Ensembl BiomartNOTE: To make sure that you have a sufficiently stable (internet) connection between R and the respective databases, please set the default
timeoutsetting on your local machine from 60sec to at least 30000sec before running any retrieval functions via:
The Ensembl Biomart database enables users to retrieve a
vast diversity of annotation data for specific organisms. Initially,
Steffen Durinck and Wolfgang Huber provided a powerful interface between
the R language and Ensembl Biomart by implementing the R
package biomaRt.
The purpose of the biomaRt package was to mimic the
ENSEMBL BioMart database structure to construct queries that can be sent
to the Application Programming Interface (API) of BioMart. Although,
this procedure was very useful in the past, it seems not intuitive from
an organism centric point of view. Usually, users wish to download
functional annotation for a particular organism of interest. However,
the BioMart and thus the biomaRt package require that users
already know in which mart and dataset the
organism of interest will be found which requires significant efforts of
searching and screening. In addition, once the mart and
dataset of a particular organism of interest were found and
specified the user must again learn which attribute has to
be specified to retrieve the functional annotation information of
interest.
The new functionality implemented in the biomartr
package aims to overcome this search bottleneck by extending the
functionality of the biomaRt
package. The new biomartr package introduces a more
organism cantered annotation retrieval concept which does not require to
screen for marts, datasets, and
attributes beforehand. With biomartr users
only need to specify the scientific name of the organism of
interest to then retrieve available marts,
datasets, and attributes for the corresponding
organism of interest.
This paradigm shift enables users to quickly construct queries to the BioMart database without having to learn the particular database structure and organization of BioMart.
The following sections will introduce users to the functionality and
data retrieval precedures of biomartr and will show how
biomartr extends the functionality of the initial biomaRt
package.
biomaRt query methodologyThe best way to get started with the old methodology
presented by the established biomaRt
package is to understand the workflow of its data retrieval process. The
query logic of the biomaRt package derives from the
database organization of Ensembl Biomart which stores a
vast diversity of annotation data for specific organisms. In detail, the
Ensembl Biomart database is organized into so called:
marts, datasets, and attributes.
Marts denote a higher level category of functional
annotation such as SNP (e.g. for functional annotation of
particular single nucleotide polymorphisms (SNPs)) or
FUNCGEN (e.g. for functional annotation of regulatory
regions or relationsships of genes). Datasets denote the
particular species of interest for which functional annotation is
available within this specific mart. It
can happen that datasets (= particular species of interest)
are available in one mart (= higher category of functional
annotation) but not in an other mart. For the actual
retrieval of functional annotation information users must then specify
the type of functional annotation information they wish to
retrieve. These types are called attributes in
the biomaRt notation.
Hence, when users wish to retrieve information for a specific
organism of interest, they first need to specify a particular
mart and dataset in which the information of
the corresponding organism of interest can be found. Subsequently they
can specify the attributes argument to retrieve a
particular type of functional annotation (e.g. Gene Ontology terms).
The following section shall illustrate how marts,
datasets, and attributes could be explored
using biomaRt before the biomartr package
existed.
The availability of marts, datasets, and
attributes can be checked by the following functions:
# install the biomaRt package
# source("https://bioconductor.org/biocLite.R")
# biocLite("biomaRt")
# load biomaRt
library(biomaRt)
# look at top 10 databases
head(biomaRt::listMarts(host = "https://www.ensembl.org"), 10) Users will observe that several marts providing
annotation for specific classes of organisms or groups of organisms are
available.
For our example, we will choose the
hsapiens_gene_ensembl mart and list all
available datasets that are element of this mart.
head(biomaRt::listDatasets(biomaRt::useMart("ENSEMBL_MART_ENSEMBL", host = "https://www.ensembl.org")), 10) The useMart() function is a wrapper function provided by
biomaRt to connect a selected BioMart database
(mart) with a corresponding dataset stored within this
mart.
