biomaRt 2.36.0
select()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 92
## 2 ENSEMBL_MART_MOUSE Mouse strains 92
## 3 ENSEMBL_MART_SNP Ensembl Variation 92
## 4 ENSEMBL_MART_FUNCGEN Ensembl Regulation 92Note: 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:
options(RCurlOptions = list(proxy="uscache.kcc.com:80",proxyuserpwd="------:-------"))The 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().
listDatasets(ensembl)## dataset description version
## 1 acarolinensis_gene_ensembl Anole lizard genes (AnoCar2.0) AnoCar2.0
## 2 amelanoleuca_gene_ensembl Panda genes (ailMel1) ailMel1
## 3 amexicanus_gene_ensembl Cave fish genes (AstMex102) AstMex102
## 4 anancymaae_gene_ensembl Ma's night monkey genes (Anan_2.0) Anan_2.0
## 5 aplatyrhynchos_gene_ensembl Duck genes (BGI_duck_1.0) BGI_duck_1.0
## 6 btaurus_gene_ensembl Cow genes (UMD3.1) UMD3.1
## 7 caperea_gene_ensembl Brazilian guinea pig genes (CavAp1.0) CavAp1.0
## 8 catys_gene_ensembl Sooty mangabey genes (Caty_1.0) Caty_1.0
## 9 ccapucinus_gene_ensembl Capuchin genes (Cebus_imitator-1.0) Cebus_imitator-1.0
## 10 cchok1gshd_gene_ensembl Chinese hamster CHOK1GS genes (CHOK1GS_HDv1) CHOK1GS_HDv1
## 11 ccrigri_gene_ensembl Chinese hamster CriGri genes (CriGri_1.0) CriGri_1.0
## 12 celegans_gene_ensembl Caenorhabditis elegans genes (WBcel235) WBcel235
## 13 cfamiliaris_gene_ensembl Dog genes (CanFam3.1) CanFam3.1
## 14 chircus_gene_ensembl Goat genes (ARS1) ARS1
## 15 choffmanni_gene_ensembl Sloth genes (choHof1) choHof1
## 16 cintestinalis_gene_ensembl C.intestinalis genes (KH) KH
## 17 cjacchus_gene_ensembl Marmoset genes (ASM275486v1) ASM275486v1
## 18 clanigera_gene_ensembl Long-tailed chinchilla genes (ChiLan1.0) ChiLan1.0
## 19 cpalliatus_gene_ensembl Angola colobus genes (Cang.pa_1.0) Cang.pa_1.0
## 20 cporcellus_gene_ensembl Guinea Pig genes (Cavpor3.0) Cavpor3.0
## 21 csabaeus_gene_ensembl Vervet-AGM genes (ChlSab1.1) ChlSab1.1
## 22 csavignyi_gene_ensembl C.savignyi genes (CSAV 2.0) CSAV 2.0
## 23 csyrichta_gene_ensembl Tarsier genes (Tarsius_syrichta-2.0.1) Tarsius_syrichta-2.0.1
## 24 dmelanogaster_gene_ensembl Fruitfly genes (BDGP6) BDGP6
## 25 dnovemcinctus_gene_ensembl Armadillo genes (Dasnov3.0) Dasnov3.0
## 26 dordii_gene_ensembl Kangaroo rat genes (Dord_2.0) Dord_2.0
## 27 drerio_gene_ensembl Zebrafish genes (GRCz11) GRCz11
## 28 ecaballus_gene_ensembl Horse genes (Equ Cab 2) Equ Cab 2
## 29 eeuropaeus_gene_ensembl Hedgehog genes (eriEur1) eriEur1
## 30 etelfairi_gene_ensembl Lesser hedgehog tenrec genes (TENREC) TENREC
## 31 falbicollis_gene_ensembl Flycatcher genes (FicAlb_1.4) FicAlb_1.4
## 32 fcatus_gene_ensembl Cat genes (Felis_catus_8.0) Felis_catus_8.0
## 33 fdamarensis_gene_ensembl Damara mole rat genes (DMR_v1.0) DMR_v1.0
## 34 gaculeatus_gene_ensembl Stickleback genes (BROAD S1) BROAD S1
## 35 ggallus_gene_ensembl Chicken genes (Gallus_gallus-5.0) Gallus_gallus-5.0
## 36 ggorilla_gene_ensembl Gorilla genes (gorGor4) gorGor4
## 37 gmorhua_gene_ensembl Cod genes (gadMor1) gadMor1
## 38 hfemale_gene_ensembl Naked mole-rat female genes (HetGla_female_1.0) HetGla_female_1.0
## 39 hmale_gene_ensembl Naked mole-rat male genes (HetGla_1.0) HetGla_1.0
## 40 hsapiens_gene_ensembl Human genes (GRCh38.p12) GRCh38.p12
## 41 itridecemlineatus_gene_ensembl Squirrel genes (SpeTri2.0) SpeTri2.0
## 42 jjaculus_gene_ensembl Lesser Egyptian jerboa genes (JacJac1.0) JacJac1.0
## 43 lafricana_gene_ensembl Elephant genes (Loxafr3.0) Loxafr3.0
## 44 