The biomaRt user's guide
Contents
1. wormbase useMart WS220 dataset wormbase_gene listFilters wormbase listAttributes wormbase getBM attributes c public_name rnai rnai_phenotype_phenotype_label filters gene_name values c unc 26 his 33 mart wormbase Vttvvvy 16 public_name rnai rnai_phenotype_phenotype_label 1 his 33 WBRNAi00082060 GRO slow growth 2 his 33 WBRNAi00082060 postembryonic development variant 3 his 33 WBRNAi00082060 EMB embryonic lethal 4 his 33 WBRNAi00082060 LVL larval lethal 5 his 33 WBRNAi00082060 LVA larval arrest 6 his 33 WBRNAi00082060 accumulated cell corpses 7 biomaRt helper functions This 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 7 1 exportFASTA 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 7 2 Finding out more information on filters 7 2 1 filterType 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 th2. 10 Session Info gt sessionInfo R version 3 2 2 2015 08 14 Platform x86_64 pc linux gnu 64 bit Running under Ubuntu 14 04 3 LTS locale 1 LC_CTYPE en_US UTF 8 LC_NUMERIC C LC_TIME en_US UTF 8 5 LC_MONETARY en_US UTF 8 LC_MESSAGES en_US UTF 8 LC_PAPER en_US UTF 3 9 LC_ADDRESS C LC_TELEPHONE C LC_MEASUREMENT en_US UTF 3 attached base packages 1 stats graphics grDevices utils datasets methods base other attached packages 1 biomaRt_2 24 1 loaded via a namespace and not attached 1 IRanges_2 2 7 DBI_O 3 1 parallel_3 2 2 tools_3 2 2 6 Biobase_2 28 0 AnnotationDbi_1 30 1 RSQLite_1 0 0 S4Vectors_0 6 5 11 GenomeInfoDb_1 4 2 stats4_3 2 2 bitops_1 0 6 XML_3 98 1 3 gt warnings NULL 25
3. Affy HG U95AV2 Affy HG U95B Affy HG U95C Affy HG U95D Affy HG U95E Affy HG U95A Affy HuGene FL Affy HTA 2_0 Affy HuEx 1_0 st v2 Affy HuGene 1_0 st vi Affy HuGene 2_0 st vi probeset probeset probeset probeset probeset probeset probeset probeset probeset probeset probeset probeset probeset probeset probeset probeset Affy primeview Affy U133 X3P probeset Agilent CGH 44b probe Codelink probe Illumina HumanWG 6 Illumina HumanWG 6 Illumina HumanWG 6 vi probe v2 probe v3 probe Illumina Human HT 12 V3 probe Illumina Human HT 12 V4 probe Illumina Human Ref 8 V3 probe Phalanx OneArray probe Ensembl Protein Family ID s Ensembl Family Description PIRSF ID PIRSF start PIRSF end SUPERFAMILY ID SUPERFAMILY start SUPERFAMILY end SMART ID SMART start SMART end HAMAP Accession ID HAMAP start HAMAP end Pfscan ID Pfscan start Pfscan end ScanProsite ID 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 prosite_start prosite_end prints prints_start prints_end pfam pfam_start pfam_end tigrfam tigrfam_start tigrfam_end gene3d gene3d_start gene3d_end hmmpanther hmmpanther_start hmmpanther_end interpro interpro_short_description interpro_description interpro_start interpro_end low_complexity low_complexity_start low_complexity_end tra
4. coding_gene_flank gives the flanking region of the gene including the UTRs this must be ac companied with a given value for the upstream or downstream attribute transcript_flank gives the flanking region of the transcript exculding the UTRs this must be accompanied with a given value for the upstream or downstream attribute gene_flank gives the flanking region of the gene ex cluding the UTRs this must be accompanied with a given value for the upstream or downstream attribute 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 chro mosome as start and an end position Task 4 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 12 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 gt entrez c 673 7157 8
5. of the available attributes and filters From these we need refsnp_id al lele 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 chro mosome between the given start and end positions Putting our selected attributes and filters into getBM gives gt getBM c refsnp_id allele chrom_start chrom_strand filters c chr_name chrom_start chrom_end val refsnp_id allele chrom_start chrom_strand 1 rs1134195 G T 148394 d 2 rs4046274 C A 148394 1 3 rs4046275 A G 148411 1 4 rs13291 C T 148462 1 5 rs1134192 G A 148462 6 rs4046276 C T 148462 7 rs12019378 T G 148471 1 8 rsi134191 C T 148499 9 rs40462T7T G A 148499 1 10 rs11136408 G A 148525 1 11 rs1134190 C T 148533 12 rs4046278 G A 148533 13 rs1134189 G A 148535 i 14 rs3965587 C T 148535 1 15 rs1134187 G A 148539 ST 16 rs1134186 T C 148569 1 17 rs4378731 G A 148601 1 4 11 Task 11 Given the human gene TP53 retrieve the hu man chromosomal location of this gene and also retrieve the chromosomal location and RefSeq id of it s homolog in mouse The 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 trans
