biomaRt 2.46.3
In recent years a wealth of biological data has become available in public data repositories. Easy access to these valuable data resources and firm integration with data analysis is needed for comprehensive bioinformatics data analysis. The biomaRt package, provides an interface to a growing collection of databases implementing the BioMart software suite. The package enables retrieval of large amounts of data in a uniform way without the need to know the underlying database schemas or write complex SQL queries. Examples of BioMart databases are Ensembl, Uniprot and HapMap. These major databases give biomaRt users direct access to a diverse set of data and enable a wide range of powerful online queries from R.
Every analysis with biomaRt starts with selecting a BioMart database to use. A first step is to check which BioMart web services are available. The function listMarts() will display all available BioMart web services
library("biomaRt")
listMarts()## biomart version
## 1 ENSEMBL_MART_ENSEMBL Ensembl Genes 102
## 2 ENSEMBL_MART_MOUSE Mouse strains 102
## 3 ENSEMBL_MART_SNP Ensembl Variation 102
## 4 ENSEMBL_MART_FUNCGEN Ensembl Regulation 102The useMart() function can now be used to connect to a specified BioMart database, this must be a valid name given by listMarts(). In the next example we choose to query the Ensembl BioMart database.
ensembl <- useMart("ensembl")BioMart databases can contain several datasets, for Ensembl every species is a different dataset. In a next step we look at which datasets are available in the selected BioMart by using the function listDatasets(). Note: use the function head() to display only the first 5 entries as the complete list has 203 entries.
datasets <- listDatasets(ensembl)
head(datasets)## dataset description version
## 1 acalliptera_gene_ensembl Eastern happy genes (fAstCal1.2) fAstCal1.2
## 2 acarolinensis_gene_ensembl Anole lizard genes (AnoCar2.0) AnoCar2.0
## 3 acchrysaetos_gene_ensembl Golden eagle genes (bAquChr1.2) bAquChr1.2
## 4 acitrinellus_gene_ensembl Midas cichlid genes (Midas_v5) Midas_v5
## 5 amelanoleuca_gene_ensembl Panda genes (ailMel1) ailMel1
## 6 amexicanus_gene_ensembl Mexican tetra genes (Astyanax_mexicanus-2.0) Astyanax_mexicanus-2.0To select a dataset we can update the Mart object using the function useDataset(). In the example below we choose to use the hsapiens dataset.
ensembl = useDataset("hsapiens_gene_ensembl",mart=ensembl)Or alternatively if the dataset one wants to use is known in advance, we can select a BioMart database and dataset in one step by:
ensembl = useMart("ensembl",dataset="hsapiens_gene_ensembl")The getBM() function has three arguments that need to be introduced: filters, attributes and values.
Filters define a restriction on the query. For example you want to restrict the output to all genes located on the human X chromosome then the filter chromosome_name can be used with value ‘X’. The listFilters() function shows you all available filters in the selected dataset.
filters = listFilters(ensembl)
filters[1:5,]## name description
## 1 chromosome_name Chromosome/scaffold name
## 2 start Start
## 3 end End
## 4 band_start Band Start
## 5 band_end Band EndAttributes define the values we are interested in to retrieve. For example we want to retrieve the gene symbols or chromosomal coordinates. The listAttributes() function displays all available attributes in the selected dataset.
attributes = listAttributes(ensembl)
attributes[1:5,]## name description page
## 1 ensembl_gene_id Gene stable ID feature_page
## 2 ensembl_gene_id_version Gene stable ID version feature_page
## 3 ensembl_transcript_id Transcript stable ID feature_page
## 4 ensembl_transcript_id_version Transcript stable ID version feature_page
## 5 ensembl_peptide_id Protein stable ID feature_pageThe getBM() function is the main query function in biomaRt. It has four main arguments:
attributes: is a vector of attributes that one wants to retrieve (= the output of the query).filters: is a vector of filters that one wil use as input to the query.values: a vector of values for the filters. In case multple filters are in use, the values argument requires a list of values where each position in the list corresponds to the position of the filters in the filters argument (see examples below).mart: is an object of class Mart, which is created by the useMart() function.Now that we selected a BioMart database and dataset, and know about attributes, filters, and the values for filters; we can build a biomaRt query. Let’s make an easy query for the following problem: We have a list of Affymetrix identifiers from the u133plus2 platform and we want to retrieve the corresponding EntrezGene identifiers using the Ensembl mappings.