lchalumnae_gene_ensembl Coelacanth genes (LatCha1) LatCha1
## 45 loculatus_gene_ensembl Spotted gar genes (LepOcu1) LepOcu1
## 46 mauratus_gene_ensembl Golden Hamster genes (MesAur1.0) MesAur1.0
## 47 mcaroli_gene_ensembl Ryukyu mouse genes (CAROLI_EIJ_v1.1) CAROLI_EIJ_v1.1
## 48 mdomestica_gene_ensembl Opossum genes (monDom5) monDom5
## 49 mfascicularis_gene_ensembl Crab-eating macaque genes (Macaca_fascicularis_5.0) Macaca_fascicularis_5.0
## 50 mfuro_gene_ensembl Ferret genes (MusPutFur1.0) MusPutFur1.0
## 51 mgallopavo_gene_ensembl Turkey genes (Turkey_2.01) Turkey_2.01
## 52 mleucophaeus_gene_ensembl Drill genes (Mleu.le_1.0) Mleu.le_1.0
## 53 mlucifugus_gene_ensembl Microbat genes (Myoluc2.0) Myoluc2.0
## 54 mmulatta_gene_ensembl Macaque genes (Mmul_8.0.1) Mmul_8.0.1
## 55 mmurinus_gene_ensembl Mouse Lemur genes (Mmur_3.0) Mmur_3.0
## 56 mmusculus_gene_ensembl Mouse genes (GRCm38.p6) GRCm38.p6
## 57 mnemestrina_gene_ensembl Pig-tailed macaque genes (Mnem_1.0) Mnem_1.0
## 58 mochrogaster_gene_ensembl Prairie vole genes (MicOch1.0) MicOch1.0
## 59 mpahari_gene_ensembl Shrew mouse genes (PAHARI_EIJ_v1.1) PAHARI_EIJ_v1.1
## 60 mspretus_gene_ensembl Algerian mouse genes (SPRET_EiJ_v1) SPRET_EiJ_v1
## 61 neugenii_gene_ensembl Wallaby genes (Meug_1.0) Meug_1.0
## 62 ngalili_gene_ensembl Upper Galilee mountains blind mole rat genes (S.galili_v1.0) S.galili_v1.0
## 63 nleucogenys_gene_ensembl Gibbon genes (Nleu_3.0) Nleu_3.0
## 64 oanatinus_gene_ensembl Platypus genes (OANA5) OANA5
## 65 oaries_gene_ensembl Sheep genes (Oar_v3.1) Oar_v3.1
## 66 ocuniculus_gene_ensembl Rabbit genes (OryCun2.0) OryCun2.0
## 67 odegus_gene_ensembl Degu genes (OctDeg1.0) OctDeg1.0
## 68 ogarnettii_gene_ensembl Bushbaby genes (OtoGar3) OtoGar3
## 69 olatipes_gene_ensembl Medaka genes (HdrR) HdrR
## 70 oniloticus_gene_ensembl Tilapia genes (Orenil1.0) Orenil1.0
## 71 oprinceps_gene_ensembl Pika genes (OchPri2.0-Ens) OchPri2.0-Ens
## 72 pabelii_gene_ensembl Orangutan genes (PPYG2) PPYG2
## 73 panubis_gene_ensembl Olive baboon genes (Panu_3.0) Panu_3.0
## 74 pbairdii_gene_ensembl Northern American deer mouse genes (Pman_1.0) Pman_1.0
## 75 pcapensis_gene_ensembl Hyrax genes (proCap1) proCap1
## 76 pcoquereli_gene_ensembl Coquerel's sifaka genes (Pcoq_1.0) Pcoq_1.0
## 77 pformosa_gene_ensembl Amazon molly genes (Poecilia_formosa-5.1.2) Poecilia_formosa-5.1.2
## 78 pmarinus_gene_ensembl Lamprey genes (Pmarinus_7.0) Pmarinus_7.0
## 79 ppaniscus_gene_ensembl Bonobo genes (panpan1.1) panpan1.1
## 80 psinensis_gene_ensembl Chinese softshell turtle genes (PelSin_1.0) PelSin_1.0
## 81 ptroglodytes_gene_ensembl Chimpanzee genes (Pan_tro_3.0) Pan_tro_3.0
## 82 pvampyrus_gene_ensembl Megabat genes (pteVam1) pteVam1
## 83 rbieti_gene_ensembl Black snub-nosed monkey genes (ASM169854v1) ASM169854v1
## 84 rnorvegicus_gene_ensembl Rat genes (Rnor_6.0) Rnor_6.0
## 85 rroxellana_gene_ensembl Golden snub-nosed monkey genes (Rrox_v1) Rrox_v1
## 86 saraneus_gene_ensembl Shrew genes (sorAra1) sorAra1
## 87 sboliviensis_gene_ensembl Bolivian squirrel monkey genes (SaiBol1.0) SaiBol1.0
## 88 scerevisiae_gene_ensembl Saccharomyces cerevisiae genes (R64-1-1) R64-1-1
## 89 sharrisii_gene_ensembl Tasmanian devil genes (Devil_ref v7.0) Devil_ref v7.0
## 90 sscrofa_gene_ensembl Pig genes (Sscrofa11.1) Sscrofa11.1
## 91 tbelangeri_gene_ensembl Tree Shrew genes (tupBel1) tupBel1
## 92 tguttata_gene_ensembl Zebra Finch genes (taeGut3.2.4) taeGut3.2.4
## 93 tnigroviridis_gene_ensembl