Uniprot Database
Google DeepMindOfficialPublished by Google DeepMind
- Skill
- Tool integration
- Science and research
- Apache-2.0
- Low risk
- Has scripts
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
The UniProt Database skill provides a robust interface for interacting with the UniProt Knowledgebase (UniProtKB), UniParc, and UniRef. It enables researchers and developers to perform protein discovery, retrieve curated biological annotations, map identifiers across numerous external databases, and conduct bulk data retrieval. The skill is designed for programmatic access, ensuring reliable data extraction for bioinformatics workflows while adhering to UniProt's licensing and usage guidelines.
What it does
- Retrieves detailed protein metadata, functional annotations, and sequences.
- Maps protein identifiers between UniProt and over 100 external biological databases.
- Performs complex queries and data discovery using SPARQL.
- Supports bulk data retrieval through streaming for large datasets.
- Provides access to historical data entries via UniSave.
- Enables exploration of protein-organism relationships and taxonomy.
When to use it
- When you need to fetch specific protein information by accession number.
- When performing large-scale analysis requiring bulk retrieval of protein data.
- When you need to convert identifiers from external databases to UniProt accessions.
- When searching for proteins based on specific functional, gene, or taxonomic criteria.
- When investigating historical versions of protein entries.
Advantages
- Direct programmatic access to authoritative biological data.
- Efficient handling of both single-entry lookups and large-scale data streams.
- Powerful filtering capabilities using field-specific search syntax.
- Integrated support for complex graph queries via SPARQL.
- Standardized workflow for ID mapping across diverse bioinformatics resources.
Usage examples
- “Find all reviewed human proteins related to the p53 gene.”
- The agent uses the search tool with the query 'gene:p53 AND reviewed:true AND taxonomy_id:9606' to return the relevant UniProtKB entries.
- “Map the UniProt accession P04637 to its corresponding Gene Name.”
- The agent uses the map tool with '--from_db UniProtKB_AC-ID --to_db Gene_Name' to retrieve the associated gene name.
- “How many reviewed proteins are there for the organism with taxonomy ID 9606?”
- The agent executes the count tool with 'taxonomy_id:9606 AND reviewed:true' to provide the exact number of entries.
- “Retrieve the full sequence for UniProt accession P04637.”
- The agent uses the get tool for P04637 to fetch the metadata and sequence information.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/google-deepmind/uniprot-database/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip uniprot-database.zip -d .agents/skills
Documentation by Google DeepMind
UniProt Database Access
Prerequisites
uv: Read theuvskill and follow its Setup instructions to ensureuvis installed and on PATH.User Notification: If .licenses/uniprot_database_LICENSE.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 5to 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
--limitas it applies to lines, not entries.
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
searchwith--limitif you need a subset.
count: Counts entries matching a query. Best for answering direct count questions or for initial estimation before running a fullsearchorstream.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.searchvs.map: Trysearchfirst before resorting tomapif 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:
Pivot to Protein Name: Search for the common protein name (e.g.,
protein_name:Alpha-crystallin A).Use UniParc: Search the UniParc dataset, which integrates sequences from across all of life, even if they aren't fully annotated in UniProtKB.
Check Orthologs/Canonical: Resolve the Human/Mouse ortholog first to find the correct naming/mnemonic.
Bulk Retrieval Priorities
[!IMPORTANT] Always prefer
streamorsparqlfor bulk data.searchis 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 asearchorstream.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
/searchendpoint 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
countorSPARQLfor 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
ORto separate items.Grouped:
accession:(P12345 OR P67890)Repeated:
accession:P12345 OR accession:P67890
Space = AND: E.g.,
gene:p53 humansearches 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 5Retrieve a single entry by accession.
uv run scripts/uniprot_tools.py get P04637Retrieve Historical/Deleted Entry (UniSave).
uv run scripts/uniprot_tools.py get P04637 --dataset unisaveStream 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.tsvMap IDs from one database to another.
uv run scripts/uniprot_tools.py map "P04637" --from_db UniProtKB_AC-ID --to_db Gene_NameExecute 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 ofprotein_name::name:is not a supported query term, useprotein_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
UniProtKBinstead.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 likecc_function:orprotein_name: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
searchfor retrieving millions of entries ifstreamorsparqlcan do the job. Streaming is more efficient for very large datasets. Note thatstreamhas a hard limit of 10,000,000 outputs and does NOT support--limit.Forgetting to Check Data Volume: ALWAYS perform a
countbefore running asearchwithout--limitor before usingstream. Unlimited queries can take a long time and consume significant resources if millions of entries are returned.Using
--limitwithstream: Thestreamcommand does NOT support--limit. If you need a limited number of results, usesearchwith--limitinstead.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
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/sparqlget --dataset unisave->rest.uniprot.org/unisave/
Files
- ATTRIBUTION.mdAdded by AutoGineer914 B
- LICENSELicence file added by AutoGineer11.1 KiB
- SKILL.md12.0 KiB
- references/citation.bib1.4 KiB
- references/id_mapping_databases.md7.1 KiB
- references/search_query_fields.md18.0 KiB
- references/sparql_examples.md2.8 KiB
- scripts/uniprot_tools.pyScript14.1 KiB
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Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
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Needs: Needs Python
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Attribution and licence
- Published by
- Google DeepMind
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- Google DeepMind (Official)
- Source
- google-deepmind/science-skills · skills/uniprot_database · commit 4de6786
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
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