The u133plus2 platform will be the filter for this query and as values for this filter we use our list of Affymetrix identifiers. As output (attributes) for the query we want to retrieve the EntrezGene and u133plus2 identifiers so we get a mapping of these two identifiers as a result. The exact names that we will have to use to specify the attributes and filters can be retrieved with the listAttributes() and listFilters() function respectively. Let’s now run the query:
affyids=c("202763_at","209310_s_at","207500_at")
getBM(attributes=c('affy_hg_u133_plus_2', 'entrezgene_id'),
filters = 'affy_hg_u133_plus_2',
values = affyids,
mart = ensembl)## affy_hg_u133_plus_2 entrezgene_id
## 1 202763_at 836
## 2 209310_s_at 837
## 3 207500_at 838To save time and computing resources biomaRt will attempt to identify when you are re-running a query you have executed before. Each time a new query is run, the results will be saved to a cache on your computer. If a query is identified as having been run previously, rather than submitting the query to the server, the results will be loaded from the cache.
You can get some information on the size and location of the cache using the function biomartCacheInfo():
biomartCacheInfo()## biomaRt cache
## - Location: ~/.cache/biomaRt
## - No. of files: 869
## - Total size: 126.7 MbThe cache can be deleted using the command biomartCacheClear(). This will remove all cached files.
There are a small number of non-Ensembl databases that offer a BioMart interface to their data. The biomaRt package can be used to access these in a very similar fashion to Ensembl. The majority of biomaRt functions will work in the same manner, but the construction of the initial Mart object requires slightly more setup. In this section we demonstrate the setting requires to query Wormbase ParaSite and Phytozome
To demonstrate the use of the biomaRt package with non-Ensembl databases the next query is performed using the Wormbase ParaSite BioMart. In this example, we use the listMarts() function to find the name of the available marts, given the URL of Wormbase. We use this to connect to Wormbase BioMart using the useMart() function. Note that we use the https address and must provide the port as 443. Queries to WormBase will fail without these options.
listMarts(host = "parasite.wormbase.org")## biomart version
## 1 parasite_mart WBPS 15 Martwormbase <- useMart(biomart = "parasite_mart",
host = "https://parasite.wormbase.org",
port = 443)We can then use functions described earlier in this vignette to find and select the gene dataset, and print the first 6 available attributes and filters. Then we use a list of gene names as filter and retrieve associated transcript IDs and the transcript biotype.
listDatasets(wormbase)## dataset description version
## 1 wbps_gene All Species (WBPS15) 15wormbase <- useDataset(mart = wormbase, dataset = "wbps_gene")
head(listFilters(wormbase))## name description
## 1 species_id_1010 Genome
## 2 nematode_clade_1010 Nematode Clade
## 3 chromosome_name Chromosome name
## 4 start Start
## 5 end End
## 6 strand Strandhead(listAttributes(wormbase))## name description page
## 1 species_id_key Internal Name feature_page
## 2 production_name_1010 Genome project feature_page
## 3 display_name_1010 Genome name feature_page
## 4 taxonomy_id_1010 Taxonomy ID feature_page
## 5 assembly_accession_1010 Assembly accession feature_page
## 6 nematode_clade_1010 Nematode clade feature_pagegetBM(attributes = c("external_gene_id", "wbps_transcript_id", "transcript_biotype"),
filters = "gene_name",
values = c("unc-26","his-33"),
mart = wormbase)## external_gene_id wbps_transcript_id transcript_biotype
## 1 his-33 F17E9.13.1 protein_coding
## 2 unc-26 JC8.10a.1 protein_coding
## 3 unc-26 JC8.10b.1 protein_coding
## 4 unc-26 JC8.10c.1 protein_coding
## 5 unc-26 JC8.10c.2 protein_coding
## 6 unc-26 JC8.10d.1 protein_codingThe Phytozome BioMart can be accessed with the following settings. Note that we use the https address. Queries to Phytozome will fail without specifying this.