We select dataset hsapiens_gene_ensembl and now check for
available attributes (annotation data) that can be accessed for
Homo sapiens genes.
head(biomaRt::listAttributes(biomaRt::useDataset(
dataset = "hsapiens_gene_ensembl",
mart = useMart("ENSEMBL_MART_ENSEMBL",
host = "https://www.ensembl.org"))), 10) Please note the nested structure of this attribute query. For an
attribute query procedure an additional wrapper function named
useDataset() is needed in which useMart() and
a corresponding dataset needs to be specified. The result is a table
storing the name of available attributes for
Homo sapiens as well as a short description.
Furthermore, users can retrieve all filters for Homo sapiens that can be specified by the actual BioMart query process.
head(biomaRt::listFilters(biomaRt::useDataset(dataset = "hsapiens_gene_ensembl",
mart = useMart("ENSEMBL_MART_ENSEMBL",
host = "https://www.ensembl.org"))), 10) After accumulating all this information, it is now possible to
perform an actual BioMart query by using the getBM()
function.
In this example we will retrieve attributes:
start_position,end_position and
description
for the Homo sapiens gene "GUCA2A".
Since the input genes are ensembl gene ids, we need to
specify the filters argument
filters = "hgnc_symbol".
# 1) select a mart and data set
mart <- biomaRt::useDataset(dataset = "hsapiens_gene_ensembl",
mart = useMart("ENSEMBL_MART_ENSEMBL",
host = "https://www.ensembl.org"))
# 2) run a biomart query using the getBM() function
# and specify the attributes and filter arguments
geneSet <- "GUCA2A"
resultTable <- biomaRt::getBM(attributes = c("start_position","end_position","description"),
filters = "hgnc_symbol",
values = geneSet,
mart = mart)
resultTable When using getBM() users can pass all attributes
retrieved by listAttributes() to the
attributes argument of the getBM()
function.
biomaRt using the new query system of the
biomartr packagebiomartrThis query methodology provided by Ensembl Biomart and
the biomaRt package is a very well defined approach for
accurate annotation retrieval. Nevertheless, when learning this query
methodology it (subjectively) seems non-intuitive from the user
perspective. Therefore, the biomartr package provides
another query methodology that aims to be more organism centric.
Taken together, the following workflow allows users to perform fast
BioMart queries for attributes using the biomart() function
implemented in this biomartr package:
get attributes, datasets, and marts via :
organismAttributes()
choose available biological features (filters) via:
organismFilters()
specify a set of query genes: e.g. retrieved with
getGenome(), getProteome() or
getCDS()
specify all arguments of the biomart() function
using steps 1) - 3) and perform a BioMart query
Note that dataset names change very frequently due to the
update of dataset versions. So in case some query functions do not work
properly, users should check with
organismAttributes(update = TRUE) whether or not their
dataset name has been changed. For example,
organismAttributes("Homo sapiens", topic = "id", update = TRUE)
might reveal that the dataset ENSEMBL_MART_ENSEMBL has
changed.
The getMarts() function allows users to list all
available databases that can be accessed through BioMart interfaces.
mart version
<chr> <chr>
1 ENSEMBL_MART_ENSEMBL Ensembl Genes 104
2 ENSEMBL_MART_MOUSE Mouse strains 104
3 ENSEMBL_MART_SEQUENCE Sequence
4 ENSEMBL_MART_ONTOLOGY Ontology
5 ENSEMBL_MART_GENOMIC Genomic features 104
6 ENSEMBL_MART_SNP Ensembl Variation 104
7 ENSEMBL_MART_FUNCGEN Ensembl Regulation 104
8 plants_mart Ensembl Plants Genes 51
9 plants_variations Ensembl Plants Variations 51
10 fungi_mart Ensembl Fungi Genes 51
11 fungi_variations Ensembl Fungi Variations 51
12 protists_mart Ensembl Protists Genes 51
13 protists_variations Ensembl Protists Variations 51
14 metazoa_mart Ensembl Metazoa Genes 51
15 metazoa_variations Ensembl Metazoa Variations 51 Now users can select a specific database to list all available data
sets that can be accessed through this database. In this example we
choose the ENSEMBL_MART_ENSEMBL database.
dataset description version
<chr> <chr> <chr>
1 fcatus_gene_ensembl Cat genes (Felis_catus_9.0) Felis_catus
2 umaritimus_gene_ensembl Polar bear genes (UrsMar_1.0) UrsMar_1.0
3 ogarnettii_gene_ensembl Bushbaby genes (OtoGar3) OtoGar3
4 lcrocea_gene_ensembl Large yellow croaker genes (L_cro L_crocea_2.0
5 sformosus_gene_ensembl Asian bonytongue genes (fSclFor1. fSclFor1.1 Now you can select the dataset hsapiens_gene_ensembl and
list all available attributes that can be retrieved from this
dataset.
