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BioMed Central
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BMC Genomics
Open Access
Software
BioMart – biological queries made easy
Damian Smedley1, Syed Haider1, Benoit Ballester1, Richard Holland1,
Darin London2, Gudmundur Thorisson3 and Arek Kasprzyk*4
Address: 1European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK, 2Institute for Genome
Sciences & Policy (IGSP), Duke University CIEMAS, 101 Science Drive, DUMC Box 3382, Durham, NC 27708, USA, 3Department of Genetics,
University of Leicester, University Road, Leicester, LE1 7RH, UK and 4Ontario Institute for Cancer Research, MaRS Centre, South Tower, 101
College Street, Suite 800 Toronto, Ontario, M5G 0A3, Canada
Email: Damian Smedley - damian@ebi.ac.uk; Syed Haider - syed@ebi.ac.uk; Benoit Ballester - benoit@ebi.ac.uk;
Richard Holland - holland@ebi.ac.uk; Darin London - darin.london@duke.edu; Gudmundur Thorisson - gthorisson@gmail.com;
Arek Kasprzyk* - Arek.Kasprzyk@oicr.on.ca
* Corresponding author
Abstract
Background: Biologists need to perform complex queries, often across a variety of databases.
Typically, each data resource provides an advanced query interface, each of which must be learnt
by the biologist before they can begin to query them. Frequently, more than one data source is
required and for high-throughput analysis, cutting and pasting results between websites is certainly
very time consuming. Therefore, many groups rely on local bioinformatics support to process
queries by accessing the resource's programmatic interfaces if they exist. This is not an efficient
solution in terms of cost and time. Instead, it would be better if the biologist only had to learn one
generic interface. BioMart provides such a solution.
Results: BioMart enables scientists to perform advanced querying of biological data sources
through a single web interface. The power of the system comes from integrated querying of data
sources regardless of their geographical locations. Once these queries have been defined, they may
be automated with its "scripting at the click of a button" functionality. BioMart's capabilities are
extended by integration with several widely used software packages such as BioConductor, DAS,
Galaxy, Cytoscape, Taverna. In this paper, we describe all aspects of BioMart from a user's
perspective and demonstrate how it can be used to solve real biological use cases such as SNP
selection for candidate gene screening or annotation of microarray results.
Conclusion: BioMart is an easy to use, generic and scalable system and therefore, has become an
integral part of large data resources including Ensembl, UniProt, HapMap, Wormbase, Gramene,
Dictybase, PRIDE, MSD and Reactome. BioMart is freely accessible to use at http://
www.biomart.org.
Background
In this post-genomics era, data of increasing volume and
complexity is being deposited into databases around the
world. Biologists need to ask complex queries of this data
to test and drive their research hypotheses. Typically, each
data source provides an advanced query interface on their
website to satisfy this requirement. However, each site has
its own solution and subsequently, the user has a learning
Published: 14 January 2009
BMC Genomics 2009, 10:22 doi:10.1186/1471-2164-10-22
Received: 22 September 2008
Accepted: 14 January 2009
This article is available from: http://www.biomedcentral.com/1471-2164/10/22
© 2009 Smedley et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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curve before they can start interacting with the data. A fur-
ther problem the researcher has is that they often need to
query more than one data source, necessitating mastering
more than one interface and having to cut and paste
results between the sites. If the analysis involves high-
throughput data, this approach is not usually scalable. To
overcome this problem, many groups rely on bioinforma-
ticians who can generate scripts to interact with the vary-
ing programmatic interfaces of the different data sources.
They also often have to learn a number of different web
services or application programmatic interfaces (APIs) for
each resource. A preferable solution would be to have
generic software that a biologist can use on top of any data
source. BioMart[1] is such a solution.
BioMart is an open source data management system that
comes with a range of query interfaces that allow users to
group and refine data based upon many different criteria.
In addition, the software features a built-in query opti-
miser for fast data retrieval. A BioMart installation can
provide domain-specific querying of a single data source
or function as a one-stop shop (web portal) to a wide
range of BioMarts as our central portal [2] does. All
BioMart websites have the same look and feel (only vary-
ing in colour scheme and branding), which has obvious
advantages to users moving between different resources.
