Biomart queries

Version Compatibility

Reference examples tested with: pybiomart 0.9+, R biomaRt 2.58+ (Bioconductor); Ensembl BioMart (release 110+)

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show pybiomart
• R: packageVersion('biomaRt')

The BioMart XML query format is stable across Ensembl releases; the underlying mart names and attribute IDs can change between Ensembl releases. For published work, pin the Ensembl release via useEnsembl(version=110).

BioMart Queries

"Bulk-convert IDs / pull coordinate tables / extract ortholog wide tables" -> BioMart is the right answer for any Ensembl-rooted query producing >5,000 rows. It is a separate service from the Ensembl REST API, with separate rate behavior and a different query model (XML-based, batch-oriented). For one-off lookups (<100 records), Ensembl REST is more convenient; for bulk anything, BioMart wins.

The single most important fact: BioMart returns a flat table from a single query. There is no per-record loop, no rate-limit cascade, no async polling. One XML query in; one TSV out.

• Python: pybiomart (https://github.com/jrderuiter/pybiomart) is the lightest client
• R: biomaRt Bioconductor (Durinck et al. 2009 Nat Protoc 4:1184) is the canonical client
• CLI: curl against the XML endpoint works but is rarely used directly
• Web: https://www.ensembl.org/biomart/martview for interactive query design

Installation

pip install pybiomart pandas
# R:
# BiocManager::install('biomaRt')

BioMart hierarchy

Level	Examples
Mart	ENSEMBL_MART_ENSEMBL (genes), ENSEMBL_MART_SNP (variants), ENSEMBL_MART_MOUSE (mouse-specific)
Dataset	hsapiens_gene_ensembl, mmusculus_gene_ensembl, etc. (per species)
Attribute	Fields to return: ensembl_gene_id, external_gene_name, chromosome_name, etc.
Filter	Constraints on the query: chromosome_name = 17, biotype = protein_coding, etc.

A query is: pick a mart, pick a dataset, list attributes to return, list filters to constrain. BioMart returns a single TSV.

Discovery:

from pybiomart import Server
server = Server(host='http://www.ensembl.org')
print(server.marts)                                          # list marts
mart = server['ENSEMBL_MART_ENSEMBL']
print(mart.datasets)                                         # list datasets (species)
ds = mart['hsapiens_gene_ensembl']
print(ds.attributes)                                         # list attributes
print(ds.filters)                                            # list filters

Decision matrix: BioMart vs Ensembl REST

Question	BioMart	Ensembl REST
Bulk ID mapping (>5000 IDs)	yes (1 query)	rate-limited cascade
Single-gene lookup	overkill	yes
Coordinate tables for thousands of genes	yes	rate-limited
Ortholog wide-table across species	yes (multi-species mart)	per-gene loop
VEP variant annotation	no	yes (or local VEP)
Sequence retrieval	partial	yes
Real-time	no (batch)	yes (per-record)
Reproducibility (version pin)	useEnsembl(version=110)	archive URL e110.rest.ensembl.org

For >5K rows, BioMart is the right tool. For real-time per-record lookups, REST.

Common attribute selectors

Attribute	Returns
ensembl_gene_id	Stable Ensembl Gene ID
ensembl_gene_id_version	With .N version suffix
external_gene_name	HGNC symbol (or species-equivalent)
hgnc_id, hgnc_symbol	HGNC permanent ID and symbol
entrezgene_id	NCBI Gene ID
refseq_mrna, refseq_peptide	RefSeq accessions
uniprotswissprot, uniprotsptrembl	UniProt accessions
chromosome_name, start_position, end_position, strand	Gene coordinates
transcript_count, exon_count	Counts
biotype	protein_coding, lncRNA, miRNA, etc.
description	Free-text gene description
go_id, name_1006, namespace_1003	GO term ID, name, namespace

Common filter selectors

Filter	Constraint
ensembl_gene_id	List of Gene IDs
external_gene_name	List of symbols
entrezgene_id	List of NCBI Gene IDs
chromosome_name	One or more chromosomes
start / end	Coordinate range
biotype	One or more biotypes
with_<source>	Boolean: has cross-ref to <source> (e.g. with_hpa = has Human Protein Atlas)

Code patterns

Bulk ID mapping: Ensembl Gene -> HGNC + RefSeq + UniProt

Goal: Convert 5,000 Ensembl Gene IDs to HGNC symbols, RefSeq mRNA accessions, and UniProt accessions in one query.

