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Ensembl Database

Community
jimmc414
ensembl-database

Query Ensembl genome database REST API for 250+ species. Gene lookups, sequence retrieval, variant analysis, comparative genomics, orthologs, VEP predictions, for genomic research.

Overview

Publisherjimmc414
RepositoryKosmos
Skill nameensembl-database
Stars
585
Forks
105
Bundled files
2
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by jimmc414 on GitHub. Read the source before you install it.

Installation

Install the Ensembl Database AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/jimmc414/Kosmos.git /tmp/Kosmos
mkdir -p .claude/skills
cp -r /tmp/Kosmos/kosmos-claude-scientific-skills/scientific-skills/ensembl-database .claude/skills/ensembl-database
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ensembl Database in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Ensembl Database on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Ensembl Database is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Ensembl Database

Overview

Access and query the Ensembl genome database, a comprehensive resource for vertebrate genomic data maintained by EMBL-EBI. The database provides gene annotations, sequences, variants, regulatory information, and comparative genomics data for over 250 species. Current release is 115 (September 2025).

When to Use This Skill

This skill should be used when:

  • Querying gene information by symbol or Ensembl ID
  • Retrieving DNA, transcript, or protein sequences
  • Analyzing genetic variants using the Variant Effect Predictor (VEP)
  • Finding orthologs and paralogs across species
  • Accessing regulatory features and genomic annotations
  • Converting coordinates between genome assemblies (e.g., GRCh37 to GRCh38)
  • Performing comparative genomics analyses
  • Integrating Ensembl data into genomic research pipelines

Core Capabilities

1. Gene Information Retrieval

Query gene data by symbol, Ensembl ID, or external database identifiers.

Common operations:

  • Look up gene information by symbol (e.g., "BRCA2", "TP53")
  • Retrieve transcript and protein information
  • Get gene coordinates and chromosomal locations
  • Access cross-references to external databases (UniProt, RefSeq, etc.)

Using the ensembl_rest package:

python
from ensembl_rest import EnsemblClient

client = EnsemblClient()

# Look up gene by symbol
gene_data = client.symbol_lookup(
    species='human',
    symbol='BRCA2'
)

# Get detailed gene information
gene_info = client.lookup_id(
    id='ENSG00000139618',  # BRCA2 Ensembl ID
    expand=True
)

Direct REST API (no package):

python
import requests

server = "https://rest.ensembl.org"

# Symbol lookup
response = requests.get(
    f"{server}/lookup/symbol/homo_sapiens/BRCA2",
    headers={"Content-Type": "application/json"}
)
gene_data = response.json()

2. Sequence Retrieval

Fetch genomic, transcript, or protein sequences in various formats (JSON, FASTA, plain text).

Operations:

  • Get DNA sequences for genes or genomic regions
  • Retrieve transcript sequences (cDNA)
  • Access protein sequences
  • Extract sequences with flanking regions or modifications

Example:

python
# Using ensembl_rest package
sequence = client.sequence_id(
    id='ENSG00000139618',  # Gene ID
    content_type='application/json'
)

# Get sequence for a genomic region
region_seq = client.sequence_region(
    species='human',
    region='7:140424943-140624564'  # chromosome:start-end
)

3. Variant Analysis

Query genetic variation data and predict variant consequences using the Variant Effect Predictor (VEP).

Capabilities:

  • Look up variants by rsID or genomic coordinates
  • Predict functional consequences of variants
  • Access population frequency data
  • Retrieve phenotype associations

VEP example:

python
# Predict variant consequences
vep_result = client.vep_hgvs(
    species='human',
    hgvs_notation='ENST00000380152.7:c.803C>T'
)

# Query variant by rsID
variant = client.variation_id(
    species='human',
    id='rs699'
)

4. Comparative Genomics

Perform cross-species comparisons to identify orthologs, paralogs, and evolutionary relationships.

Operations:

  • Find orthologs (same gene in different species)
  • Identify paralogs (related genes in same species)
  • Access gene trees showing evolutionary relationships
  • Retrieve gene family information

Example:

python
# Find orthologs for a human gene
orthologs = client.homology_ensemblgene(
    id='ENSG00000139618',  # Human BRCA2
    target_species='mouse'
)

# Get gene tree
gene_tree = client.genetree_member_symbol(
    species='human',
    symbol='BRCA2'
)

5. Genomic Region Analysis

Find all genomic features (genes, transcripts, regulatory elements) in a specific region.