However, the power of the system comes from integrated
querying of the different BioMarts. If any datasets share
common identifiers (such as Ensembl gene IDs or Uni-
prot IDs) or even mappings to a common genome assem-
bly, these can be used to link BioMarts together in
integrated queries. Additionally, these datasets do not
have to be located on the same server or even at the same
geographical location. This distributed solution has many
advantages; not least of which is the fact that each site can
utilise their own domain expertise to deploy their
BioMart.
BioMart also has the advantage of being integrated with
external software packages such as BioConductor [3], the
Distributed Annotation System (DAS) [4], Galaxy [5],
Cytoscape [6], Taverna [7]. This enables users to perform
integrated queries with non-BioMart data sources as well
as detailed analysis of the results. BioMart is also part of
the GMOD (Generic Model Organism Database) [8] suite
of tools for building a model organism site.
Originally developed for the Ensembl genome browser [9]
as the EnsMart data warehouse [10], BioMart has now
become a fully generic data integration solution.
Although applicable to any type of data, BioMart is partic-
ularly suited for advanced searching of the complex
descriptive data typically found in biological datasets.
Numerous BioMarts have now been installed by external
groups, in large part because of its automated deployment
tools and cross platform compatibility. These include
model organism databases such as Gramene [11], Dicty-
base [12], Wormbase [13] and RGD (Rat Genome Data-
base) [14] as well as HapMap variation [15], pancreatic
expression database [16], Reactome pathways [17] and
PRIDE proteomic [18] databases (see Table 1 for the full
list). A wide variety of analyses and tasks are possible from
the publicly available BioMarts, ranging from SNP (single
nucleotide polymorphism) selection for candidate gene
screening, microarray annotation, cross-species analysis,
through to recovery of disease links, sequence variations
and expression patterns.
The range of interfaces is designed with both biologists
and bioinformaticians in mind. The simplest way of que-
rying BioMart is via the web interface called MartView
(either on our central portal [2] or follow the links on our
main page [1] to the individual sites). Programmatic
access is available via a Perl API or BioMart's web services
(MartServices). An important and novel feature of
BioMart is that it offers "scripting at the click of a button".
A user can generate an API or MartServices script by build-
ing up a query on the MartView website followed by a
simple click of a button. All the interfaces allow the user
to build up biological queries by first selecting dataset(s)
of interest, then the data to view and/or save (attributes),
some optional restrictions (filters) on the query and
finally the format for the data.
Implementation
Here we will describe a top-level view of the BioMart sys-
tem as the focus of this paper is on practical use of
BioMart rather than implementation and deployment.
Further documentation on these aspects can be found on
the BioMart website http://www.biomart.org. BioMart is
designed around a simple, three tier architecture:
(i) The first tier consists of one or more relational data-
bases. Each database may consist of one or more "marts",
which are schema compliant with BioMart definitions.
Each "mart" may hold a number of different datasets. The
BioMart data model is a denormalised, query optimised
schema and can be deployed using Oracle, MySQL or
Postgres relational database management systems. Each
dataset uses a reversed star model [10] where data map-
ping 1:1 with the central object being modelled is stored
in a main table. Data mapping 1:n is stored in one or
more satellite, dimension tables. Two tools are provided
to build and configure the mart databases in the first tier:
• MartBuilder, to construct SQL statements that will trans-
form your schema into a mart.
• MartEditor, to configure the finished mart for use with
the rest of the system. This produces a dataset configura-
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tion XML (Extensible markup language) that is stored in
metadata tables within the actual mart database.
(ii) The second tier is the Perl API (distributed in the
biomart-perl package) which interacts with both the data-
set configuration and the mart databases.
(iii) The third tier consists of the query interfaces which
utilise the API to present the possible BioMart queries and
results:
• MartView, a web browser interface.
• MartService, a web services interface.
• MartURLAccess, a URL based access to MartView.
Results
(i) BioMart website
In this section we describe how to use the MartView web
interface with an example, followed by a few more biolog-
ically relevant queries that users may perform with the
currently available mart interfaces. For the first example,
we show how to retrieve "the Ensembl mouse genes and
genomic locations in the first 10 Mbp of chromosome 1
region that have already been targeted as part of the Inter-
national Mouse Mutagenesis Consortium ".
