# The biomaRt users guide

Steffen Durinck, Wolfgang Huber, Mike Smith

#### 14 December 2017

#### Package

biomaRt 2.34.1

# Contents

# 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. 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 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 91
    ## 2   ENSEMBL_MART_MOUSE      Mouse strains 91
    ## 3     ENSEMBL_MART_SNP  Ensembl Variation 91
    ## 4 ENSEMBL_MART_FUNCGEN Ensembl Regulation 91

Note: if the function `useMart()` runs into proxy problems you should set your proxy first before calling any biomaRt functions.
You can do this using the Sys.putenv command:

    Sys.setenv("http_proxy" = "http://my.proxy.org:9999")

Some users have reported that the workaround above does not work, in this case an alternative proxy solution below can be tried:

    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       amelanoleuca_gene_ensembl                                        Panda genes (ailMel1)                 ailMel1
    ## 2             dordii_gene_ensembl                                Kangaroo rat genes (Dord_2.0)                Dord_2.0
    ## 3            mpahari_gene_ensembl                          Shrew mouse genes (PAHARI_EIJ_v1.1)         PAHARI_EIJ_v1.1
    ## 4          trubripes_gene_ensembl                                        Fugu genes (FUGU 4.0)                FUGU 4.0
    ## 5           pmarinus_gene_ensembl                                 Lamprey genes (Pmarinus_7.0)            Pmarinus_7.0
    ## 6          sharrisii_gene_ensembl                       Tasmanian devil genes (Devil_ref v7.0)          Devil_ref v7.0
    ## 7        nleucogenys_gene_ensembl                                      Gibbon genes (Nleu_3.0)                Nleu_3.0
    ## 8            ggallus_gene_ensembl                            Chicken genes (Gallus_gallus-5.0)       Gallus_gallus-5.0
    ## 9           olatipes_gene_ensembl                                          Medaka genes (HdrR)                    HdrR
    ## 10        pcoquereli_gene_ensembl                           Coquerel's sifaka genes (Pcoq_1.0)                Pcoq_1.0
    ## 11         pvampyrus_gene_ensembl                                      Megabat genes (pteVam1)                 pteVam1
    ## 12     acarolinensis_gene_ensembl                               Anole lizard genes (AnoCar2.0)               AnoCar2.0
    ## 13           hfemale_gene_ensembl              Naked mole-rat female genes (HetGla_female_1.0)       HetGla_female_1.0
    ## 14        ogarnettii_gene_ensembl                                     Bushbaby genes (OtoGar3)                 OtoGar3
    ## 15     tnigroviridis_gene_ensembl                              Tetraodon genes (TETRAODON 8.0)           TETRAODON 8.0
    ## 16       falbicollis_gene_ensembl                                Flycatcher genes (FicAlb_1.4)              FicAlb_1.4
    ## 17     mfascicularis_gene_ensembl          Crab-eating macaque genes (Macaca_fascicularis_5.0) Macaca_fascicularis_5.0
    ## 18          neugenii_gene_ensembl                                     Wallaby genes (Meug_1.0)                Meug_1.0
    ## 19           ngalili_gene_ensembl Upper Galilee mountains blind mole rat genes (S.galili_v1.0)           S.galili_v1.0
    ## 20          saraneus_gene_ensembl                                        Shrew genes (sorAra1)                 sorAra1
    ## 21        ttruncatus_gene_ensembl                                      Dolphin genes (turTru1)                 turTru1
    ## 22        cpalliatus_gene_ensembl                           Angola colobus genes (Cang.pa_1.0)             Cang.pa_1.0
    ## 23       scerevisiae_gene_ensembl                     Saccharomyces cerevisiae genes (R64-1-1)                 R64-1-1
    ## 24        mgallopavo_gene_ensembl                                   Turkey genes (Turkey_2.01)             Turkey_2.01
    ## 25     cintestinalis_gene_ensembl                                    C.intestinalis genes (KH)                      KH
    ## 26 itridecemlineatus_gene_ensembl                                   Squirrel genes (SpeTri2.0)               SpeTri2.0
    ## 27            odegus_gene_ensembl                                       Degu genes (OctDeg1.0)               OctDeg1.0
    ## 28         psinensis_gene_ensembl                  Chinese softshell turtle genes (PelSin_1.0)              PelSin_1.0
    ## 29       fdamarensis_gene_ensembl                             Damara mole rat genes (DMR_v1.0)                DMR_v1.0
    ## 30           gmorhua_gene_ensembl                                          Cod genes (gadMor1)                 gadMor1
    ## 31          pformosa_gene_ensembl                  Amazon molly genes (Poecilia_formosa-5.1.2)  Poecilia_formosa-5.1.2
    ## 32         csavignyi_gene_ensembl                                  C.savignyi genes (CSAV 2.0)                CSAV 2.0
    ## 33         ppaniscus_gene_ensembl                                     Bonobo genes (panpan1.1)               panpan1.1
    ## 34          mauratus_gene_ensembl                             Golden Hamster genes (MesAur1.0)               MesAur1.0
    ## 35        choffmanni_gene_ensembl                                        Sloth genes (choHof1)                 choHof1
    ## 36          csabaeus_gene_ensembl                                 Vervet-AGM genes (ChlSab1.1)               ChlSab1.1
