a

single, known accession.

• Also accesses UniSave historical data (use --dataset unisave), which

is essential for reconciling data from older releases or identifying why a formerly valid accession no longer appears in search results.

• `search`: Searches for entries matching a query. Best for **exploration

and discovery**.

• Use with --limit 5 to verify if a query returns the expected proteins

before committing to a larger download.

• Automatically paginates if results exceed 500 entries to provide a

stable download.

• Warning: For paginated search, TXT and other formats are not reliable

with --limit as it applies to lines, not entries.

• See

Search Query Fields Documentation.

• `stream`: Streams all matching entries. Best for bulk retrieval of

large datasets (up to 10,000,000 entries).

• Does NOT support --limit; always returns the full result set.
• Use search with --limit if you need a subset.
• `count`: Counts entries matching a query. Best for answering direct

count questions or for initial estimation before running a full search or stream.

• `sparql`: Executes graph queries for complex discovery. Best for

counting, exact sequence matches, and multi-database queries.

• See SPARQL Examples.
• `map`: Converts IDs between UniProt and 100+ databases. Best for ID

mapping tasks.

• See ID Mapping Documentation.
• `search` vs. `map`: Try search first before resorting to map if

not explicitly requested by the user. E.g., an external ID might be searchable in UniParc but fail to map to UniProtKB.

Workflows

Typical Protein Research Workflow

Copy this checklist and track progress:

• [ ] Step 1: Identify target protein(s) and organism(s).
• [ ] Step 2: Search UniProtKB for reviewed entries (reviewed:true).
• [ ] Step 3: If no reviewed entries, search unreviewed or use UniParc for

sequence discovery.

• [ ] Step 4: Map external IDs (e.g., Ensembl, PDB) to UniProt Accessions if

necessary.

• [ ] Step 5: Retrieve functional metadata or sequence in desired format

(JSON, FASTA).

Handling Search Misses (e.g. Gene Search in Non-Model Organisms)

If a direct query (e.g., gene:SYMBOL) fails:

1. Pivot to Protein Name: Search for the common protein name (e.g.,

protein_name:Alpha-crystallin A).

1. Use UniParc: Search the UniParc dataset, which integrates sequences from

across all of life, even if they aren't fully annotated in UniProtKB.

1. Check Orthologs/Canonical: Resolve the Human/Mouse ortholog first to

find the correct naming/mnemonic.

Bulk Retrieval Priorities

[!IMPORTANT] Always prefer `stream` or `sparql` for bulk data. search is suitable for exploration; if results exceed 500 entries, it automatically paginates to provide a stable download.

• Priority 0: `count`: ALWAYS check the result count before running a

search or stream.

• Priority 1: `stream`: The primary method for bulk data retrieval (up to

10M entries). Does NOT support --limit; always returns all results.

• Priority 2: `sparql`: Best for complex filtering and exact matching

during retrieval.

Sequence-Based Search (Exact Match)

[!IMPORTANT] Use SPARQL when searching for a protein by its full amino acid sequence. The REST API /search endpoint does not support direct sequence-string lookups. For any non-exact match use specialized sequence similarity search skills. Use UniParc if you cannot find query in UniProt.

SPARQL Query Pattern (UniProt):

PREFIX up: <http://purl.uniprot.org/core/>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
SELECT ?protein ?name WHERE {
  ?protein a up:Protein ;
           up:sequence/rdf:value "SEQUENCE_HERE" .
  OPTIONAL {
    ?protein up:recommendedName/up:fullName ?name .
  }
}

SPARQL Query Pattern (UniParc):

PREFIX up: <http://purl.uniprot.org/core/>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>

SELECT ?uniparc ?val WHERE {
  GRAPH <http://sparql.uniprot.org/uniparc> {
    ?uniparc a up:Sequence ;
             rdf:value ?val .
    FILTER (?val = "SEQUENCE_HERE")
  }
}

Counting Entries Efficiently

[!IMPORTANT] Use `count` or `SPARQL` for counting entries (e.g., "How many proteins in Human?").

Counting Pattern (Proteins per Organism):

PREFIX up: <http://purl.uniprot.org/core/>
PREFIX taxon: <http://purl.uniprot.org/taxonomy/>
SELECT (COUNT(?protein) AS ?count) WHERE {
  ?protein a up:Protein ;
           up:reviewed true ;
           up:organism taxon:9606 .
}

REST Search Syntax

• No Commas in Lists: Commas are treated as literals. Use capitalized OR

to separate items.