MartView is reached by clicking the link from the BioMart
site [1]. The query is started by selecting the ENSEMBL
GENES database and the Mus musculus genes dataset (Fig-
ure 1A) hosted at the EBI, UK. Next filters to refine the
query are set by clicking on the Filters bar in the left hand
pane, expanding the REGION section in the main right
hand pane and setting the Chromosome, Gene Start and
Gene End filters to 1, 1 and 10,000,000 respectively (Fig-
ure 1B). To find out how many genes would be returned
at this point, use the Count button in the menu bar. Next,
the user should choose the data fields they want to view
or download in a similar manner by clicking on the
Attributes bar in the left hand pane and choosing Ensembl
Gene ID, Associated Gene name, Chromosome Name, Gene
Start (bp), Gene End (bp) in the GENE section (Figure 1C).
Note that attributes and filters can be selected in any
order. The left pane shows the summary of datasets,
attributes and filters chosen. They will appear in the order
of selection, and this same order will be used to organise
the results later. Finally, the user needs to click on the
Results button in the menu bar to get a preview of the
results (Figure 1D). In this panel the number of rows to
Table 1: Description of all publicly accessible BioMarts to date
Name of BioMart Description of contents Location of BioMart
Ensembl Genes Automated annotation of over 40 eukaryotic genomes EMBL-EBI, UK
Ensembl Homology Ensembl Compara orthologues and paralogues EMBL-EBI, UK
Ensembl Variation Ensembl Variation data from dbSNP and other sources EMBL-EBI, UK
Ensembl Genomic Features Ensembl Markers, clones and contigs data EMBL-EBI, UK
Vega Manually curated human, mouse and zebrafish genes EMBL-EBI, UK
HTGT High throughput gene targeting/trapping to produce mouse
knock-outs
Sanger, UK
Gramene Comparative Grass Genomics CSHL, USA
Reactome Curated database of biological pathways CSHL, USA
Wormbase C. elegans and C. briggsae genome database CSHL, USA
Dictybase Dictyostelium discoideum genome database Northwestern University, USA
RGD Rat model organism database Medical College of Wisconsin, USA
PRIDE Proteomic data repository EMBL-EBI, UK
EURATMart Rat tissue expression compendium EMBL-EBI, UK
MSD Protein structures EMBL-EBI, UK
Uniprot Protein sequence and function repository EMBL-EBI, UK
Pancreatic Expression Database Pancreatic cancer expression database Barts & The London School of Medicine, UK
PepSeeker Peptide mass spectrometer data for proteomics University of Manchester, UK
ArrayExpress Microarray data repository EMBL-EBI, UK
GermOnLine Cross species knowledgebase of genes relevant for sexual
reproduction
Biozentrum/SIB, Switzerland
DroSpeGe Annotation of 12 Drosophila genomes Indiana Univeristy, USA
HapMap Catalogue of common human variations in a range of populations CSHL, USA
VectorBase Invertebrate vectors of human pathogens University of Notre Dame, USA
Paramecium DB Paramecium tetraurelia model organism database CNRS, France
Eurexpress Mouse in situ expression data MRC Edinburgh, UK
Europhenome Mouse phenotype data from high throughput standardized
screens
MRC Harwell, UK
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BioMart query showing that the St18 gene is the only mouse gene in the first 10 Mb of chromosome 1 that is annotated as "tar-geting complete" by the International Mouse Mutagenesis ConsortiumFigure 1
BioMart query showing that the St18 gene is the only mouse gene in the first 10 Mb of chromosome 1 that is
annotated as "targeting complete" by the International Mouse Mutagenesis Consortium. This involves: (A) select-
ing the Ensembl Mus musculus genes dataset, (B) setting the filters, (C) setting the attributes, (D) viewing the results, and (E)
adding in the Gene targeting dataset to obtain just the gene that has reached the "targeting complete" status.
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preview can be changed along with the format to preview
them in e.g. hypertext markup language (HTML), Excel
(XLS), FASTA, tab-separated values (TSV), comma-sepa-
rated values (CSV), or gene structure format (GFF). From
this panel the user may also export all the results to a file.
Time-out settings at particular sites can cause problems, so
for these cases there is a 'notify by email' option where the
results are generated and stored on the server-side. When
the results are ready, the user is sent an email containing
a link to download the results. The 'Unique results only'
option is useful for removing redundant rows in the out-
put: for example if a user selected Ensembl Gene ID and
another attribute mapped at the transcript level, this could
happen.