Use cases:

  • Identify all genes in a chromosomal region
  • Find regulatory features (promoters, enhancers)
  • Locate variants within a region
  • Retrieve structural features

Example:

python
# Find all features in a region
features = client.overlap_region(
    species='human',
    region='7:140424943-140624564',
    feature='gene'
)

6. Assembly Mapping

Convert coordinates between different genome assemblies (e.g., GRCh37 to GRCh38).

Important: Use https://grch37.rest.ensembl.org for GRCh37/hg19 queries and https://rest.ensembl.org for current assemblies.

Example:

python
from ensembl_rest import AssemblyMapper

# Map coordinates from GRCh37 to GRCh38
mapper = AssemblyMapper(
    species='human',
    asm_from='GRCh37',
    asm_to='GRCh38'
)

mapped = mapper.map(chrom='7', start=140453136, end=140453136)

API Best Practices

Rate Limiting

The Ensembl REST API has rate limits. Follow these practices:

  1. Respect rate limits: Maximum 15 requests per second for anonymous users
  2. Handle 429 responses: When rate-limited, check the Retry-After header and wait
  3. Use batch endpoints: When querying multiple items, use batch endpoints where available
  4. Cache results: Store frequently accessed data to reduce API calls

Error Handling

Always implement proper error handling:

python
import requests
import time

def query_ensembl(endpoint, params=None, max_retries=3):
    server = "https://rest.ensembl.org"
    headers = {"Content-Type": "application/json"}

    for attempt in range(max_retries):
        response = requests.get(
            f"{server}{endpoint}",
            headers=headers,
            params=params
        )

        if response.status_code == 200:
            return response.json()
        elif response.status_code == 429:
            # Rate limited - wait and retry
            retry_after = int(response.headers.get('Retry-After', 1))
            time.sleep(retry_after)
        else:
            response.raise_for_status()

    raise Exception(f"Failed after {max_retries} attempts")

Installation

Python Package (Recommended)

bash
uv pip install ensembl_rest

The ensembl_rest package provides a Pythonic interface to all Ensembl REST API endpoints.

Direct REST API

No installation needed - use standard HTTP libraries like requests:

bash
uv pip install requests

Resources

references/

  • api_endpoints.md: Comprehensive documentation of all 17 API endpoint categories with examples and parameters

scripts/

  • ensembl_query.py: Reusable Python script for common Ensembl queries with built-in rate limiting and error handling

Common Workflows

Workflow 1: Gene Annotation Pipeline

  1. Look up gene by symbol to get Ensembl ID
  2. Retrieve transcript information
  3. Get protein sequences for all transcripts
  4. Find orthologs in other species
  5. Export results

Workflow 2: Variant Analysis

  1. Query variant by rsID or coordinates
  2. Use VEP to predict functional consequences
  3. Check population frequencies
  4. Retrieve phenotype associations
  5. Generate report

Workflow 3: Comparative Analysis

  1. Start with gene of interest in reference species
  2. Find orthologs in target species
  3. Retrieve sequences for all orthologs
  4. Compare gene structures and features
  5. Analyze evolutionary conservation

Species and Assembly Information

To query available species and assemblies:

python
# List all available species
species_list = client.info_species()

# Get assembly information for a species
assembly_info = client.info_assembly(species='human')

Common species identifiers:

  • Human: homo_sapiens or human
  • Mouse: mus_musculus or mouse
  • Zebrafish: danio_rerio or zebrafish
  • Fruit fly: drosophila_melanogaster

Additional Resources

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Ensembl Database AI skill do?

Query Ensembl genome database REST API for 250+ species. Gene lookups, sequence retrieval, variant analysis, comparative genomics, orthologs, VEP predictions, for genomic research.

Why use Ensembl Database on TypingMind?

Because you install it once and use it with any model. Ensembl Database is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Ensembl Database in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jimmc414/Kosmos/tree/master/kosmos-claude-scientific-skills/scientific-skills/ensembl-database. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Ensembl Database?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Ensembl Database?

As many as you like. As long as a model supports skills, you can use Ensembl Database with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ensembl Database AI skill free?

It is published on GitHub by jimmc414. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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