6. Lepisosteus oculatus genes Lep0cu1 Oryzias latipes genes HdrR Gorilla gorilla genes gorGor3 1 Ochotona princeps genes OchPri2 0 Dipodomys ordii genes dip0rdi Ovis aries genes 0ar_v3 1 Mus musculus genes GRCm38 p4 Meleagris gallopavo genes UMD2 Gadus morhua genes gadMor1 Anas platyrhynchos genes BGI_duck_1 0 Rnor_6 0 PelSin_1 0 C_jacchus3 2 1 turTrui R64 1 1 WBcel235 ChlSabi 1 Orenil1 0 FUGU4 0O AstMex102 Pmarinus_7 0 eriEuri FicAlb_1 4 CHIMP2 1 4 TENREC KH Nleu1 0 Sscrofa10 2 OryCun2 0 Dasnov3 0 proCapi taeGut3 2 4 myoLuc2 GRCh38 p3 PoeFor_5 1 2 MusPutFur1 0 tupBeli Galgal4 JGI4 2 EguCab2 PPYG2 Xipmac4 4 2 GRCz10 LatChai TETRAODON8 0 ailMeli MMUL_1 pteVami PapAnu2 0 monDom5 AnoCar2 0 vicPaci tarSyri OtoGar3 BDGP6 micMuri LepOcul HdrR gorGor3 1 OchPri2 0 dipOrdi Oar_v3 1 GRCm38 p4 UMD2 gadMor1 BGI duck 1 0 65 saraneus_gene_ensembl Sorex araneus genes sorAral sorAral 66 sharrisii_gene_ensembl Sarcophilus harrisii genes DEVIL7 0 DEVIL7 O 67 meugenii_gene_ensembl Macropus eugenii genes Meug_1 0 Meug_1 0 68 btaurus_gene_ensembl Bos taurus genes UMD3 1 UMD3 1 69 cfamiliaris_gene_ensembl Canis familiaris genes CanFam3 1 CanFam3 1 To 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 data
7. below can be tried options RCurl0ptions list proxy uscache kcc com 80 proxyuserpwd Passo 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 ex ample we choose to query the Ensembl BioMart database gt ensembl useMart ensemb1 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 gt listDatasets ensembl dataset description version 1 oanatinus_gene_ensembl Ornithorhynchus anatinus genes OANA5 OANAS 2 cporcellus_gene_ensembl Cavia porcellus genes cavPor3 cavPor3 3 gaculeatus_gene_ensembl Gasterosteus aculeatus genes BROADS1 BROADS1 4 lafricana_gene_ensembl Loxodonta africana genes loxAfr3 loxAfr3 5 itridecemlineatus_gene_ensembl Ictidomys tridecemlineatus genes spetri2 spetri2 6 choffmanni_gene_ensembl Choloepus hoffmanni genes choHof1 choHof1 7 csavignyi_gene_ensembl Ciona savignyi genes CSAV2 0 CSAV2 0 8 fcatus_gene_ensembl Felis catus genes Felis_catus_6 2 Felis_catus_6 2 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 rnorvegicus_gene_ensembl psinensis_gene_ensembl cjacchus_gene_ens
8. e filters is a vector of filters that one wil use as input to the query e 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 e mart is and 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 ul33plus2 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 gt affyids c 202763_at 209310_s_
9. entrezgene hgnc_symbol filters go values G0 0004707 mart ensemb1 entrezgene hgnc_symbol 1 5601 MAPK9 2 225689 MAPK15 3 5599 MAPK8 4 5594 MAPK1 5 6300 MAPK12 4 7 Task 7 Given a set of EntrezGene identifiers retrieve 100bp upstream promoter sequences All 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 Se quences 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 argu ments 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 cdna peptide for protein sequences 3utr for 3 UTR sequences 5utr for 5 UTR sequences gene_exon for exon sequences only transcript_exon for transcript specific exonic sequences only transcript_exon_intron gives the full unspliced transcript that is ex ons introns gene_exon_intron gives the exons introns of a gene coding gives the coding sequence only coding_transcript_flank gives the flanking region of the transcript including the UTRs this must be accompanied with a given value for the upstream or downstream attribute
10. multiple filters chromosome_name start and end as we filter on these three conditions Note that when a chromo some 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 gt getBM 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 ENSGO0000196557 5 ENSGO0000196557 6 ENSG00000260403 7 ENSGO0000259910 8 ENSG00000261294 9 220339_s_at ENSGO0000116176 10 ENSGO0000277010 11 217023_x_at ENSGO0000197253 12 210084_x_at ENSGO0000197253 13 215382_x_at ENSGO0000197253 14 216474_x_at ENSGO0000197253 15 207134_x_at ENSGO0000197253 16 205683_x_at ENSGO0000197253 17 217023_x_at ENSGO0000172236 18 210084_x_at ENSGO0000172236 19 215382_x_at ENSGO0000172236 20 207741_x_at ENSGO0000172236 21 216474_x_at ENSGO0000172236 22 207134_x_at ENSGO0000172236 23 205683_x_at ENSGO0000172236 4 6 Task 6 Retrieve all entrezgene identifiers and HUGO gene symbols of genes which have a MAP kinase activ ity GO term associated with it The GO identifier for MAP kinase activity is GO 0004707 In our guery we will use go as filter and entrezgene and hgnc symbol as attributes Here s the guery 11 gt getBM c