1 csabaeus_gene_ensembl Vervet-AGM genes (ChlSab1.1) ChlSab1.1
2 chircus_gene_ensembl Goat genes (ARS1) ARS1
3 mmulatta_gene_ensembl Macaque genes (Mmul_10) Mmul_10
4 mmonoceros_gene_ensembl Narwhal genes (NGI_Narwhal_1) NGI_Narwhal_1
5 csemilaevis_gene_ensembl Tongue sole genes (Cse_v1.0) Cse_v1.0
6 cpbellii_gene_ensembl Painted turtle genes (Chryse Chrysemys_pict
7 clanigera_gene_ensembl Long-tailed chinchilla genes ChiLan1.0
8 catys_gene_ensembl Sooty mangabey genes (Caty_1 Caty_1.0
9 tguttata_gene_ensembl Zebra finch genes (bTaeGut1_ bTaeGut1_v1.p
10 nleucogenys_gene_ensembl Gibbon genes (Nleu_3.0) Nleu_3.0
# with 28 more rowsNow that you have selected a database
(hsapiens_gene_ensembl) and a dataset
(hsapiens_gene_ensembl), users can list all available
attributes for this dataset using the getAttributes()
function.
# show all elements of the data.frame
options(tibble.print_max = Inf)
# list all available attributes for dataset: hsapiens_gene_ensembl
head( biomartr::getAttributes(mart = "ENSEMBL_MART_ENSEMBL",
dataset = "hsapiens_gene_ensembl"), 10 )Starting retrieval of attribute information from mart ENSEMBL_MART_ENSEMBL and dataset hsapiens_gene_ensembl ...
name description
1 ensembl_gene_id Gene stable ID
2 ensembl_gene_id_version Gene stable ID version
3 ensembl_transcript_id Transcript stable ID
4 ensembl_transcript_id_version Transcript stable ID version
5 ensembl_peptide_id Protein stable ID
6 ensembl_peptide_id_version Protein stable ID version
7 ensembl_exon_id Exon stable ID
8 description Gene description
9 chromosome_name Chromosome/scaffold name
10 start_position Gene start (bp)Finally, the getFilters() function allows users to list
available filters for a specific dataset that can be used for a
biomart() query.
# show all elements of the data.frame
options(tibble.print_max = Inf)
# list all available filters for dataset: hsapiens_gene_ensembl
head( biomartr::getFilters(mart = "ENSEMBL_MART_ENSEMBL",
dataset = "hsapiens_gene_ensembl"), 10 )Starting retrieval of filters information from mart ENSEMBL_MART_ENSEMBL and dataset hsapiens_gene_ensembl ...
name description
1 chromosome_name Chromosome/scaffold name
2 start Start
3 end End
4 band_start Band Start
5 band_end Band End
6 marker_start Marker Start
7 marker_end Marker End
8 encode_region Encode region
9 strand Strand
10 chromosomal_region e.g. 1:100:10000:-1, 1:100000:200000:1In most use cases, users will work with a single or a set of model
organisms. In this process they will mostly be interested in specific
annotations for this particular model organism. The
organismBM() function addresses this issue and provides
users with an organism centric query to marts and
datasets which are available for a particular organism of
interest.
Note that when running the following functions for
the first time, the data retrieval procedure will take some time, due to
the remote access to BioMart. The corresponding result is then saved in
a *.txt file named _biomart/listDatasets.txt
within the tempdir() folder, allowing subsequent queries to
be performed much faster. The tempdir() folder, however,
will be deleted after a new R session was established. In this case the
inital call of the subsequent functions again will take time to retrieve
all organism specific data from the BioMart database.