listMarts(host = "https://phytozome.jgi.doe.gov")## biomart version
## 1 phytozome_mart V12 Genomes and Families
## 2 phytozome_diversity__mart V12 Genome Diversity
## 3 phytozome_mart_archive Genome Archivephytozome <- useMart(biomart = "phytozome_mart",
host = "https://phytozome.jgi.doe.gov")
listDatasets(phytozome)## dataset description version
## 1 phytozome Phytozome 12 Genomes
## 2 phytozome_clusters Phytozome 12 Familieswormbase <- useDataset(mart = phytozome, dataset = "phytozome")Once this is set up the usual biomaRt functions can be used to interogate the database options and run queries.
getBM(attributes = c("organism_name", "gene_name1"),
filters = "gene_name_filter",
values = "82092",
mart = phytozome)## Error in martCheck(mart): No dataset selected, please select a dataset first. You can see the available datasets by using the listDatasets function see ?listDatasets for more information. Then you should create the Mart object by using the useMart function. See ?useMart for more informationVersion 13 of Phyotozome can be found at https://phytozome-next.jgi.doe.gov/ and if you wish to query that version the URL used to create the Mart object must reflect that.
phytozome_v13 <- useMart(biomart = "phytozome_mart",
dataset = "phytozome",
host = "https://phytozome-next.jgi.doe.gov")The biomaRt package can be used with a local install of a public BioMart database or a locally developed BioMart database and web service.
In order for biomaRt to recognize the database as a BioMart, make sure that the local database you create has a name conform with database_mart_version where database is the name of the database and version is a version number. No more underscores than the ones showed should be present in this name. A possible name is for example ensemblLocal_mart_46.
## Minimum requirements for local database installation
More information on installing a local copy of a BioMart database or develop your own BioMart database and webservice can be found on http://www.biomart.org
Once the local database is installed you can use biomaRt on this database by:
listMarts(host="www.myLocalHost.org", path="/myPathToWebservice/martservice")
mart=useMart("nameOfMyMart",dataset="nameOfMyDataset",host="www.myLocalHost.org", path="/myPathToWebservice/martservice")For more information on how to install a public BioMart database see: http://www.biomart.org/install.html and follow link databases.
select()In order to provide a more consistent interface to all annotations in
Bioconductor the select(), columns(),
keytypes() and keys() have been implemented to wrap
some of the existing functionality above. These methods can be called
in the same manner that they are used in other parts of the project
except that instead of taking a AnnotationDb derived class
they take instead a Mart derived class as their 1st argument.
Otherwise usage should be essentially the same. You still use
columns() to discover things that can be extracted from a
Mart, and keytypes() to discover which things can be
used as keys with select().
mart <- useMart(dataset="hsapiens_gene_ensembl",biomart='ensembl')## Ensembl site unresponsive, trying asia mirrorhead(keytypes(mart), n=3)## [1] "affy_hc_g110" "affy_hg_focus" "affy_hg_u133_plus_2"head(columns(mart), n=3)## [1] "3_utr_end" "3_utr_end" "3_utr_start"And you still can use keys() to extract potential keys, for a
particular key type.
k = keys(mart, keytype="chromosome_name")
head(k, n=3)## [1] "1" "2" "3"When using keys(), you can even take advantage of the extra
arguments that are available for others keys methods.
k = keys(mart, keytype="chromosome_name", pattern="LRG")
head(k, n=3)## character(0)Unfortunately the keys() method will not work with all key
types because they are not all supported.
But you can still use select() here to extract columns of data
that match a particular set of keys (this is basically a wrapper for
getBM()).
affy=c("202763_at","209310_s_at","207500_at")
select(mart, keys=affy, columns=c('affy_hg_u133_plus_2','entrezgene_id'),
keytype='affy_hg_u133_plus_2')## affy_hg_u133_plus_2 entrezgene_id
## 1 202763_at 836
## 2 209310_s_at 837
## 3 207500_at 838So why would we want to do this when we already have functions like getBM()? For two reasons: 1) for people who are familiar
with select and it’s helper methods, they can now proceed to use biomaRt making the same kinds of calls that are already familiar to them and 2) because the select method is implemented in many places elsewhere, the fact that these methods are shared allows for more convenient programmatic access of all these resources. An example of a package that takes advantage of this is the OrganismDbi package. Where several packages can be accessed as if they were one resource.
Note: if the function useMart() runs into proxy problems you should set your proxy first before calling any biomaRt functions.
You can do this using the Sys.putenv command:
Sys.setenv("http_proxy" = "http://my.proxy.org:9999")Some users have reported that the workaround above does not work, in this case an alternative proxy solution below can be tried:
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