11. version of the archive you need please look at the 2nd way to access archives gt listMarts archive TRUE biomart version 1 ensembl_mart_47 ENSEMBL GENES 47 SANGER 2 genomic_features_mart_47 Genomic Features 3 snp_mart_47 SNP 4 vega_mart_47 Vega 5 compara_mart_homology_47 Compara homology 6 compara_mart_multiple_ga_47 Compara multiple alignments 7 compara_mart_pairwise_ga_47 Compara pairwise alignments 8 ensembl_mart_46 ENSEMBL GENES 46 SANGER 9 genomic_features_mart_46 Genomic Features 10 snp_mart_46 SNP 11 vega_mart_46 Vega 12 compara_mart_homology_46 Compara homology 13 compara_mart_multiple_ga_46 Compara multiple alignments 14 compara_mart_pairwise_ga_46 Compara pairwise alignments 15 ensembl_mart_45 ENSEMBL GENES 45 SANGER 16 snp_mart_45 SNP 17 vega_mart_45 Vega 18 compara_mart_homology_45 Compara homology 19 compara_mart_multiple_ga_45 Compara multiple alignments 20 compara_mart_pairwise_ga_45 Compara pairwise alignments 21 ensembl_mart_44 ENSEMBL GENES 44 SANGER 15 22 snp_mart_44 SNP 23 vega_mart_44 Vega 24 compara_mart_homology_44 Compara homology 25 compara_mart_pairwise_ga_44 Compara pairwise alignments 26 ensembl_mart_43 ENSEMBL GENES 43 SANGER 27 snp_mart_43 SNP 28 vega_mart_43 Vega 29 compara_mart_homology_43 Compara homology 30 compara_mart_pairwise_ga_43 Compara pairwise alignments Next we select the archive we want to use using the useMart function again setting the archive attribute to TRUE an
12. 0 WORMB MGI JACKS FANTOM5 phase1 1 R PARAMECIUM GEN PARAMECIUM BIBLIOGRA EUREXPRESS HA SIGENAE OLIGO ANNOTA SIGENAE OLIGO ANNOTA SIGENAE OLIGO ANNOTA BCCTB Bioinformatics Portal Regulatory Genomics Group Predictive models of gene regulation from processed high throughput epigenomics data Regulatory Genomics Group Predictive models of gene regulation from processed high throughput epigenomics da Regulatory Genomics Group PANCREATIC EXPRESSION DATABASE BARTS CAN Multi species marker QTL SNP gene germplasm phenotype association with 37 Grapevine 8x stuctural annotation with Genetic maps g 38 Grapevine 12x 0 stuctural and functional annotation with Genetic maps g 39 Wheat stuctural annotation with Genetic maps g 40 Arabidopsis Thaliana TAIRV10 genes func 41 Zea mays ZmB73 genes func 42 Tomato stuctural and func 43 Populus trichocarpa genes func 44 Populus trichocarpa genes functiona 45 Botrytis cinerea T4 genes funct 46 Botrytis cinerea B0510 genes funct 47 Leptosphaeria maculans genes func 48 49 Vec 50 Vect 51 GRAMENE 40 ENSEMBL GENES 52 GRAMENE 40 VARIATION Note if the function useMart runs into proxy problems you should set your proxy first before calling any biomaRt functions You can do this using the Sys putenv command Sys putenv 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
13. 001005353 RefSeq Predicted Protein ID e g XP_001720922 Rfam ID Rfam transcript name RNACentral ID ucsc ID Unigene ID UniParc UniProt TrEMBL Accession UniProt SwissProt Accession UniProt Gene Name Uniprot Transcript Name WikiGene Name WikiGene ID WikiGene Description Agilent SurePrint G3 GE 8x60k probe Agilent SurePrint G3 GE 8x60k v2 probe Agilent WholeGenome 4x44k vi probe Agilent WholeGenome 4x44k v2 probe Affy HC G110 probeset 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 affy_hg_focus affy_hg_u133_plus_2 affy_hg_u133a_2 affy_hg_u133a affy_hg_u133b affy_hg_u95av2 affy_hg_u95b affy_hg_u95c affy_hg_u95d affy_hg_u95e affy_hg_u95a affy_hugenefl affy_hta_2_0 affy_huex_1_0_st_v2 affy_hugene_1_0_st_vi affy_hugene_2_0_st_vi affy_primeview affy_u133_x3p agilent_cgh_44b codelink illumina_humanwg_6_vi illumina_humanwg_6_v2 illumina_humanwg_6_v3 illumina_humanht_12_v3 illumina_humanht_12_v4 illumina_humanref_8_v3 phalanx_onearray family family_description pirsf pirsf_start pirsf_end superfamily superfamily_start superfamily_end smart smart_start smart_end hamap hamap_start hamap_end profile profile_start profile_end prosite 21 Affy HG FOCUS Affy HG U133 PLUS 2 Affy HG U133A_2 Affy HG U133A Affy HG U133B