The user now has the genomic locations of all the mouse
genes in the first 10 Mbp of chromosome 1 but not
whether the genes have been targeted yet in the mouse
knockout project. To finish this example, a second dataset
needs to be added to the query, this time retrieving mouse
knockout data from the BioMart located at the Sanger
Institute, UK. The second (lower) Dataset bar on the left
hand pane has to be clicked and the Gene targeting dataset
from the HIGH THROUGHPUT GENE TARGETING AND
TRAPPING database selected. The status filter is then set to
'ES cells – Targeting confirmed' and the attributes set to
Gene symbol, EUCOMM, KOMP, NorCOMM (component
projects of the International Mouse Mutagenesis Consor-
tium) and status. This time, when the results button is
clicked, a single mouse gene (St18) in the chromosome 1
region is shown to be have reached the "targeting con-
firmed stage" and has been assigned to the KOMP project
(Figure 1E).
Another common use of BioMart is the analysis of the
genes up-regulated in a particular microarray experiment.
For example, a user might retrieve "1 kb of upstream
sequences from a cluster of human genes identified by an
expression profile experiment on an Affymetrix Genechip
U95Av2".
This query is started with the New button on the menu bar
to take the user to a new query page. The Homo sapiens
genes dataset is selected, and filters selected by clicking on
the Filter bar again but this time the ID list limit filter in
the GENE section is chosen. Choosing the Affy hg u95av2
ID(s) option allows the user to upload a file of experimen-
tally relevant Affymetrix probeset IDs from this Genechip
using the file Browse button or to enter IDs by cutting and
pasting into the text box (we include some example IDs in
Additional file 1). The various sequence options can be
seen by clicking on Sequences at the top of the page in the
attributes section (Figure 2A). These include cDNA (com-
plementary DNA), peptides, coding regions, UTRs
(untranslated regions), and exons with additional
upstream and downstream flanking regions. In order to
identify upstream regulatory features in subsequent anal-
ysis, the user would select 1000 bp of upstream flank
sequence for each gene (Figure 2B). Note that a variety of
attributes can be selected for the FASTA header line of the
sequence file.
BioMart may also be used for annotating experiments or
for mapping identifiers to genes and vice a versa. For
instance, instead of exporting sequence in the above
example, the user could have chosen gene identifiers and
names mapping to the uploaded microarray probe IDs.
Ensembl contains a wide range of external identifiers for
its genes making detailed annotation possible e.g. GO
(Gene Ontology), EMBL (European Molecular Biology
Laboratory)/Genbank, UniProt (Universal Protein
Resource), UniGene, Pfam (Protein Family), PDB (Pro-
tein Data Bank) and RefSeq identifiers as well as official
names from the naming committees of each species, such
as the HGNC (HUGO Gene Nomenclature Committee)
and MGI (Mouse Genome Informatics) symbols.
Another typical use case for BioMart is in the identifica-
tion of candidate genes for disease association. For exam-
ple, a locus for Arrhythmogenic right ventricular dysplasia
was originally mapped to 14q24 [19]. A list of 172 genes
may be identified in this region, with 67 having expres-
sion in heart as assessed from EST (expressed sequence
tag) derived data (Figure 3A). Exporting the GO descrip-
tion data for these candidate genes (Figure 3B) immedi-
ately reveals two potential candidates with a role in organ
morphogenesis which is affected in this disorder:
ZFP36L1 and TGFB3. TGFB3 was eventually shown to be
mutated in the affected families [20]. BioMart enabled
this complex query to be performed very quickly and eas-
ily. Following the identification of candidate disease asso-
ciated genes, researchers often screen for SNPs showing an
association with the disease. BioMart provides a quick
way of identifying suitable SNPs to screen. For each of the
candidate genes, the user can export a list of the SNP iden-
tifiers mapped within that gene, and SNP attributes such
as their location in the transcript and coding sequence,
and whether they are non-synonymous (together with the
associated amino acid change) (Figure 3C).
Recently, many other groups have applied the BioMart
technology to help their scientists answer complex queries
such as those shown above. For example, the CASIMIR
(Coordination and Sustainability of International Mouse
Informatics Resources) consortium has created a mouse
portal prototype [21].
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(ii) Scripting at the click of a button
(a) Perl API
The Perl API (for download and install instructions see
[22]) is self-explanatory with the help of an example. The
best way to learn and, indeed, generate the API scripts is
to use the Perl button in the top pane of any MartView site
after a manual query has been defined. The script below
extracts the mouse and human Ensembl Gene ID and
genomic positions for all human genes on chromosome 1
that have a mouse orthologue on chromosome 2. Just like
the website, generating a query involves setting a dataset,
adding filters and attributes and selecting an output for-
mat. The user can even get a count of the results just like
the website.