Tetraodon genes (TETRAODON 8.0) TETRAODON 8.0
## 94 trubripes_gene_ensembl Fugu genes (FUGU 4.0) FUGU 4.0
## 95 ttruncatus_gene_ensembl Dolphin genes (turTru1) turTru1
## 96 vpacos_gene_ensembl Alpaca genes (vicPac1) vicPac1
## 97 xmaculatus_gene_ensembl Platyfish genes (Xipmac4.4.2) Xipmac4.4.2
## 98 xtropicalis_gene_ensembl Xenopus genes (JGI 4.2) JGI 4.2To 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.Note: for some frequently used queries to Ensembl, wrapper functions are available: getGene() and getSequence(). These functions call the getBM() function with hard coded filter and attribute names.
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'),
filters = 'affy_hg_u133_plus_2',
values = affyids,
mart = ensembl)## affy_hg_u133_plus_2 entrezgene
## 1 209310_s_at 837
## 2 207500_at 838
## 3 202763_at 836In the sections below a variety of example queries are described. Every example is written as a task, and we have to come up with a biomaRt solution to the problem.
We have a list of Affymetrix hgu133plus2 identifiers and we would like to retrieve the HUGO gene symbols, chromosome names, start and end positions and the bands of the corresponding genes. The listAttributes() and the listFilters() functions give us an overview of the available attributes and filters and we look in those lists to find the corresponding attribute and filter names we need. For this query we’ll need the following attributes: hgnc_symbol, chromsome_name, start_position, end_position, band and affy_hg_u133_plus_2 (as we want these in the output to provide a mapping with our original Affymetrix input identifiers. There is one filter in this query which is the affy_hg_u133_plus_2 filter as we use a list of Affymetrix identifiers as input. Putting this all together in the getBM() and performing the query gives:
affyids=c("202763_at","209310_s_at","207500_at")
getBM(attributes = c('affy_hg_u133_plus_2', 'hgnc_symbol', 'chromosome_name',
'start_position', 'end_position', 'band'),
filters = 'affy_hg_u133_plus_2',
values = affyids,
mart = ensembl)## affy_hg_u133_plus_2 hgnc_symbol chromosome_name start_position end_position band
## 1 209310_s_at CASP4 11 104942866 104969436 q22.3
## 2 207500_at CASP5 11 104994235 105023168 q22.3
## 3 202763_at CASP3 4 184627696 184649509 q35.1In this task we start out with a list of EntrezGene identiers and we want to retrieve GO identifiers related to biological processes that are associated with these entrezgene identifiers. Again we look at the output of listAttributes() and listFilters() to find the filter and attributes we need. Then we construct the following query:
entrez=c("673","837")
goids = getBM(attributes = c('entrezgene', 'go_id'),
filters = 'entrezgene',
values = entrez,
mart = ensembl)
head(goids)## entrezgene go_id
## 1 673 GO:0005524
## 2 673 GO:0007165
## 3 673 GO:0006468
## 4 673 GO:0035556
## 5 673 GO:0004672
## 6 673 GO:0005057The GO terms we are interested in are: GO:0051330, GO:0000080, GO:0000114, GO:0000082. The key to performing this query is to understand that the getBM() function enables you to use more than one filter at the same time. In order to do this, the filter argument should be a vector with the filter names. The values should be a list, where the first element of the list corresponds to the first filter and the second list element to the second filter and so on. The elements of this list are vectors containing the possible values for the corresponding filters.