Approach: pybiomart query with three attributes; ID list as a filter; returns one TSV.

Reference (pybiomart 0.9+, Ensembl release 110+):

from pybiomart import Server
import pandas as pd

server = Server(host='http://www.ensembl.org')
mart = server['ENSEMBL_MART_ENSEMBL']
ds = mart['hsapiens_gene_ensembl']

ensembl_ids = ['ENSG00000139618', 'ENSG00000141510', 'ENSG00000171862']  # ...up to 5K+

df = ds.query(
    attributes=['ensembl_gene_id', 'external_gene_name', 'hgnc_id',
                'refseq_mrna', 'uniprotswissprot'],
    filters={'ensembl_gene_id': ensembl_ids},
)
print(df.head())
# One row per (gene, cross-ref) pair; genes with multiple RefSeq mRNAs get multiple rows.

Pull gene coordinate table for a chromosome

df = ds.query(
    attributes=['ensembl_gene_id', 'external_gene_name', 'chromosome_name',
                'start_position', 'end_position', 'strand', 'biotype'],
    filters={'chromosome_name': '17', 'biotype': 'protein_coding'},
)
print(f'{len(df)} protein-coding genes on chr17')

Bulk ortholog wide-table (human <-> mouse <-> zebrafish)

Goal: One TSV with human Ensembl ID, mouse ortholog Ensembl ID, zebrafish ortholog Ensembl ID per row.

Approach: Ortholog attributes from the human mart query both species' orthologs.

df = ds.query(
    attributes=['ensembl_gene_id', 'external_gene_name',
                'mmusculus_homolog_ensembl_gene', 'mmusculus_homolog_orthology_type',
                'drerio_homolog_ensembl_gene', 'drerio_homolog_orthology_type'],
    filters={'chromosome_name': '17'},
)
# pybiomart columns use the mart display names, which can vary across releases.
# Resolve column names defensively rather than hardcoding strings:
mouse_type_col = next(c for c in df.columns if 'Mouse' in c and 'type' in c)
zebra_type_col = next(c for c in df.columns if 'Zebrafish' in c and 'type' in c)
df_one2one = df[(df[mouse_type_col] == 'ortholog_one2one') &
                (df[zebra_type_col] == 'ortholog_one2one')]
print(f'{len(df_one2one)} 1:1 orthologs across all three species on chr17')

GO term annotation for a gene set

df = ds.query(
    attributes=['ensembl_gene_id', 'external_gene_name',
                'go_id', 'name_1006', 'namespace_1003'],
    filters={'external_gene_name': ['TP53', 'BRCA1', 'MYC', 'EGFR']},
)
# Long format: one row per (gene, GO term) pair

Version-pinned query (R biomaRt)

# Reference: Bioconductor biomaRt 2.58+ | Verify API if version differs
library(biomaRt)

# Pin to release 110 for reproducibility
ensembl <- useEnsembl(biomart='genes', dataset='hsapiens_gene_ensembl', version=110)

# Or via host URL (for older or specific assemblies)
# ensembl <- useMart('ENSEMBL_MART_ENSEMBL',
#                     dataset='hsapiens_gene_ensembl',
#                     host='https://nov2020.archive.ensembl.org')

df <- getBM(
    attributes = c('ensembl_gene_id', 'external_gene_name', 'entrezgene_id',
                   'uniprotswissprot', 'refseq_mrna'),
    filters = 'ensembl_gene_id',
    values = c('ENSG00000139618', 'ENSG00000141510'),
    mart = ensembl
)
head(df)

Discover attributes / filters programmatically

# What attributes are available?
attrs = ds.attributes
ortho_attrs = [a for a in attrs if 'homolog' in a]
print(f'{len(ortho_attrs)} ortholog attributes; first 5: {ortho_attrs[:5]}')

# What filters?
filts = ds.filters
chrom_filts = [f for f in filts if 'chrom' in f]

Failure modes

Trying to pull >100K rows in one query

• Trigger: Query without any filter (e.g. all attributes for the whole human genome).
• Mechanism: BioMart times out or truncates on very large queries.
• Symptom: Empty or partial result.
• Fix: Chunk by chromosome; combine results client-side.