This concept of locally storing all organism specific database linking information available in BioMart into an internal file allows users to significantly speed up subsequent retrieval queries for that particular organism.
# show all elements of the data.frame
options(tibble.print_max = Inf)
# retrieving all available datasets and biomart connections for
# a specific query organism (scientific name)
biomartr::organismBM(organism = "Homo sapiens") Starting retrieval of all available BioMart datasets for Homo sapiens ...
Datasets for the following marts will be retrieved:
mart version
1 ENSEMBL_MART_ENSEMBL Ensembl Genes 104
2 ENSEMBL_MART_MOUSE Mouse strains 104
3 ENSEMBL_MART_SEQUENCE Sequence
4 ENSEMBL_MART_ONTOLOGY Ontology
5 ENSEMBL_MART_GENOMIC Genomic features 104
6 ENSEMBL_MART_SNP Ensembl Variation 104
7 ENSEMBL_MART_FUNCGEN Ensembl Regulation 104
8 plants_mart Ensembl Plants Genes 51
9 plants_variations Ensembl Plants Variations 51
10 fungi_mart Ensembl Fungi Genes 51
11 fungi_variations Ensembl Fungi Variations 51
12 protists_mart Ensembl Protists Genes 51
13 protists_variations Ensembl Protists Variations 51
14 metazoa_mart Ensembl Metazoa Genes 51
Processing mart ENSEMBL_MART_ENSEMBL ...
Processing mart ENSEMBL_MART_MOUSE ...
Processing mart ENSEMBL_MART_SEQUENCE ...
Processing mart ENSEMBL_MART_ONTOLOGY ...
Processing mart ENSEMBL_MART_GENOMIC ...
Processing mart ENSEMBL_MART_SNP ...
Processing mart ENSEMBL_MART_FUNCGEN ...
Processing mart plants_mart ...
Processing mart plants_variations ...
Processing mart fungi_mart ...
Processing mart fungi_variations ...
Processing mart protists_mart ...
Processing mart protists_variations ...
Processing mart metazoa_mart ...
A tibble: 15 x 5
organism_name description mart dataset version
<chr> <chr> <chr> <chr> <chr>
1 hsapiens Human genes (GRCh38.p13) ENSEMBL_ hsapiens_ge GRCh38
2 hsapiens Human sequences (GRCh38. ENSEMBL_ hsapiens_ge GRCh38
3 hsapiens encode ENSEMBL_ hsapiens_en GRCh38
4 hsapiens marker_feature_end ENSEMBL_ hsapiens_ma GRCh38
5 hsapiens marker_feature ENSEMBL_ hsapiens_ma GRCh38
6 hsapiens karyotype_end ENSEMBL_ hsapiens_ka GRCh38
7 hsapiens karyotype_start ENSEMBL_ hsapiens_ka GRCh38
8 hsapiens Human Somatic Short Vari ENSEMBL_ hsapiens_sn GRCh38
9 hsapiens Human Structural Variant ENSEMBL_ hsapiens_st GRCh38
10 hsapiens Human Short Variants (SN ENSEMBL_ hsapiens_snp GRCh38
11 hsapiens Human Somatic Structural ENSEMBL_ hsapiens_st GRCh38
12 hsapiens Human Regulatory Evidenc ENSEMBL_ hsapiens_pe GRCh38
13 hsapiens Human Regulatory Feature ENSEMBL_ hsapiens_re GRCh38
14 hsapiens Human Other Regulatory R ENSEMBL_ hsapiens_ex GRCh38
15 hsapiens Human miRNA Target Regio ENSEMBL_ hsapiens_mi GRCh38The result is a table storing all marts and
datasets from which annotations can be retrieved for
Homo sapiens. Furthermore, a short description as well as the
version of the data set being accessed (very useful for publications) is
returned.
Users will observe that 3 different marts provide 6
different datasets storing annotation information for
Homo sapiens.
**_Please note__*, however, that scientific names of organisms must be written correctly! For ex. “Homo Sapiens” will be treated differently (not recognized) than “Homo sapiens” (recognized).__
Similar to the biomaRt package query methodology, users
need to specify attributes and filters to be
able to perform accurate BioMart queries. Here the functions
organismAttributes() and organismFilters()
provide useful and intuitive concepts to obtain this information.