go=c("GO:0051330","GO:0000080","GO:0000114","GO:0000082")
chrom=c(17,20,"Y")
getBM(attributes= "hgnc_symbol",
filters=c("go_id","chromosome_name"),
values=list(go, chrom), mart=ensembl)## Error in getBM(attributes = "hgnc_symbol", filters = c("go_id", "chromosome_name"), : Invalid filters(s): go_id
## Please use the function 'listFilters' to get valid filter namesIn this example we want to annotate the following two RefSeq identifiers: NM_005359 and NM_000546 with INTERPRO protein domain identifiers and a description of the protein domains.
refseqids = c("NM_005359","NM_000546")
ipro = getBM(attributes=c("refseq_mrna","interpro","interpro_description"),
filters="refseq_mrna",
values=refseqids,
mart=ensembl)
ipro## refseq_mrna interpro interpro_description
## 1 NM_000546 IPR002117 p53 tumour suppressor family
## 2 NM_000546 IPR008967 p53-like transcription factor, DNA-binding
## 3 NM_000546 IPR010991 p53, tetramerisation domain
## 4 NM_000546 IPR011615 p53, DNA-binding domain
## 5 NM_000546 IPR012346 p53/RUNT-type transcription factor, DNA-binding domain superfamily
## 6 NM_000546 IPR013872 p53 transactivation domain
## 7 NM_000546 IPR036674 p53-like tetramerisation domain superfamily
## 8 NM_005359 IPR001132 SMAD domain, Dwarfin-type
## 9 NM_005359 IPR003619 MAD homology 1, Dwarfin-type
## 10 NM_005359 IPR008984 SMAD/FHA domain superfamily
## 11 NM_005359 IPR013019 MAD homology, MH1
## 12 NM_005359 IPR013790 Dwarfin
## 13 NM_005359 IPR017855 SMAD-like domain superfamily
## 14 NM_005359 IPR036578 SMAD MH1 domain superfamilyIn this example we will again use multiple filters: chromosome_name, start, and end as we filter on these three conditions. Note that when a chromosome name, a start position and an end position are jointly used as filters, the BioMart webservice interprets this as return everything from the given chromosome between the given start and end positions.
getBM(attributes = c('affy_hg_u133_plus_2','ensembl_gene_id'),
filters = c('chromosome_name','start','end'),
values = list(16,1100000,1250000),
mart = ensembl)## affy_hg_u133_plus_2 ensembl_gene_id
## 1 ENSG00000260702
## 2 215502_at ENSG00000260532
## 3 ENSG00000273551
## 4 205845_at ENSG00000196557
## 5 ENSG00000196557
## 6 ENSG00000260403
## 7 ENSG00000259910
## 8 ENSG00000261294
## 9 220339_s_at ENSG00000116176
## 10 ENSG00000277010
## 11 205683_x_at ENSG00000197253
## 12 207134_x_at ENSG00000197253
## 13 217023_x_at ENSG00000197253
## 14 210084_x_at ENSG00000197253
## 15 215382_x_at ENSG00000197253
## 16 216474_x_at ENSG00000197253
## 17 205683_x_at ENSG00000172236
## 18 207134_x_at ENSG00000172236
## 19 217023_x_at ENSG00000172236
## 20 210084_x_at ENSG00000172236
## 21 215382_x_at ENSG00000172236
## 22 216474_x_at ENSG00000172236The GO identifier for MAP kinase activity is GO:0004707. In our query we will use go_id as our filter, and entrezgene and hgnc_symbol as attributes. Here’s the query:
getBM(attributes = c('entrezgene','hgnc_symbol'),
filters = 'go',
values = 'GO:0004707',
mart = ensembl)## entrezgene hgnc_symbol
## 1 5594 MAPK1
## 2 51701 NLK
## 3 5595 MAPK3
## 4 225689 MAPK15
## 5 5601 MAPK9
## 6 5603 MAPK13
## 7 5597 MAPK6
## 8 5596 MAPK4
## 9 1432 MAPK14
## 10 5599 MAPK8
## 11 5598 MAPK7
## 12 6300 MAPK12
## 13 5600 MAPK11
## 14 5602 MAPK10All sequence related queries to Ensembl are available through the getSequence() wrapper function. getBM() can also be used directly to retrieve sequences but this can get complicated so using getSequence is recommended.
Sequences can be retrieved using the getSequence() function either starting from chromosomal coordinates or identifiers.
The chromosome name can be specified using the chromosome argument. The start and end arguments are used to specify start and end positions on the chromosome.
The type of sequence returned can be specified by the seqType argument which takes the following values:
In MySQL mode the getSequence() function is more limited and the sequence that is returned is the 5’ to 3’+ strand of the genomic sequence, given a chromosome, as start and an end position.