• Grouped: accession:(P12345 OR P67890)
• Repeated: accession:P12345 OR accession:P67890
• Space = AND: E.g., gene:p53 human searches for both.

Example Commands

Below are example commands for each mode of uniprot_tools.py.

Count total number of entries for a given query.

uv run scripts/uniprot_tools.py count "taxonomy_id:9606"

Search for entries.

uv run scripts/uniprot_tools.py search "gene:p53 AND reviewed:true" --limit 5

Retrieve a single entry by accession.

uv run scripts/uniprot_tools.py get P04637

Retrieve Historical/Deleted Entry (UniSave).

uv run scripts/uniprot_tools.py get P04637 --dataset unisave

Stream large result sets for bulk retrieval (returns ALL matched entries, no --limit support).

uv run scripts/uniprot_tools.py stream "taxonomy_id:9606 AND reviewed:true" --format tsv --fields accession,gene_names > human_reviewed.tsv

Map IDs from one database to another.

uv run scripts/uniprot_tools.py map "P04637" --from_db UniProtKB_AC-ID --to_db Gene_Name

Execute graph queries with SPARQL.

uv run scripts/uniprot_tools.py sparql 'PREFIX up: <http://purl.uniprot.org/core/> SELECT ?protein WHERE { ?protein a up:Protein ; up:reviewed true . } LIMIT 5'

Common Mistakes

• Using `name:` instead of `protein_name:`: name: is not a supported

query term, use protein_name: instead.

• Ignoring UniParc: Non-model organisms might only exist in UniParc.
• Confusing Accession with UPI: UniProtKB Accessions (e.g., P04637) are

linked to functional metadata; UniParc IDs (UPI...) are for sequences only. You can find cross-references from UniParc IDs to UniProtKB Accessions using the ID Mapping tool.

• Using UniProtKB-AC as Target in ID Mapping: Use UniProtKB instead.
• Giving up on Complex Queries: If a complex search query fails, try to

use SPARQL instead of giving up.

• Using IDs Without Verifying Meaning: NEVER assume you know the meaning

of an ID (e.g. keyword, GO term, Pfam ID etc.). ALWAYS look up the natural language description/meaning of an ID in UniProt before using it for search to ensure it matches your intended search term.

• Ignoring Citation Noise in Broad Searches: Broad text searches (`search

"term") frequently return false positives (e.g., common maintenance proteins) because UniProt searches full metadata, including publication titles. ALWAYS prefer field-specific filters like ccfunction:` or `proteinname:` for functional discovery.

• Forgetting to Quote Short Search Terms: Short, unquoted terms (e.g.,

lanM) can match substrings in organism names (e.g., Lancefieldella) or other fields. Use quotes and field prefixes (e.g., gene:lanM) to isolate true hits.

• Manipulating Protein Sequences Directly: Always use code and tools for

sequence-based operations. Do not attempt to edit, truncate, or modify protein sequences manually.

• Over-using Search for Bulk Data: DO NOT use search for retrieving

millions of entries if stream or sparql can do the job. Streaming is more efficient for very large datasets. Note that stream has a hard limit of 10,000,000 outputs and does NOT support --limit.

• Forgetting to Check Data Volume: ALWAYS perform a count before running

a search without --limit or before using stream. Unlimited queries can take a long time and consume significant resources if millions of entries are returned.

• Using `--limit` with `stream`: The stream command does NOT support

--limit. If you need a limited number of results, use search with --limit instead.

• Forgetting the License Notice: Do not neglect to state that the UniProt

Database was used and to advise the user to review the licensing terms when presenting results for the first time. Even if the task is concise, this attribution is required in the first response containing UniProt data.

Reference Materials

• SPARQL Examples
• Search Query Fields Documentation
• ID Mapping Documentation
• UniProt Evidence Docs
• Underlying API Endpoints (Used by scripts/uniprot_tools.py):
• get, search, stream, count -> rest.uniprot.org/{dataset}/
• map -> rest.uniprot.org/idmapping/
• sparql -> sparql.uniprot.org/sparql
• get --dataset unisave -> rest.uniprot.org/unisave/