# show all elements of the data.frame
options(tibble.print_max = Inf)
# return available attributes for "Homo sapiens"
head(biomartr::organismAttributes("Homo sapiens"), 20)1 ensembl_gene_id Gene stable ID hsapiens_ge ENSEMBL_M
2 ensembl_gene_id_version Gene stable ID version hsapiens_ge ENSEMBL_M
3 ensembl_transcript_id Transcript stable ID hsapiens_ge ENSEMBL_M
4 ensembl_transcript_id_version Transcript stable ID hsapiens_ge ENSEMBL_M
5 ensembl_peptide_id Protein stable ID hsapiens_ge ENSEMBL_M
6 ensembl_peptide_id_version Protein stable ID ver hsapiens_ge ENSEMBL_M
7 ensembl_exon_id Exon stable ID hsapiens_ge ENSEMBL_M
8 description Gene description hsapiens_ge ENSEMBL_M
9 chromosome_name Chromosome/scaffold n hsapiens_ge ENSEMBL_M
10 start_position Gene start (bp) hsapiens_ge ENSEMBL_M
11 end_position Gene end (bp) hsapiens_ge ENSEMBL_M
12 strand Strand hsapiens_ge ENSEMBL_M
13 band Karyotype band hsapiens_ge ENSEMBL_M
14 transcript_start Transcript start (bp) hsapiens_ge ENSEMBL_M
15 transcript_end Transcript end (bp) hsapiens_ge ENSEMBL_M
16 transcription_start_site Transcription start s hsapiens_ge ENSEMBL_M
17 transcript_length Transcript length (in hsapiens_ge ENSEMBL_M
18 transcript_tsl Transcript support le hsapiens_ge ENSEMBL_M
19 transcript_gencode_basic GENCODE basic annotat hsapiens_ge ENSEMBL_M
20 transcript_appris APPRIS annotation hsapiens_ge ENSEMBL_MUsers will observe that the organismAttributes()
function returns a data.frame storing attribute names, data sets, and
marts which are available for Homo sapiens. After the
ENSEMBL release 87 the ENSEMBL_MART_SEQUENCE service
provided by Ensembl does not work properly and thus the
organismAttributes() function prints out warning messages
to make the user aware when certain marts provided by Ensembl do not
work properly, yet.
An additional feature provided by organismAttributes()
is the topic argument. The topic argument
allows users to to search for specific attributes, topics, or categories
for faster filtering.
# show all elements of the data.frame
options(tibble.print_max = Inf)
# search for attribute topic "id"
head(biomartr::organismAttributes("Homo sapiens", topic = "id"), 20) name description dataset mart
<chr> <chr> <chr> <chr>
1 ensembl_gene_id Gene stable ID hsapiens_ge ENSEMBL_M
2 ensembl_gene_id_version Gene stable ID version hsapiens_ge ENSEMBL_M
3 ensembl_transcript_id Transcript stable ID hsapiens_ge ENSEMBL_M
4 ensembl_transcript_id_version Transcript stable ID hsapiens_ge ENSEMBL_M
5 ensembl_peptide_id Protein stable ID hsapiens_ge ENSEMBL_M
6 ensembl_peptide_id_version Protein stable ID ver hsapiens_ge ENSEMBL_M
7 ensembl_exon_id Exon stable ID hsapiens_ge ENSEMBL_M
8 study_external_id Study external refere hsapiens_ge ENSEMBL_M
9 go_id GO term accession hsapiens_ge ENSEMBL_M
10 dbass3_id DataBase of Aberrant hsapiens_ge ENSEMBL_M
11 dbass5_id DataBase of Aberrant hsapiens_ge ENSEMBL_M
12 hgnc_id HGNC ID hsapiens_ge ENSEMBL_M
13 protein_id INSDC protein ID hsapiens_ge ENSEMBL_M
14 mim_morbid_description MIM morbid description hsapiens_ge ENSEMBL_M
15 mim_morbid_accession MIM morbid accession hsapiens_ge ENSEMBL_M
16 mirbase_id miRBase ID hsapiens_ge ENSEMBL_M
17 refseq_peptide RefSeq peptide ID hsapiens_ge ENSEMBL_M
18 refseq_peptide_predicted RefSeq peptide predic hsapiens_ge ENSEMBL_M
19 wikigene_id WikiGene ID hsapiens_ge ENSEMBL_M
20 mobidblite MobiDBLite hsapiens_ge ENSEMBL_MNow, all attribute names having id as part
of their name are being returned.