This task requires us to retrieve 100bp upstream promoter sequences from a set of EntrzGene identifiers. The type argument in getSequence() can be thought of as the filter in this query and uses the same input names given by listFilters(). In our query we use entrezgene for the type argument. Next we have to specify which type of sequences we want to retrieve, here we are interested in the sequences of the promoter region, starting right next to the coding start of the gene. Setting the seqType to coding_gene_flank will give us what we need. The upstream argument is used to specify how many bp of upstream sequence we want to retrieve, here we’ll retrieve a rather short sequence of 100bp. Putting this all together in getSequence() gives:
entrez=c("673","7157","837")
getSequence(id = entrez,
type="entrezgene",
seqType="coding_gene_flank",
upstream=100,
mart=ensembl) ## coding_gene_flank entrezgene
## 1 TCCTTCTCTGCAGGCCCAGGTGACCCAGGGTTGGAAGTGTCTCATGCTGGATCCCCACTTTTCCTCTTGCAGCAGCCAGACTGCCTTCCGGGTCACTGCC 7157
## 2 CACGTTTCCGCCCTTTGCAATAAGGAAATACATAGTTTACTTTCATTTTTGACTCTGAGGCTCTTTCCAACGCTGTAAAAAAGGACAGAGGCTGTTCCCT 837
## 3 CCTCCGCCTCCGCCTCCGCCTCCGCCTCCCCCAGCTCTCCGCCTCCCTTCCCCCTCCCCGCCCGACAGCGGCCGCTCGGGCCCCGGCTCTCGGTTATAAG 673As described in the provious task getSequence can also use chromosomal coordinates to retrieve sequences of all genes that lie in the given region. We also have to specify which type of identifier we want to retrieve together with the sequences, here we choose for entrezgene identifiers.
utr5 = getSequence(chromosome=3, start=185514033, end=185535839,
type="entrezgene",
seqType="5utr",
mart=ensembl)
utr5## 5utr
## 1 AGTCCCTAGGGAACTTCCTGTTGTCACCACACCTCTGAGTCGTCTGAGCTCACTGTGAGCAAAATCCCACAGTGGAAACTCTTAAGCCTCTGCGAAGTAAATCATTCTTGTGAATGTGACACACGATCTCTCCAGTTTCCAT
## 2 ATTCTTGTGAATGTGACACACGATCTCTCCAGTTTCCAT
## 3 Sequence unavailable
## 4 TGAGCAAAATCCCACAGTGGAAACTCTTAAGCCTCTGCGAAGTAAATCATTCTTGTGAATGTGACACACGATCTCTCCAGTTTCCAT
## entrezgene
## 1 200879
## 2 200879
## 3 200879
## 4 200879In this task the type argument specifies which type of identifiers we are using.
To get an overview of other valid identifier types we refer to the listFilters() function.
protein = getSequence(id=c(100, 5728),
type="entrezgene",
seqType="peptide",
mart=ensembl)
protein## peptide
## 1 MAQTPAFDKPKVELHVHLDGSIKPETILYYGRRRGIALPANTAEGLLNVIGMDKPLTLPDFLAKFDYYMPAIAGCREAIKRIAYEFVEMKAKEGVVYVEVRYSPHLLANSKVEPIPWNQAEGDLTPDEVVALVGQGLQEGERDFGVKARSILCCMRHQPNWSPKVVELCKKYQQQTVVAIDLAGDETIPGSSLLPGHVQAYQEAVKSGIHRTVHAGEVGSAEVVKEAVDILKTERLGHGYHTLEDQALYNRLRQENMHFEAQK*
## 2 MAQTPAFDKPKVELHVHLDGSIKPETILYYGRRRGIALPANTAEGLLNVIGMDKPLTLPDFLAKFDYYMPAIAGCREAIKRIAYEFVEMKAKEGVVYVEVRYSPHLLANSKVEPIPWNQAEGDLTPDEVVALVGQGLQEGERDFGVKARSILCCMRHQPNWSPKVVELCKKYQQQTVVAIDLAGDETIPGSSLLPGHVQAYQEAVKSGIHRTVHAGEVGSAEVVKEAVDILKTERLGHGYHTLEDQALYNRLRQENMHFEICPWSSYLTGAWKPDTEHAVIRLKNDQANYSLNTDDPLIFKSTLDTDYQMTKRDMGFTEEEFKRLNINAAKSSFLPEDEKRELLDLLYKAYGMPPSASAGQNL*
## 3 