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:
options(timeout = 30000)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.
# load the biomartr package
library(biomartr)
# list all available databases
biomartr::getMarts() 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.
head(biomartr::getDatasets(mart = "ENSEMBL_MART_ENSEMBL") , 5) 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.
tail(biomartr::getDatasets(mart = "ENSEMBL_MART_ENSEMBL") , 38)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 × 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… GRCh38…The 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_M…Users 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_M…Now, 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… ENSEM…Or 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_MAR…Analogous 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_… ENSEMBL…The 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… ENSEMBL…biomartrThe 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.