Another example is topic = "homolog".
# show all elements of the data.frame
options(tibble.print_max = Inf)
# search for attribute topic "homolog"
head(biomartr::organismAttributes("Homo sapiens", topic = "homolog"), 20) <chr> <chr> <chr> <chr>
1 mspretus_homolog_ensembl_gene Algerian mouse hsapie ENSEM
2 mspretus_homolog_associated_gene_name Algerian mouse hsapie ENSEM
3 mspretus_homolog_ensembl_peptide Algerian mouse hsapie ENSEM
4 mspretus_homolog_chromosome Algerian mouse hsapie ENSEM
5 mspretus_homolog_chrom_start Algerian mouse hsapie ENSEM
6 mspretus_homolog_chrom_end Algerian mouse hsapie ENSEM
7 mspretus_homolog_canonical_transcript_protein Query protein hsapie ENSEM
8 mspretus_homolog_subtype Last common an hsapie ENSEM
9 mspretus_homolog_orthology_type Algerian mouse hsapie ENSEM
10 mspretus_homolog_perc_id %id. target Al hsapie ENSEM
11 mspretus_homolog_perc_id_r1 %id. query gen hsapie ENSEM
12 mspretus_homolog_goc_score Algerian mouse hsapie ENSEM
13 mspretus_homolog_wga_coverage Algerian mouse hsapie ENSEM
14 mspretus_homolog_dn dN with Algeri hsapie ENSEM
15 mspretus_homolog_ds dS with Algeri hsapie ENSEM
16 mspretus_homolog_orthology_confidence Algerian mouse hsapie ENSEM
17 vpacos_homolog_ensembl_gene Alpaca gene st hsapie ENSEM
18 vpacos_homolog_associated_gene_name Alpaca gene na hsapie ENSEM
19 vpacos_homolog_ensembl_peptide Alpaca protein hsapie ENSEM
20 vpacos_homolog_chromosome Alpaca chromos hsapie ENSEMOr topic = "dn" and topic = "ds" for
dn and ds value retrieval.
# show all elements of the data.frame
options(tibble.print_max = Inf)
# search for attribute topic "dn"
head(biomartr::organismAttributes("Homo sapiens", topic = "dn")) name description dataset mart
<chr> <chr> <chr> <chr>
1 cdna_coding_start cDNA coding start hsapiens_gene_ensembl ENSEMBL_M
2 cdna_coding_end cDNA coding end hsapiens_gene_ensembl ENSEMBL_M
3 mspretus_homolog_dn dN with Algerian mouse hsapiens_gene_ensembl ENSEMBL_M
4 vpacos_homolog_dn dN with Alpaca hsapiens_gene_ensembl ENSEMBL_M
5 pformosa_homolog_dn dN with Amazon molly hsapiens_gene_ensembl ENSEMBL_M
6 cpalliatus_homolog_dn dN with Angola colobus hsapiens_gene_ensembl ENSEMBL_M# show all elements of the data.frame
options(tibble.print_max = Inf)
# search for attribute topic "ds"
head(biomartr::organismAttributes("Homo sapiens", topic = "ds")) name description dataset mart
<chr> <chr> <chr> <chr>
1 ccds CCDS ID hsapiens_gene_ensembl ENSEMBL_MAR
2 cds_length CDS Length hsapiens_gene_ensembl ENSEMBL_MAR
3 cds_start CDS start hsapiens_gene_ensembl ENSEMBL_MAR
4 cds_end CDS end hsapiens_gene_ensembl ENSEMBL_MAR
5 mspretus_homolog_ds dS with Algerian mouse hsapiens_gene_ensembl ENSEMBL_MAR
6 vpacos_homolog_ds dS with Alpaca hsapiens_gene_ensembl ENSEMBL_MARAnalogous to the organismAttributes() function, the
organismFilters() function returns all filters that are
available for a query organism of interest.