MTAIIKEIVSRNKRRYQEDGFDLDLTYIYPNIIAMGFPAERLEGVYRNNIDDVVRFLDSKHKNHYKIYNLCAERHYDTAKFNCRVAQYPFEDHNPPQLELIKPFCEDLDQWLSEDDNHVAAIHCKAGKGRTGVMICAYLLHRGKFLKAQEALDFYGEVRTRDKKGVTIPSQRRYVYYYSYLLKNHLDYRPVALLFHKMMFETIPMFSGGTCNPQFVVCQLKVKIYSSNSGPTRREDKFMYFEFPQPLPVCGDIKVEFFHKQNKMLKKDKMFHFWVNTFFIPGPEETSEKVENGSLCDQEIDSICSIERADNDKEYLVLTLTKNDLDKANKDKANRYFSPNFKVKLYFTKTVEEPSNPEASSSTSVTPDVSDNEPDHYRYSDTTDSDPENEPFDEDQHTQITKV*
## 4 Sequence unavailable
## 5 MAQTPAFDKPKVELHVHLDGSIKPETILYYGRRRGIALPANTAEGLLNVIGMDKPLTLPDFLAKFDYYMPAIARL*
## 6 Sequence unavailable
## 7 ALLFHKMMFETIPMFSGGTCNPQFVVCQLKVKIYSSNSGPTRREDKFMYFEFPQPLPVCGDIKVEFFHKQNKMLKKDKMFHFWVNTFFIPGPEETSEKVENGSLCDQEIDSICSIERADNDKEYLVLTLTKNDLDKANKDKANRYFSPNFKVS*
## 8 MAQTPAFDKPKVELHVHLDGSIKPETILYYGRRRGIALPANTAEGLLNVIGMDKPLTLPDFLAKFDYYMPAIAGCREAIKRIAYEFVEMKAKEGVVYVEVRYSPHLLANSKVEPIPWNQAEGDLTPDEVVALVGQGLQEGERDFGVKARSILCCMRHQPNWSPKVVELCKKYQQQTVVAIDLAGDETIPGSSLLPGHVQAYQAVDILKTERLGHGYHTLEDQALYNRLRQENMHFEICPWSSYLTGAWKPDTEHAVIRLKNDQANYSLNTDDPLIFKSTLDTDYQMTKRDMGFTEEEFKRLNINAAKSSFLPEDEKRELLDLLYKAYGMPPSASAGQNL*
## 9 LERGGEAAAAAAAAAAAPGRGSESPVTISRAGNAGELVSPLLLPPTRRRRRRHIQGPGPVLNLPSAAAAPPVARAPEAAGGGSRSEDYSSSPHSAAAAARPLAAEEKQAQSLQPSSSRRSSHYPAAVQSQAAAERGASATAKSRAISILQKKPRHQQLLPSLSSFFFSHRLPDMTAIIKEIVSRNKRRYQEDGFDLDLTYIYPNIIAMGFPAERLEGVYRNNIDDVVRFLDSKHKNHYKIYNLCAERHYDTAKFNCRVAQYPFEDHNPPQLELIKPFCEDLDQWLSEDDNHVAAIHCKAGKGRTGVMICAYLLHRGKFLKAQEALDFYGEVRTRDKKGVTIPSQRRYVYYYSYLLKNHLDYRPVALLFHKMMFETIPMFSGGTCNPQFVVCQLKVKIYSSNSGPTRREDKFMYFEFPQPLPVCGDIKVEFFHKQNKMLKKDKMFHFWVNTFFIPGPEETSEKVENGSLCDQEIDSICSIERADNDKEYLVLTLTKNDLDKANKDKANRYFSPNFKVKLYFTKTVEEPSNPEASSSTSVTPDVSDNEPDHYRYSDTTDSDPENEPFDEDQHTQITKV*
## entrezgene
## 1 100
## 2 100
## 3 5728
## 4 5728
## 5 100
## 6 100
## 7 5728
## 8 100
## 9 5728For this example we’ll first have to connect to a different BioMart database, namely snp.
snpmart = useMart(biomart = "ENSEMBL_MART_SNP", dataset="hsapiens_snp")The listAttributes() and listFilters() functions give us an overview of the available attributes and filters.
From these we need: refsnp_id, allele, chrom_start and chrom_strand as attributes; and as filters we’ll use: chrom_start, chrom_end and chr_name.
Note that when a chromosome name, a start position and an end position are jointly used as filters,
the BioMart webservice interprets this as return everything from the given chromosome between the given start and end positions.
Putting our selected attributes and filters into getBM gives:
getBM(attributes = c('refsnp_id','allele','chrom_start','chrom_strand'),
filters = c('chr_name','start','end'),
values = list(8,148350,148612),
mart = snpmart)## refsnp_id allele chrom_start chrom_strand
## 1 rs868546642 A/G 148372 1
## 2 rs547420070 A/C 148373 1
## 3 rs77274555 G/A 148391 1
## 4 rs567299969 T/A 148394 1
## 5 rs368076569 G/A 148407 1
## 6 rs745318437 C/G 148497 1
## 7 rs190721891 C/G 148576 1The getLDS() (Get Linked Dataset) function provides functionality to link 2 BioMart datasets which each other and construct a query over the two datasets. In Ensembl, linking two datasets translates to retrieving homology data across species.