14. 37 gt getSequence id entrez type entrezgene seqType coding_gene_flank upstream 100 mart ensembl 4 8 Task 8 Retrieve all 5 UTR sequences of all genes that are located on chromosome 3 between the positions 185514033 and 185535839 As 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 gt utr5 getSequence chromosome 3 start 185514033 end 185535839 type entrezgene seqType 5utr mart ensembl gt utr5 vi v2 Yna GAAGCGGTGGC 1981 4 9 Task 9 Retrieve protein seguences for a given list of EntrezGene identifiers In 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 gt protein getSeguence id c 100 5728 type entrezgene segType peptide mart ensembl gt protein peptide entrezgene MAGTPAFDKPKVEL 100 MTAIIKEIVSRNKRR 5728 13 4 10 Task 10 Retrieve known SNPs located on the human chromosome 8 between positions 148350 and 148612 For this example we ll first have to connect to a different BioMart database namely snp gt snpmart useMart snp dataset hsapiens_snp The listAttributes and listFilters functions give us an overview
15. 9 mim_morbid_accession 70 mim_morbid_description 71 mim_gene_accession 72 mim_gene_description 73 mirbase_accession 74 mirbase_id 75 mirbase_transcript_name 76 pdb TT protein id 78 pubmed 79 reactome 80 reactome_gene 81 reactome_transcript 82 refseg_mrna 83 refseq_mrna_predicted 84 refseq_ncrna 85 refseq_ncrna_predicted 86 refseq_peptide 87 refseq_peptide_predicted 88 rfam 89 rfam_transcript_name 90 rnacentral 91 ucsc 92 unigene 93 uniparc 94 uniprot_sptrembl 95 uniprot_swissprot 96 uniprot_genename 97 uniprot_genename_transcript_name 98 wikigene_name 99 wikigene_id 100 wikigene_description 101 efg_agilent_sureprint_g3_ge_8x60k 102 efg_agilent_sureprint_g3_ge_8x60k_v2 103 efg_agilent_wholegenome_4x44k_ v1 104 efg_agilent_wholegenome_4x44k_ v2 105 affy_hc_g110 20 Human Protein Atlas Antibody ID VEGA gene ID s OTTG VEGA transcript ID s OTTT VEGA protein ID s OTTP HGNC ID s HGNC symbol HGNC transcript name MEROPS ID MIM Morbid Accession MIM Morbid Description MIM Gene Accession MIM Gene Description miRBase Accession s miRBase ID s miRBase transcript name PDB ID Protein Genbank ID e g AAA02487 PubMed ID e g 7716543 Reactome ID Reactome gene ID e g REACT_1006 Reactome transcript ID e g REACT_11045 RefSeq mRNA e g NM_001195597 RefSeq mRNA predicted e g XM_001125684 RefSeq ncRNA e g NR_002834 RefSeq ncRNA predicted e g XR_108264 RefSeq Protein ID e g NP_
16. The biomaRt user s guide Steffen Durinck Wolfgang Hubert September 15 2015 Contents 1 Introduction 2 2 Selecting a BioMart database and dataset 3 How to build a biomaRt quer lt Nj Examples of biomaRt queries 1 Task 1 Annotate a set of Affymetrix identifiers with HUGO symbol and chromosomal locations of corresponding genes 2 Task 2 Annotate a set of EntrezGene identifiers with GO ALMOCALION e u 4 2 toa teh O O ae Hee gO eo a dee bees 3 Task 3 Retrieve all HUGO gene symbols of genes that are located on chromosomes 17 20 or Y and are associated with one the following GO terms GO 0051330 GO 0000080 GO 0000114 GO 0000082 here we ll use more than one filter domain identifiers GG ee 5 Task 5 Select all Affymetrix identifiers on the hgul33plus2 chip and Ensembl gene identifiers for genes located on chro mosome 16 between basepair 1100000 and 1250000 11 symbols of genes which have a MAP kinase activity GO term associated with it I ug 11 No rm oO m durincks gene com Thuber ebi ac uk j be Ease ee ee aes Sd 1 SI bo aes ARES OT OS ER he k 13 EEE ee ee a 13 vee eee 14 4 11 Task 11 Given the human gene TP53 retrieve the human chromosomal location of this gene and also retrieve the chro mosomal location and RefSeq id of it s homolog in mouse rr re ee ae re a rer rr
17. Tr 14 5 Using archived versions of Ensembl 15 5 1 Using thearchivec TRUE 15 5 2 Accessing archives through specifying the archive host 16 6 Using a BioMart other than Ensembl 16 7 biomaRt helper functions 17 7 1 exportFASIA I GI G a 17 7 2 Finding out more information on filters 17 2 1 filter Lype e e e Das Bog Ye Uy as Yee Dd GR 17 7 2 2 filterOptions I yg 17 7 3 Attribute Pagesl 0202000000048 18 8 Local BioMart databases 22 8 1 Minimum requirements for local database installation 23 23 25 1 Introduction 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 www biomart org 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 2 Selecting a BioMart database and dataset Every analysis with biomaRt starts with selecting a BioMart database to use A first