# show all elements of the data.frame
options(tibble.print_max = Inf)
# return available filters for "Homo sapiens"
head(biomartr::organismFilters("Homo sapiens"), 20) name description dataset mart
<chr> <chr> <chr> <chr>
1 chromosome_name Chromosome/scaffold hsapiens_ ENSEMBL
2 start Start hsapiens_ ENSEMBL
3 end End hsapiens_ ENSEMBL
4 band_start Band Start hsapiens_ ENSEMBL
5 band_end Band End hsapiens_ ENSEMBL
6 marker_start Marker Start hsapiens_ ENSEMBL
7 marker_end Marker End hsapiens_ ENSEMBL
8 encode_region Encode region hsapiens_ ENSEMBL
9 strand Strand hsapiens_ ENSEMBL
10 chromosomal_region e.g. 1:100:10000:-1 hsapiens_ ENSEMBL
11 with_ccds With CCDS ID(s) hsapiens_ ENSEMBL
12 with_chembl With ChEMBL ID(s) hsapiens_ ENSEMBL
13 with_clone_based_ensembl_gene With Clone-based (E hsapiens_ ENSEMBL
14 with_clone_based_ensembl_transcript With Clone-based (E hsapiens_ ENSEMBL
15 with_dbass3 With DataBase of Ab hsapiens_ ENSEMBL
16 with_dbass5 With DataBase of Ab hsapiens_ ENSEMBL
17 with_ens_hs_transcript With Ensembl Human hsapiens_ ENSEMBL
18 with_ens_hs_translation With Ensembl Human hsapiens_ ENSEMBL
19 with_entrezgene_trans_name With EntrezGene tra hsapiens_ ENSEMBL
20 with_embl With European Nucle hsapiens_ ENSEMBLThe organismFilters() function also allows users to
search for filters that correspond to a specific topic or category.
# show all elements of the data.frame
options(tibble.print_max = Inf)
# search for filter topic "id"
head(biomartr::organismFilters("Homo sapiens", topic = "id"), 20) name description dataset mart
<chr> <chr> <chr> <chr>
1 with_protein_id With INSDC protein ID ID hsapiens_g ENSEMBL
2 with_mim_morbid With MIM morbid ID(s) hsapiens_g ENSEMBL
3 with_refseq_peptide With RefSeq peptide ID(s) hsapiens_g ENSEMBL
4 with_refseq_peptide_predicted With RefSeq peptide pred hsapiens_g ENSEMBL
5 ensembl_gene_id Gene stable ID(s) [e.g. hsapiens_g ENSEMBL
6 ensembl_gene_id_version Gene stable ID(s) with v hsapiens_g ENSEMBL
7 ensembl_transcript_id Transcript stable ID(s) hsapiens_g ENSEMBL
8 ensembl_transcript_id_version Transcript stable ID(s) hsapiens_g ENSEMBL
9 ensembl_peptide_id Protein stable ID(s) [e. hsapiens_g ENSEMBL
10 ensembl_peptide_id_version Protein stable ID(s) wit hsapiens_g ENSEMBL
11 ensembl_exon_id Exon ID(s) [e.g. ENSE000 hsapiens_g ENSEMBL
12 dbass3_id DataBase of Aberrant 3' hsapiens_g ENSEMBL
13 dbass5_id DataBase of Aberrant 5' hsapiens_g ENSEMBL
14 hgnc_id HGNC ID(s) [e.g. HGNC:10 hsapiens_g ENSEMBL
15 protein_id INSDC protein ID(s) [e.g hsapiens_g ENSEMBL
16 mim_morbid_accession MIM morbid accession(s) hsapiens_g ENSEMBL
17 mirbase_id miRBase ID(s) [e.g. hsa- hsapiens_g ENSEMBL
18 refseq_peptide RefSeq peptide ID(s) [e. hsapiens_g ENSEMBL
19 refseq_peptide_predicted RefSeq peptide predicted hsapiens_g ENSEMBL
20 wikigene_id WikiGene ID(s) [e.g. 1] hsapiens_g ENSEMBLbiomartrThe short introduction to the functionality of
organismBM(), organismAttributes(), and
organismFilters() will allow users to perform BioMart
queries in a very intuitive organism centric way. The main function to
perform BioMart queries is biomart().