The usage of getLDS is very similar to getBM(). The linked dataset is provided by a separate Mart object and one has to specify filters and attributes for the linked dataset. Filters can either be applied to both datasets or to one of the datasets. Use the listFilters and listAttributes functions on both Mart objects to find the filters and attributes for each dataset (species in Ensembl). The attributes and filters of the linked dataset can be specified with the attributesL and filtersL arguments. Entering all this information into getLDS() gives:
human = useMart("ensembl", dataset = "hsapiens_gene_ensembl")
mouse = useMart("ensembl", dataset = "mmusculus_gene_ensembl")
getLDS(attributes = c("hgnc_symbol","chromosome_name", "start_position"),
filters = "hgnc_symbol", values = "TP53",mart = human,
attributesL = c("refseq_mrna","chromosome_name","start_position"), martL = mouse)## HGNC.symbol Chromosome.scaffold.name Gene.start..bp. RefSeq.mRNA.ID Chromosome.scaffold.name.1 Gene.start..bp..1
## 1 TP53 17 7661779 11 69580359
## 2 TP53 17 7661779 NM_001127233 11 69580359
## 3 TP53 17 7661779 NM_011640 11 69580359It is possible to query archived versions of Ensembl through biomaRt.
biomaRt provides the function listEnsemblArchives() to view the available archives. This function takes no arguments, and produces a table containing the names of the available archived versions, the date they were first available, and the URL where they can be accessed.
listEnsemblArchives()## name date url version current_release
## 1 Ensembl GRCh37 Feb 2014 http://grch37.ensembl.org GRCh37
## 2 Ensembl 92 Apr 2018 http://apr2018.archive.ensembl.org 92 *
## 3 Ensembl 91 Dec 2017 http://dec2017.archive.ensembl.org 91
## 4 Ensembl 90 Aug 2017 http://aug2017.archive.ensembl.org 90
## 5 Ensembl 89 May 2017 http://may2017.archive.ensembl.org 89
## 6 Ensembl 88 Mar 2017 http://mar2017.archive.ensembl.org 88
## 7 Ensembl 87 Dec 2016 http://dec2016.archive.ensembl.org 87
## 8 Ensembl 86 Oct 2016 http://oct2016.archive.ensembl.org 86
## 9 Ensembl 85 Jul 2016 http://jul2016.archive.ensembl.org 85
## 10 Ensembl 84 Mar 2016 http://mar2016.archive.ensembl.org 84
## 11 Ensembl 83 Dec 2015 http://dec2015.archive.ensembl.org 83
## 12 Ensembl 82 Sep 2015 http://sep2015.archive.ensembl.org 82
## 13 Ensembl 81 Jul 2015 http://jul2015.archive.ensembl.org 81
## 14 Ensembl 80 May 2015 http://may2015.archive.ensembl.org 80
## 15 Ensembl 79 Mar 2015 http://mar2015.archive.ensembl.org 79
## 16 Ensembl 78 Dec 2014 http://dec2014.archive.ensembl.org 78
## 17 Ensembl 77 Oct 2014 http://oct2014.archive.ensembl.org 77
## 18 Ensembl 76 Aug 2014 http://aug2014.archive.ensembl.org 76
## 19 Ensembl 75 Feb 2014 http://feb2014.archive.ensembl.org 75
## 20 Ensembl 74 Dec 2013 http://dec2013.archive.ensembl.org 74
## 21 Ensembl 67 May 2012 http://may2012.archive.ensembl.org 67
## 22 Ensembl 54 May 2009 http://may2009.archive.ensembl.org 54Alternatively, one can use the http://www.ensembl.org website to find archived version. From the main page scroll down the bottom of the page, click on ‘view in Archive’ and select the archive you need.
You will notice that there is an archive URL even for the current release of Ensembl. It can be useful to use this if you wish to ensure that script you write now will return exactly the same results in the future. Using www.ensembl.org will always access the current release, and so the data retrieved may change over time as new releases come out.