18. 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 G0 0051330 G0 0000080 G0 0000114 chrom c 17 20 Y getBM attributes hgnc_symbol filters c go_id chromosome_name values list go chrom mart ensembl hgnc_symbol 1 E2F1 4 4 Task 4 Annotate set of idenfiers with INTERPRO pro tein domain identifiers In 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 gt refsegids c NM_005359 NM_000546 gt ipro getBM attributes c refseg_dna interpro interpro_ description filters ipro refseq_dna interpro interpro_ description 1 NM_000546 IPR002117 p53 tumor antigen 2 NM_000546 IPRO10991 p53 tetramerisation 10 3 NM_000546 IPR011615 p53 DNA binding 4 NM_000546 IPRO13872 p53 transactivation domain TAD 5 NM_000546 IPR000694 Proline rich region 6 NM_005359 IPRO01132 MAD homology 2 Dwarfin type 7 NM_005359 IPR003619 MAD homology 1 Dwarfin type 8 NM_005359 IPRO13019 MAD homology MH1 4 5 Task 5 Select all Affymetrix identifiers on the hgu133plus2 chip and Ensembl gene identifiers for genes located on chromosome 16 between basepair 1100000 and 1250000 In this example we will again use
19. abase installation More information on installing a local copy of a BioMart database or develop our own BioMart database and webservice can be found on ep uw 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 name0fMyDataset host www myLocalHost org path myPathToWebservice martser For more information on how to install a public BioMart database see http www biomart org install html and follow link databases 9 Using select In order to provide a more consistent interface to all annotations in Biocon ductor 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 gt mart lt useMart dataset hsapiens_gene_ensembl biomart ensembl gt head keytypes mart n 3 1 chromosome_name start end gt head columns mart n 3 1 ensembl_gene_id ensembl_transcript_id ensembl_peptide_id 23 And you still can us
20. at 207500_at gt getBM attributes c affy_hg u133_plus_2 entrezgene filters affy_hg_u133_plus_2 values affyids mart affy_hg_u133_plus_2 entrezgene 1 209310_s_at 837 2 207500_at 838 3 202763_at 836 4 Examples of biomaRt queries In 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 4 1 Task 1 Annotate a set of Affymetrix identifiers with HUGO symbol and chromosomal locations of correspond ing genes We have a list of Affymetrix hgul33plus2 identifiers and we would like to retrieve the HUGO gene symbols chromosome names start and end po sitions and the bands of the corresponding genes The listAttributes and the listFilters functions give us an overview of the available at tributes and filters and we look in those lists to find the corresponding at tribute and filter names we need For this query we ll need the following at tributes hgnc_symbol chromsome_name start_position end_position band and affy_hg_ul33_plus_2 as we want these in the output to provide a map ping 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 gt affyids c 202763_at 209310_s_at 207500_at gt getBM attributes c affy_h
21. d giving the full name of the BioMart e g ensembl_mart_46 gt ensembl useMart ensembl_mart_46 dataset hsapiens_gene_ensembl archive TRU If you don t know the dataset you want to use could first connect to the BioMart using useMart and then use the listDatasets function on this object After you selected the BioMart database and dataset queries can be performed in the same way as when using the current BioMart versions 5 2 Accessing archives through specifying the archive host Use the http www ensembl org website and go down the bottom of the page Click on view in Archive and select the archive you need Copy the url and use that url as shown below to connect to the specified BioMart database The example below shows how to query Ensembl 54 gt listMarts host may2009 archive ensembl org gt ensembl54 useMart host may2009 archive ensembl org biomart ENSEMBL_MART_ENSEMBL gt ensemb154 useMart host may2009 archive ensembl org biomart ENSEMBL_MART_ENSEMBL dataset hsapiens_gene_ensem 6 Using a BioMart other than Ensembl To demonstrate the use of the biomaRt package with non Ensembl databases the next query is performed using the Wormbase BioMart WormMart We connect to Wormbase select the gene dataset to use and have a look at the available attributes and filters Then we use a list of gene names as filter and retrieve associated RNAi identifiers together with a description of the RNAi phenotype