(A) Sequence output options and (B) FASTA output for all the genes found to be up-regulated in a microarray experiment using the Affymetrix HG-U95Av2 probesetFigure 2
(A) Sequence output options and (B) FASTA output for all the genes found to be up-regulated in a microarray
experiment using the Affymetrix HG-U95Av2 probeset. Here 1000 bp upstream of the first exon have been chosen
along with the Ensembl Gene Id and the chromosomal position of the gene for the FASTA header.
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Candidate gene identification using BioMartFigure 3
Candidate gene identification using BioMart. (A) The Arrhythmogenic right ventricular dysplasia (ARVD) gene was
mapped to 14q24. BioMart identifies 172 genes in this region, which may be narrowed down to 67 with expression in the
heart. (B) This may be further refined to the two candidate genes, ZFP36L1 and TGFB3, by looking for genes involved in organ
morphogenesis, according to GO, as this condition is known to result in widespread structural abnormalities. The latter gene
is now known to be the one involved in this disorder. (C) BioMart may also be used to extract SNPs for the identified genes
including their location in the gene, whether they are upstream, downstream, intronic or coding and for the latter whether
they result in an amino acid substitution.
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use strict;
use BioMart::Initializer;
use BioMart::Query;
use BioMart::QueryRunner;
my $confFile = "PATH TO YOUR REGISTRY FILE UNDER
biomart-perl/conf/"
my $initializer = BioMart::Initializer-> new('registry-
File'=>$confFile, 'action'=>'cached');
my $registry = $initializer->getRegistry;
my $query = BioMart::Query-> new('registry'=>$regis-
try,'virtualSchemaName'=>'default');
$query->setDataset("hsapiens_gene_ensembl");
$query->addFilter("chromosome_name", ["1"]);
$query->addAttribute("ensembl_gene_id");
$query->addAttribute("chromosome_name");
$query->addAttribute("start_position");
$query->addAttribute("end_position");
$query->setDataset("mmusculus_gene_ensembl");
$query->addFilter("chromosome_name", ["2"]);
$query->addAttribute("ensembl_gene_id");
$query->addAttribute("chromosome_name");
$query->addAttribute("start_position");
$query->addAttribute("end_position");
$query->formatter("TSV");
my $query_runner = BioMart::QueryRunner->new();
# to obtain count
# $query->count(1);
# $query_runner->execute($query);
# print $query_runner->getCount();
# to obtain unique rows only
# $query_runner->uniqueRowsOnly(1);
$query_runner->execute($query);
$query_runner->printHeader();
$query_runner->printResults();
$query_runner->printFooter();
(b) MartServices
MartServices, BioMart's RESTful type web services, is avail-
able as part of the MartView web application and, as with
all the BioMart interfaces, designed to be as simple to use
as possible. It is available from the same location as Mar-
tView, i.e. if the user accesses MartView using http://
www.myurl.org/biomart/martview then they would
access the MartServices using http://www.myurl.org/
biomart/martservice. Both overview information (meta-
data) about the services and queries are submittable (for
details on the metadata services see the documentation on
the main site[1]).
Like the Perl API, the query XML for MartServices is self-
explanatory and again the best way to learn and, indeed,
generate it is to use the XML button in the top pane of any
MartView site to produce a representation of the currently
configured query. The XML to recreate the Perl API exam-
ple above (extracting the mouse and human Ensembl
Gene ID and genomic positions for all human genes on
chromosome 1 that have a mouse orthologue on chromo-
some 2) is shown below:
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE Query>
<Query virtualSchemaName = "default" formatter =
"TSV" header = "0" uniqueRows = "0" count = "" dataset-
ConfigVersion = "0.6" >
<Dataset name = "hsapiens_gene_ensembl" interface
= "default" >
<Filter name = "chromosome_name" value = "1" />
<Attribute name = "ensembl_gene_id" />
<Attribute name = "chromosome_name" />
<Attribute name = "start_position" />
<Attribute name = "end_position" />
</Dataset>
<Dataset name = "mmusculus_gene_ensembl" inter-
face = "default" >
<Filter name = "chromosome_name" value = "2" />
<Attribute name = "ensembl_gene_id" />
<Attribute name = "chromosome_name" />
<Attribute name = "start_position" />
<Attribute name = "end_position" />
</Dataset>
</Query>
Again, note how the query is formed by adding Datasets
within a Query Tag and filters and attributes within the
Datasets. As for the Perl API the output format can be
changed with a formatter setting, counts can be performed
by setting count="1" and unique rows by setting unique-
Rows="1" on the Query. To submit a query to MartServ-
ices, this XML has to be posted to http://
www.biomart.org/biomart/martservice by appending a
query parameter. For example using wget: wget – O
results.txt 'http://www.biomart.org/biomart/martserv
ice?query=MY_XML' replacing MY_XML with the XML
obtained above.