No version pinning

• Trigger: useMart('ensembl', ...) without version=.
• Mechanism: Defaults to current release; gene model versions change quarterly.
• Symptom: Re-running a year later produces different rows.
• Fix: Pin with useEnsembl(version=110) or archive host URL.

Multiple cross-refs balloon row count

• Trigger: Query for ensembl_gene_id, refseq_mrna; a gene with 10 RefSeq mRNAs produces 10 rows.
• Mechanism: BioMart joins on cross-refs; many-to-many produces row multiplication.
• Symptom: "Why do I have 50K rows for 5K input IDs?"
• Fix: Filter to one isoform per gene downstream; or use ensembl_canonical filter where available.

Symbol-based filter misses HGNC renames

• Trigger: filters={'external_gene_name': ['MARCH1']} post-2020.
• Mechanism: HGNC renamed to MARCHF1; BioMart mirrors the new symbol.
• Symptom: Empty result for that gene.
• Fix: Filter by ensembl_gene_id or hgnc_id; these are stable.

Multi-species mart query slow

• Trigger: Querying mmusculus_homolog_ensembl_gene for 30K human genes.
• Mechanism: Ortholog attributes are heavy; large queries take minutes.
• Symptom: Timeout or slow.
• Fix: Chunk by chromosome; or use Ensembl Compara REST for targeted lookups.

REST loops where BioMart belongs

• Trigger: Loop of 5,000 Ensembl REST /lookup/symbol calls.
• Mechanism: Rate-limit cascade; 5,000 * 0.07s = 6 minutes just for the rate gate, plus HTTP overhead.
• Symptom: Slow; 429 errors.
• Fix: Switch to one BioMart query.

Wrong mart for the question

• Trigger: Querying gene info from ENSEMBL_MART_SNP.
• Mechanism: SNP mart has variant attributes, not gene attributes.
• Symptom: Empty result or wrong fields.
• Fix: Discover marts with server.marts; pick ENSEMBL_MART_ENSEMBL for genes.

Common errors

Error / symptom	Cause	Solution
Empty result	Wrong attribute / filter name	List with ds.attributes and ds.filters
Timeout on big query	No filter, too many rows	Chunk by chromosome
Drift between re-runs	No version pinning	useEnsembl(version=110)
Row count > expected	Many-to-many cross-ref joins	Filter to canonical isoform
Symbol filter returns nothing	HGNC rename	Filter by Ensembl ID or HGNC ID
Slow on ortholog wide-table	Multi-species join expensive	Chunk by chromosome

References

• Durinck S, Spellman PT, Birney E, Huber W. (2009) Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nat Protoc 4:1184-1191.
• Kinsella RJ, Kahari A, Haider S, et al. (2011) Ensembl BioMarts: a hub for data retrieval across taxonomic space. Database 2011:bar030.
• Smedley D, Haider S, Durinck S, et al. (2015) The BioMart community portal: an innovative alternative to large, centralized data repositories. Nucleic Acids Res 43:W589-W598.
• pybiomart documentation: https://github.com/jrderuiter/pybiomart

Related Skills

• ensembl-rest - Per-record Ensembl queries (BioMart's complement)
• ortholog-inference - Compara ortholog calls with confidence semantics
• uniprot-access - UniProt ID mapping (preferred for UniProt-rooted lookups and obsolete-accession resolution; BioMart is preferred for Ensembl-rooted batches >5K)
• ncbi-datasets-cli - NCBI-side bulk path for genome / gene data
• entrez-search - NCBI alternative for non-Ensembl queries