22. e homologs snp snp_somatic sequences To show us a smaller list of attributes which belog to a specific page we can now specify this in the listAttributes function as follows gt listAttributes ensembl page feature_page name description 1 ensembl_gene_id Ensembl Gene ID 2 ensembl_transcript_id Ensembl Transcript ID 3 ensembl_peptide_id Ensembl Protein ID 4 ensembl_exon_id Ensembl Exon ID 5 description Description 6 chromosome_name Chromosome Name 7 start_position Gene Start bp 8 end_position Gene End bp 9 strand Strand 10 band Band 11 transcript_start Transcript Start bp 12 transcript_end Transcript End bp 13 transcription_start_site Transcription Start Site TSS 14 transcript_length Transcript length 15 transcript_tsl Transcript Support Level TSL 18 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 transcript_gencode_basic transcript_appris external_gene_name external_gene_source external_transcript_name external_transcript_source_name transcript_count percentage_gc_content gene_biotype transcript_biotype source transcript_source status transcript_status version transcript_version phenotype_description source_name study_external_id go_id name_1006 definition_1006 go_linkage_type namespace_1003 goslim_goa_accession goslim_goa_description arrayexpress che
23. e keys to extract potential keys for a particular key type gt k keys mart keytype chromosome_name gt head k n 3 1 4 ou zn When using keys you can even take advantage of the extra arguments that are available for others keys methods gt k keys mart keytype chromosome_name pattern LRG gt head k n 3 1 LRG_1 LRG_10 LRG_ 100 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 gt affy c 202763_at 209310_s_at 207500_at gt select mart keys affy columns c affy_hg_u133_plus_2 entrezgene keytype affy_hg_u133_plus_2 affy_hg_ul33_plus_2 entrezgene 1 209310_s_at 837 207500_at 838 3 202763_at 836 So 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 meth ods are shared allows for more convenient programmatic access of all these resources An example of a package that takes advantage of this is the Or ganismDbi package Where several packages can be accessed as if they were one resource 24
24. e 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 gt filterType with_affy_hg_u133_plus_2 ensembl 1 boolean_list 7 2 2 filterOptions Some filters have a limited set of values that can be given to them To know which values these are one can use the filter0ptions function to retrieve the predetermed values of the respective filter 17 gt filter0ptions biotype ensembl 1 3prime_overlapping_ncrna antisense IG_C_gene IG_C_pseudogene IG_D_gene IG_J_gene IG_J_p 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 7 3 Attribute Pages 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 func tion gt pages attributePages ensembl gt pages 1 feature_page structur
25. embl ttruncatus_gene_ensembl scerevisiae_gene_ensembl celegans_gene_ensembl csabaeus_gene_ensembl oniloticus_gene_ensembl trubripes_gene_ensembl amexicanus_gene_ensembl pmarinus_gene_ensembl eeuropaeus_gene_ensembl falbicollis_gene_ensembl ptroglodytes_gene_ensembl etelfairi_gene_ensembl cintestinalis_gene_ensembl nleucogenys_gene_ensembl sscrofa_gene_ensembl ocuniculus_gene_ensembl dnovemcinctus_gene_ensembl pcapensis_gene_ensembl tguttata_gene_ensembl mlucifugus_gene_ensembl hsapiens_gene_ensembl pformosa_gene_ensembl mfuro_gene_ensembl tbelangeri_gene_ensembl ggallus_gene_ensembl xtropicalis_gene_ensembl ecaballus_gene_ensembl pabelii_gene_ensembl xmaculatus_gene_ensembl drerio_gene_ensembl lchalumnae_gene_ensembl tnigroviridis_gene_ensembl amelanoleuca_gene_ensembl mmulatta_gene_ensembl pvampyrus_gene_ensembl panubis_gene_ensembl mdomestica_gene_ensembl acarolinensis_gene_ensembl vpacos_gene_ensembl tsyrichta_gene_ensembl ogarnettii_gene_ensembl dmelanogaster_gene_ensembl mmurinus_gene_ensembl loculatus_gene_ensembl olatipes_gene_ensembl ggorilla_gene_ensembl oprinceps_gene_ensembl dordii_gene_ensembl oaries_gene_ensembl mmusculus_gene_ensembl mgallopavo_gene_ensembl gmorhua_gene_ensembl aplatyrhynchos_gene_ensembl Rattus norvegicus genes Rnor_6 0 Pelodiscus sinensis genes PelSin_1 0 Callithrix jacchus genes C_jacchus3 2 1 Tursiops truncatus genes turTru1 Saccharomyces cerevisiae genes R64 1 1 Caeno