Whichever method you use to find the URL of the archive you wish to query, copy the url and use that in the host argument as shown below to connect to the specified BioMart database. The example below shows how to query Ensembl 54.
listMarts(host = 'may2009.archive.ensembl.org')## biomart version
## 1 ENSEMBL_MART_ENSEMBL Ensembl 54
## 2 ENSEMBL_MART_SNP Ensembl Variation 54
## 3 ENSEMBL_MART_VEGA Vega 35
## 4 REACTOME Reactome(CSHL US)
## 5 wormbase_current WormBase (CSHL US)
## 6 pride PRIDE (EBI UK)ensembl54 <- useMart(host='may2009.archive.ensembl.org',
biomart='ENSEMBL_MART_ENSEMBL',
dataset='hsapiens_gene_ensembl')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, 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.
listMarts(host = "parasite.wormbase.org")## biomart version
## 1 parasite_mart_10_merged WBPS 10 Martwormbase = useMart(biomart = "parasite_mart", host = "parasite.wormbase.org")## Error in useMart(biomart = "parasite_mart", host = "parasite.wormbase.org"): Incorrect BioMart name, use the listMarts function to see which BioMart databases are availablelistDatasets(wormbase)## Error in is(mart, "Mart"): object 'wormbase' not foundwormbase <- useDataset(mart = wormbase, dataset = "wbps_gene")## Error in useDataset(mart = wormbase, dataset = "wbps_gene"): object 'wormbase' not foundhead(listFilters(wormbase))## Error in martCheck(mart): object 'wormbase' not foundhead(listAttributes(wormbase))## Error in martCheck(mart): object 'wormbase' not foundgetBM(attributes = c("external_gene_id", "wbps_transcript_id", "transcript_biotype"),
filters="gene_name",
values=c("unc-26","his-33"),
mart=wormbase)## Error in martCheck(mart): object 'wormbase' not foundThis section describes a set of biomaRt helper functions that can be used to export FASTA format sequences, retrieve values for certain filters and exploring the available filters and attributes in a more systematic manner.
The data.frames obtained by the getSequence function can be exported to FASTA files using the exportFASTA() function. One has to specify the data.frame to export and the filename using the file argument.
Boolean filters need a value TRUE or FALSE in biomaRt. Setting the value TRUE will include all information that fulfill the filter requirement. Setting FALSE will exclude the information that fulfills the filter requirement and will return all values that don’t fulfill the filter.
For most of the filters, their name indicates if the type is a boolean or not and they will usually start with “with”. However this is not a rule and to make sure you got the type right you can use the function filterType() to investigate the type of the filter you want to use.
filterType("with_affy_hg_u133_plus_2",ensembl)## [1] "boolean_list"Some filters have a limited set of values that can be given to them. To know which values these are one can use the filterOptions() function to retrieve the predetermed values of the respective filter.
filterOptions("biotype",ensembl)## [1] "[3prime_overlapping_ncRNA,antisense,bidirectional_promoter_lncRNA,IG_C_gene,IG_C_pseudogene,IG_D_gene,IG_J_gene,IG_J_pseudogene,IG_pseudogene,IG_V_gene,IG_V_pseudogene,lincRNA,macro_lncRNA,miRNA,misc_RNA,Mt_rRNA,Mt_tRNA,non_coding,polymorphic_pseudogene,processed_pseudogene,processed_transcript,protein_coding,pseudogene,ribozyme,rRNA,scaRNA,scRNA,sense_intronic,sense_overlapping,snoRNA,snRNA,sRNA,TEC,transcribed_processed_pseudogene,transcribed_unitary_pseudogene,transcribed_unprocessed_pseudogene,translated_processed_pseudogene,TR_C_gene,TR_D_gene,TR_J_gene,TR_J_pseudogene,TR_V_gene,TR_V_pseudogene,unitary_pseudogene,unprocessed_pseudogene,vaultRNA]"If there are no predetermed values e.g. for the entrezgene filter, then filterOptions() will return the type of filter it is. And most of the times the filter name or it’s description will suggest what values one case use for the respective filter (e.g. entrezgene filter will work with enterzgene identifiers as values)
For large BioMart databases such as Ensembl, the number of attributes displayed by the listAttributes() function can be very large.
In BioMart databases, attributes are put together in pages, such as sequences, features, homologs for Ensembl.
An overview of the attributes pages present in the respective BioMart dataset can be obtained with the attributePages() function.
pages = attributePages(ensembl)
pages## [1] "feature_page" "structure" "homologs" "snp" "snp_somatic" "sequences"To show us a smaller list of attributes which belong to a specific page, we can now specify this in the listAttributes() function. The set of attributes is still quite long, so we use head() to show only the first few items here.
head(listAttributes(ensembl, page="feature_page"))## 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_page
## 6 ensembl_peptide_id_version Protein stable ID version feature_pageWe now get a short list of attributes related to the region where the genes are located.
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')
head(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'),
keytype='affy_hg_u133_plus_2')## affy_hg_u133_plus_2 entrezgene
## 1 209310_s_at 837
## 2 207500_at 838
## 3 202763_at 836So 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.
sessionInfo()## R version 3.5.0 (2018-04-23)
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