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(c) MartView URL/XML requests
The MartView web interface can be pre-populated with an
existing query using URL/XML requests. This can be
achieved by sending the XML query as described in the
previous section to the following URL
http://www.biomart.org/biomart/mar
tview?query=<XML_REQUEST>
Equivalently, an XML free representation of the same
query can be sent to the following URL for similar results
http://www.biomart.org/biomart/mar
tview?<URL_REQUEST>
<XML_REQUEST> can be replaced with the XML query as
described above for MartServices and <URL_REQUEST>
represents the same query in a URL format. As for the Perl
API and MartService interfaces, construction of the URL
request is best handled by building the query using the
MartView website and then using the URL button in the
top pane of MartView. The URL/XML request feature is
obviously useful for bookmarking favourite queries as
well as for constructing canned queries when linking from
an external site directly to MartView.
(iii) BioMart integration with external software
A number of external software packages have incorpo-
rated BioMart querying capabilities into their systems.
Generally, this has been carried out to improve their soft-
ware by importing data through BioMart into their system
for: (i) further analysis using the existing services they pro-
vide (Galaxy, BioConductor, Taverna) or (ii) to add fur-
ther annotation to their results (Cytoscape). This
integration has been made possible through MartServices.
All the requests generated by these external packages run
against the BioMart central portal. Using BioMart through
these external packages expands the usefulness of both
BioMart and these external tools. Therefore, brief descrip-
tions and examples of this integrated usage are presented
below. BioMart has also been improved by the incorpora-
tion of external software technologies. BioMart can be eas-
ily configured to become a DAS annotation server for
viewing of data through various DAS clients.
(a) Galaxy
Galaxy [5,23] integrates genomic sequences, alignments
and functional annotation through an interactive and
simple web portal, so no installation is required. The sys-
tem allows users to gather data using resources like
BioMart or the UCSC (University of California Santa
Cruz) Table Browser. The user can then manipulate the
data in a variety of ways, such as simple intersections (e.g.
selecting the genes common to two BioMart result sets),
unions, and subtractions, or more complex analysis using
tools such as those from the EMBOSS (European Molecu-
lar Biology Open Software Suite) package [24]. An exam-
ple of this integrated analysis is shown in Additional file
2.
(b) BioConductor
BioConductor [3] is open source software for the analysis
of genomic data. BioConductor is based on the R pro-
gramming language that is especially suited for statistical
analysis. Comprehensive instructions on how to install R
and BioConductor are provided on their site [25]. The
biomaRt package provides an API to query BioMart data-
bases for use within BioConductor.
biomaRt mimics the functionality of the Perl API, allowing
retrieval of any of the information that the other BioMart
interfaces allow. A second set of functions is tailored
towards Ensembl and include commonly used queries in
microarray data analysis. Using biomaRt, a user can for
example annotate the features on an array with official
gene names, GO identifiers/descriptions and OMIM
(Online Mendelian Inheritance in Man) terms retrieved
via identifiers, such as Affymetrix, Locuslink, RefSeq or
EntrezGene IDs. The package also provides homology
mappings between these identifiers across all the species
present in Ensembl.
The first stage in using biomaRt is to load the library and
choose a mart to use:
library(biomaRt)
listMarts()
The results of this command are shown in Table 2
Table 2: Output from the listMarts command of the BiomaRt library
name version
1 ensembl ENSEMBL 49 GENES (SANGER)
2 compara_mart_homology_49 ENSEMBL 49 HOMOLOGY (SANGER)
3 compara_mart_pairwise_ga_49 ENSEMBL 49 PAIRWISE ALIGNMENTS
4 compara_mart_multiple_ga_49 ENSEMBL 49 MULTIPLE ALIGNMENTS
5 Snp ENSEMBL 49 VARIATION (SANGER)
...