26. g_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 104813593 104840163 q22 3 2 207500_at CASP5 11 104864962 104893895 q22 3 3 202763_at CASP3 4 185548850 185570663 q35 1 4 2 Task 2 Annotate a set of EntrezGene identifiers with GO annotation In 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 con struct the following query gt entrez c 673 837 gt goids getBM attributes c entrezgene go_id filters entrezgene values entrez mart ensembl gt head goids entrezgene go_id 673 G0 0000186 673 G0 0006468 673 G0 0006916 673 G0 0007264 673 G0 0007268 PpPpwomr 4 3 Task 3 Retrieve all HUGO gene symbols of genes that are located on chromosomes 17 20 or Y and are associated with one the following GO terms GO 0051330 GO 0000080 GO 0000114 GO 0000082 here we ll use more than one filter The getBM function enables you to use more than one filter In this case the filter argument should be a vector with the filter names The values should be
27. lates 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 14 datasets or to one of the datasets Use the listFilters and list Attributes 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 attributesL c refseq_dna chromosome_name human start_position martL mouse Vi V2 V3 v4 V5 V6 1 TP53 17 7512464 NM_011640 11 69396600 5 Using archived versions of Ensembl It is possible to query archived versions of Ensembl through biomaRt There are currently two ways to access archived versions 5 1 Using the archive TRUE First we list the available Ensembl archives by using the listMarts function and setting the archive attribute to TRUE Note that not all archives are available this way and it seems that recently this only gives access to few archives if you don t see the
28. mbl clone_based_ensembl_gene_name clone_based_ensembl_transcript_name clone_based_vega_gene_name clone_based_vega_transcript_name ccds GENCODE basic annotation APPRIS annotation Associated Gene Name Associated Gene Source Associated Transcript Name Associated Transcript Source Transcript count GC content Gene type Transcript type Source gene Source transcript Status gene Status transcript Version gene Version transcript Phenotype description Source name Study External Reference GO Term Accession GO Term Name GO Term Definition GO Term Evidence Code GO domain GOSlim GOA Accession s GOSlim GOA Description ArrayExpress ChEMBL ID s Clone based Ensembl gene name Clone based Ensembl transcript name Clone based VEGA gene name Clone based VEGA transcript name CCDS ID dbass3_id Database of Aberrant 3 Splice Sites DBASS3 IDs dbass3_name DBASS3 Gene Name dbass5_id Database of Aberrant 5 Splice Sites DBASS5 IDs dbass5_name embl ens_hs_gene ens_hs_transcript ens_hs_translation ens_lrg_gene ens_lrg_transcript entrezgene entrezgene_transcript_name 19 DBASS5 Gene Name EMBL Genbank ID Ensembl Human Gene IDs Ensembl Human Transcript IDs Ensembl Human Translation IDs LRG to Ensembl link gene LRG to Ensembl link transcript EntrezGene ID EntrezGene transcript name ID 61 hpa 62 ottg 63 ottt 64 ottp 65 hgnc_id 66 hgnc_symbol 67 hgnc_transcript_name 68 merops 6
29. nsmembrane_domain transmembrane_domain_start transmembrane_domain_end signal_domain signal_domain_start signal_domain_end ncoils ncoils_start ncoils_end genes are located 8 Local BioMart databases ScanProsite start ScanProsite end PRINTS ID PRINTS start PRINTS end Pfam ID Pfam start Pfam end TIGRFAM ID TIGRFAM start TIGRFAM end Gene3D ID Gene3D start Gene3D end HMMPanther ID HMMPanther start HMMPanther end Interpro ID Interpro Short Description Interpro Description Interpro start Interpro end low complexity SEG low complexity SEG start low complexity SEG end Transmembrane domain tmhmm Transmembrane domain tmhmm start Transmembrane domain tmhmm end signal peptide signal peptide start signal peptide end coiled coil ncoils coiled coil ncoils start coiled coil ncoils end We now get a short list of attributes related to the region where the 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 22 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 8 1 Minimum requirements for local dat
