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)
```
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