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Next a dataset is chosen:
ensembl = useMart("ensembl")
listDatasets(ensembl)
The results of this command are shown in Table 3
To set the dataset to be queried, the useMart function is
used:
human = useMart("ensembl", dataset="hsapiens_gene_
ensembl")
The query is constructed with the getBM function. For
example, the following will return the Ensembl Gene ID
and genomic position for genes up-regulated in an Affy-
metric HG U133 Plus 2 experiment:
getBM(attributes=c("ensembl_gene_id","chromosome_n
ame","start_position","end_position"),fil-
ter="affy_hg_u133_plus_2",values=c('215984_s_at',
'203174_s_at', '215984_s_at'), mart=human)
The results of this command are shown in Table 4
(c) Cytoscape
Cytoscape [6] is open source software for the visualisation
of molecular interaction networks and their integration
with other biological data such as gene expression pro-
files. Cytoscape uses both web services and BioMart
MartServices to retrieve this extra annotation (see Addi-
tional file 3).
(d) Taverna workbench
The Taverna workbench [7,26], another open source soft-
ware package which integrates BioMart, allows interoper-
ation between local and remote analysis tools and
databases by providing an environment for the design and
execution of workflow experiments. Taverna is able to uti-
lise BioMart, web services and BioMoby [27] services
allowing its users to combine over 3000 different
resources and analysis tools, providing a flexible and
extensible platform for bioinformatics research. Overall,
Taverna allows bioinformaticians to build automated
protocols that access each data source and integrate the
collected results into a suitable form for biologists to
explore (see Additional file 4).
(e) Distributed annotation system
Any BioMart server can be easily configured to act as a
DAS annotation server [4] so that any DAS client such as
GBrowse or the Ensembl genome browser can display the
data stored in BioMart. DAS offers a simple system for
data federation where a DAS client may be used to view
the data from several sources in a single, usually graphical,
interface. For example, genes stored in a BioMart dataset,
such as those affected in a mouse strain stored in the Euro-
pean Mouse Mutant Archive (EMMA) repository may be
displayed as tracks in Ensembl contigView along with the
usual gene tracks (see Additional file 5).
A list of the sources available from our central portal is
available [28]. This server currently returns annotation
across a 'segment'. Possible 'segment' values could be a
feature identifier or a genomic region defined by chromo-
some:start,end whereby start and end are optional. For
example, an ensembl Homo sapiens DAS data source
called
'default__hsapiens_gene_ensembl__ensembl_das_gene'
can be accessed as:
http://www.biomart.org/biomart/das/
default__hsapiens_gene_ensembl__ensembl_das_gene/
features?segment=ENSG00000184895
http://www.biomart.org/biomart/das/
default__hsapiens_gene_ensembl__ensembl_das_chr/
features?segment=X
http://www.biomart.org/biomart/das/
default__hsapiens_gene_ensembl__ensembl_das_chr/
features?segment=13:31787617,31871805
Discussion
An important feature we intend to implement in the near
future is secure data access. This is vital where certain data
is sensitive and adding this feature will make BioMart an
even more attractive solution for organisations looking
for controlled access. Adoption of BioMart will then allow
secure and simple browsing of their private data, as well
as the power of integrated querying of this data with the
available public BioMarts.
We will also focus on new interfaces that further simplify
multiple dataset querying. In these new interfaces, once
dataset(s) are chosen, all the attributes and filters will be
presented to the user as if from a single data source, even
if they originate from distributed BioMarts. These inter-
faces will also address the needs of users who require a
simpler, more limited, query tool. In addition, we will
Table 3: Output from the listDatasets command of the BiomaRt
library
dataset version
1 oanatinus_gene_ensembl OANA5
2 gaculeatus_gene_ensembl BROADS1
...
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expand the options available for viewing, analysing and
saving results. For example, we will offer gene reports con-
taining all the information we have on a gene in a nicely
formatted web page. We will also offer graphical display
of results such as locations on a karyogram or a bar chart
display of distributions. Furthermore, statistical analysis
of results will be provided e.g. whether particular GO
terms are enriched in the result set.