30. rhabditis elegans genes WBcel235 Chlorocebus sabaeus genes ChlSabi 1 Oreochromis niloticus genes Orenil1 0 Takifugu rubripes genes FUGU4 0 Astyanax mexicanus genes AstMex102 Petromyzon marinus genes Pmarinus_7 0 Erinaceus europaeus genes eriEur1 Ficedula albicollis genes FicAlb_1 4 Pan troglodytes genes CHIMP2 1 4 Echinops telfairi genes TENREC Ciona intestinalis genes KH Nomascus leucogenys genes Nleu1 0 Sus scrofa genes Sscrofa10 2 Oryctolagus cuniculus genes OryCun2 0 Dasypus novemcinctus genes Dasnov3 0 Procavia capensis genes proCap1 Taeniopygia guttata genes taeGut3 2 4 Myotis lucifugus genes myoLuc2 Homo sapiens genes GRCh38 p3 Poecilia formosa genes PoeFor_5 1 2 Mustela putorius furo genes MusPutFur1 0 Tupaia belangeri genes tupBel1 Gallus gallus genes Galgal4 Xenopus tropicalis genes JGI4 2 Equus caballus genes EquCab2 Pongo abelii genes PPYG2 Xiphophorus maculatus genes Xipmac4 4 2 Danio rerio genes GRCz10 Latimeria chalumnae genes LatCha1 Tetraodon nigroviridis genes TETRAODON8 0 Ailuropoda melanoleuca genes ailMel1 Macaca mulatta genes MMUL_1 Pteropus vampyrus genes pteVam1 Papio anubis genes PapAnu2 0 Monodelphis domestica genes monDom5 Anolis carolinensis genes AnoCar2 0 Vicugna pacos genes vicPac1 Tarsius syrichta genes tarSyr1 Otolemur garnettii genes OtoGar3 Drosophila melanogaster genes BDGP6 Microcebus murinus genes micMur1
31. set one wants to use is known in advance we can select a BioMart database and dataset in one step by gt ensembl useMart ensembl dataset hsapiens_gene_ensembl 3 How to build a biomaRt query 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 chro mosome then the filter chromosome_name can be used with value X The listFilters function shows you all available filters in the selected dataset gt filters listFilters ensembl gt filters 1 5 name description 1 chromosome_name Chromosome name 2 start Gene Start bp 3 end Gene End bp 4 band_start Band Start 5 band_end Band End Attributes define the values we are interested in to retrieve For example we want to retrieve the gene symbols or chromosomal coordinates The lis tAttributes function displays all available attributes in the selected dataset gt attributes listAttributes ensembl gt attributes 1 5 name description 1 ensembl_gene_id Ensembl Gene ID 2 ensembl_transcript_id Ensembl Transcript ID 3 ensembl_peptide_id Ensembl Protein ID 4 ensembl_exon_id Ensembl Exon ID 5 description Description The getBM function is the main query function in biomaRt It has four main arguments e attributes is a vector of attributes that one wants to retrieve the output of the query
32. step is to check which BioMart web services are available The function listMarts will display all available BioMart web services gt library biomaRt gt listMarts biomart 1 ensembl 2 snp 3 regulation 4 vega 5 fungi_mart_28 6 fungi_variations_28 7 metazoa_mart_28 8 metazoa_variations_28 9 plants_mart_28 10 plants_variations_28 11 protists_mart_28 12 protists_variations_28 13 msd 14 cg_mart_02 15 WS220 16 parasite_mart 17 biomart 18 example 19 prod intermart_1 20 unimart 21 biomartDB 22 biblioDB 23 Eurexpress Biomart 24 phytozome_mart 25 metazome_mart 26 HapMap_rel27 27 GermOnline 28 Sigenae_Oligo_Annotation_Ensemb1_61 29 Sigenae Oligo Annotation Ensembl 59 30 Sigenae Oligo Annotation Ensembl 56 31 Breast_mart_69 32 K562_Gm12878 33 Hsmm_Hmec 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 oono PUNE WWWWWWWNNNNNNNNNNKPRPRPBRP BRB RBBB oT PWDNDHRrOOO NOT PWDHRrOOO NND UT PWMN Hr O allo2012 Pancreas63 Public 0BIOMARTPUB Public _VITIS Public VITIS_12x Prod_WHEAT Public TAIRV10 Public _MAIZE Prod_TOMATO Prod_POPLAR Prod_POPLAR_V2 Prod_BOTRYTISEDIT Prod_BOFUB Prod_LMACULANSEDIT vb_gene_mart_1506 vb_snp_mart_1506 expression ENSEMBL_MART_PLANT ENSEMBL_MART_PLANT_SNP ENSEMBL GEN ENSEMBL VARIAT ENSEMBL REGULAT VEG ENSEMBL ENSEMBL FUNGI VAR ENSEMBL ME ENSEMBL METAZOA VAR ENSEMBL P ENSEMBL PLANTS VAR ENSEMBL PRO ENSEMBL PROTISTS VAR PROTEOMICS UNIVERSITY
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