For the following examples we will assume that we are interested in
the annotation of specific genes from the Homo sapiens
proteome. We want to map the corresponding refseq gene id to a set of
other gene ids used in other databases. For this purpose, first we need
consult the organismAttributes() function.
# show all elements of the data.frame
options(tibble.print_max = Inf)
head(biomartr::organismAttributes("Homo sapiens", topic = "id")) name description dataset mart
<chr> <chr> <chr> <chr>
1 ensembl_gene_id Gene stable ID hsapiens_ ENSEMB
2 ensembl_gene_id_version Gene stable ID version hsapiens_ ENSEMB
3 ensembl_transcript_id Transcript stable ID hsapiens_ ENSEMB
4 ensembl_transcript_id_version Transcript stable ID version hsapiens_ ENSEMB
5 ensembl_peptide_id Protein stable ID hsapiens_ ENSEMB
6 ensembl_peptide_id_version Protein stable ID version hsapiens_ ENSEMB# show all elements of the data.frame
options(tibble.print_max = Inf)
# retrieve the proteome of Homo sapiens from refseq
file_path <- biomartr::getProteome( db = "refseq",
organism = "Homo sapiens",
path = file.path("_ncbi_downloads","proteomes") )
Hsapiens_proteome <- biomartr::read_proteome(file_path, format = "fasta")
# remove splice variants from id
gene_set <- unlist(sapply(strsplit(Hsapiens_proteome@ranges@NAMES[1:5], ".",fixed = TRUE), function(x) x[1]))
result_BM <- biomartr::biomart( genes = gene_set, # genes were retrieved using biomartr::getGenome()
mart = "ENSEMBL_MART_ENSEMBL", # marts were selected with biomartr::getMarts()
dataset = "hsapiens_gene_ensembl", # datasets were selected with biomartr::getDatasets()
attributes = c("ensembl_gene_id","ensembl_peptide_id"), # attributes were selected with biomartr::getAttributes()
filters = "refseq_peptide") # specify what ID type was stored in the fasta file retrieved with biomartr::getGenome()
result_BM refseq_peptide ensembl_gene_id ensembl_peptide_id
1 NP_000005 ENSG00000175899 ENSP00000323929
2 NP_000006 ENSG00000156006 ENSP00000286479
3 NP_000007 ENSG00000117054 ENSP00000359878
4 NP_000008 ENSG00000122971 ENSP00000242592
5 NP_000009 ENSG00000072778 ENSP00000349297The biomart() function takes as arguments a set of genes
(gene ids specified in the filter argument), the
corresponding mart and dataset, as well as the
attributes which shall be returned.
The biomartr package also enables a fast and intuitive
retrieval of GO terms and additional information via the
getGO() function. Several databases can be selected to
retrieve GO annotation information for a set of query genes. So far, the
getGO() function allows GO information retrieval from the
Ensembl Biomart database.
In this example we will retrieve GO information for a set of Homo
sapiens genes stored as hgnc_symbol.
The getGO() function takes several arguments as input to
retrieve GO information from BioMart. First, the scientific name of the
organism of interest needs to be specified. Furthermore, a
set of gene ids as well as their corresponding
filter notation (GUCA2A gene ids have
filter notation hgnc_symbol; see
organismFilters() for details) need to be specified. The
database argument then defines the database from which GO
information shall be retrieved.
# show all elements of the data.frame
options(tibble.print_max = Inf)
# search for GO terms of an example Homo sapiens gene
GO_tbl <- biomartr::getGO(organism = "Homo sapiens",
genes = "GUCA2A",
filters = "hgnc_symbol")
GO_tblHence, for each gene id the resulting table stores all
annotated GO terms found in Ensembl Biomart.