Conclusion
In this paper, we have demonstrated that BioMart pro-
vides a range of simple, but powerful, interfaces for query-
ing biological data. These may be used for many
important research applications such as the data mining
of large genomic resources, identification of candidate
disease associated genes and variations within them, and
the annotation of genome-wide experiments such as
microarray studies. BioMart's architecture allows inte-
grated querying of resources at different locations. As the
number of publicly available BioMarts increases, users
will be able to ask ever more complex queries. The pres-
ence of a services layer (MartService) and Perl API pro-
vides easy programmatic access for more technical users to
script against BioMart and to integrate our software into
their own systems. However, we hope the simplicity of
generating the MartService and Perl API queries via the
website will encourage the novice user to use these inter-
faces where appropriate. The integration of BioMart with
external software adds a further dimension to its useful-
ness as a research tool. BioMart is also part of GMOD. This
project aims to provide a free set of software for creating
and administering a model organism database, including
genome visualisation, annotation and literature curation.
Tools currently exist to produce a BioMart database from
the GFF3 gene structure files commonly used by GMOD
and further integration of BioMart and other components
of GMOD is planned in the near future.
We hope that this description of BioMart, and the direc-
tion the system is heading, will have encouraged users and
data deployers to explore BioMart for their own biological
query requirements.
Availability and requirements
Text Project name: BioMart
Project home page: http://www.biomart.org
Operating system(s): Any. Local deployment of BioMart
requires the Java Virtual Machine 1.3,1.4 or 1.5, Perl 5.6.0
or higher and either Apache 1.3,1.4 or Apache 2.0 or
higher are required
Programming language: Java and Perl
License: LGPL
Any restrictions to use by non-academics: None.
Authors' contributions
DS, SH, BB, RH, DL, GT have all contributed to the devel-
opment of AK's vision of an easy-to-use system for
advanced querying of biological data. AK continues to
drive the design and development of BioMart at his new
position at the Ontario Institute for Cancer Research with
contributions from SH. The manuscript was drafted by
DS, SH, BB with all authors contributing revisions and
with DS coordinating the effort. All authors read and
approved the final version of the manuscript.
Additional material
Additional file 1
Sample Affymetrix Probe IDs. File of Affymetrix HG U95AV2 IDs for
the BioMart example given in the paper.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2164-10-22-S1.txt]
Additional file 2
Use of BioMart within the Galaxy system. BioMart embedded in the
Galaxy framework is used to retrieve the peptide sequence for the mouse
Bambi gene (A). The peptide sequence is saved on the Galaxy server and
then transmembrane domains identified in it by running tmap analysis
(part of the EMBOSS package) also from within Galaxy (B). The down-
loaded results file shows two potential transmembrane segments (C).
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2164-10-22-S2.doc]
Additional file 3
Cytoscape platform used to visualise a yeast protein interaction net-
work. Annotation of the selected nodes in yellow is shown in the bottom
pane and uses MartServices on our central portal to retrieve the GO anno-
tation for each node.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2164-10-22-S3.doc]
Table 4: Output from the getBM command of the BiomaRt library
ensembl_gene_id chromosome_name start_position end_position
1 ENSG00000026036 20 61759607 61800495
2 ENSG00000101246 20 61801253 61809809
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Acknowledgements
We thank the Wellcome Trust, EMBL and the European Commission
within its FP6 Programme, under the thematic area "Life sciences, genomics
and biotechnology for health", contract number LHSG-CT-2004-512092
for providing funding for the project at the EBI. At the Ontario Institute for
Cancer Research work funding is provided by the government of Ontario.
We are most grateful to Cara Woodwark for her feedback on this manu-
script.
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Additional file 4
Taverna workflow demonstrating BioMart and web services interac-
tion. Ensembl Gene IDs and EMBL IDs for a given set of genes (results
of an Affymetrix microarray experiment) are recovered. The left hand
panel shows a graphical depiction of the workflow in which the EMBL IDs
are converted to KEGG IDs and then HTML links to marked up pathways
using KEGG web services. The upper right panel shows the tabular results
of the wor kfl ow w ith Ensem bl G ene IDs map ped to K EGG pathw ay U RLs .
The bottom right panel shows one of these links with the mapped gene
marked in red in the pathway.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2164-10-22-S4.doc]
Additional file 5
BioMart as a DAS server. Ensembl ContigView display showing a
EMMA mouse strain archive track in blue (A). The data is served using
the DAS protocol from a BioMart server in an external location to the rest
of the Ensembl data. Ensembl GeneView showing Pancreatic Expression
Database annotation (B). This annotation comes from a geneDAS source
served by the BioMart server.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2164-10-22-S5.doc]