    ## 37        lchalumnae_gene_ensembl                                   Coelacanth genes (LatCha1)                 LatCha1
    ## 38       rnorvegicus_gene_ensembl                                         Rat genes (Rnor_6.0)                Rnor_6.0
    ## 39          cjacchus_gene_ensembl                              Marmoset genes (C_jacchus3.2.1)          C_jacchus3.2.1
    ## 40          pbairdii_gene_ensembl                Northern American deer mouse genes (Pman_1.0)                Pman_1.0
    ## 41           mcaroli_gene_ensembl                         Ryukyu mouse genes (CAROLI_EIJ_v1.1)         CAROLI_EIJ_v1.1
    ## 42          hsapiens_gene_ensembl                                     Human genes (GRCh38.p10)              GRCh38.p10
    ## 43           btaurus_gene_ensembl                                           Cow genes (UMD3.1)                  UMD3.1
    ## 44            drerio_gene_ensembl                                     Zebrafish genes (GRCz10)                  GRCz10
    ## 45             mfuro_gene_ensembl                                  Ferret genes (MusPutFur1.0)            MusPutFur1.0
    ## 46           caperea_gene_ensembl                        Brazilian guinea pig genes (CavAp1.0)                CavAp1.0
    ## 47             catys_gene_ensembl                              Sooty mangabey genes (Caty_1.0)                Caty_1.0
    ## 48       mnemestrina_gene_ensembl                          Pig-tailed macaque genes (Mnem_1.0)                Mnem_1.0
    ## 49          ggorilla_gene_ensembl                                      Gorilla genes (gorGor4)                 gorGor4
    ## 50         csyrichta_gene_ensembl                       Tarsier genes (Tarsius_syrichta-2.0.1)  Tarsius_syrichta-2.0.1
    ## 51            vpacos_gene_ensembl                                       Alpaca genes (vicPac1)                 vicPac1
    ## 52      ptroglodytes_gene_ensembl                               Chimpanzee genes (Pan_tro_3.0)             Pan_tro_3.0
    ## 53            rbieti_gene_ensembl                  Black snub-nosed monkey genes (ASM169854v1)             ASM169854v1
    ## 54        tbelangeri_gene_ensembl                                   Tree Shrew genes (tupBel1)                 tupBel1
    ## 55        mdomestica_gene_ensembl                                      Opossum genes (monDom5)                 monDom5
    ## 56         oprinceps_gene_ensembl                                   Pika genes (OchPri2.0-Ens)           OchPri2.0-Ens
    ## 57      mleucophaeus_gene_ensembl                                    Drill genes (Mleu.le_1.0)             Mleu.le_1.0
    ## 58        ccapucinus_gene_ensembl                          Capuchin genes (Cebus_imitator-1.0)      Cebus_imitator-1.0
    ## 59          mmulatta_gene_ensembl                                   Macaque genes (Mmul_8.0.1)              Mmul_8.0.1
    ## 60        mlucifugus_gene_ensembl                                   Microbat genes (Myoluc2.0)               Myoluc2.0
    ## 61        gaculeatus_gene_ensembl                                 Stickleback genes (BROAD S1)                BROAD S1
    ## 62         lafricana_gene_ensembl                                   Elephant genes (Loxafr3.0)               Loxafr3.0
    ## 63         etelfairi_gene_ensembl                        Lesser hedgehog tenrec genes (TENREC)                  TENREC
    ## 64         loculatus_gene_ensembl                                  Spotted gar genes (LepOcu1)                 LepOcu1
    ## 65        xmaculatus_gene_ensembl                                Platyfish genes (Xipmac4.4.2)             Xipmac4.4.2
    ## 66          celegans_gene_ensembl                      Caenorhabditis elegans genes (WBcel235)                WBcel235
    ## 67           panubis_gene_ensembl                                Olive baboon genes (Panu_3.0)                Panu_3.0
    ## 68             hmale_gene_ensembl                       Naked mole-rat male genes (HetGla_1.0)              HetGla_1.0
    ## 69            oaries_gene_ensembl                                       Sheep genes (Oar_v3.1)                Oar_v3.1
    ## 70          mmurinus_gene_ensembl                                 Mouse Lemur genes (Mmur_3.0)                Mmur_3.0
    ## 71         pcapensis_gene_ensembl                                        Hyrax genes (proCap1)                 proCap1
    ## 72        ocuniculus_gene_ensembl                                     Rabbit genes (OryCun2.0)               OryCun2.0
    ## 73    aplatyrhynchos_gene_ensembl                                    Duck genes (BGI_duck_1.0)            BGI_duck_1.0
    ## 74        anancymaae_gene_ensembl                           Ma's night monkey genes (Anan_2.0)                Anan_2.0
    ## 75           pabelii_gene_ensembl                                      Orangutan genes (PPYG2)                   PPYG2
    ## 76         mspreteij_gene_ensembl                         Mouse SPRET/EiJ genes (SPRET_EiJ_v1)            SPRET_EiJ_v1
    ## 77        rroxellana_gene_ensembl                     Golden snub-nosed monkey genes (Rrox_v1)                 Rrox_v1
    ## 78          jjaculus_gene_ensembl                     Lesser Egyptian jerboa genes (JacJac1.0)               JacJac1.0
    ## 79        cporcellus_gene_ensembl                                 Guinea Pig genes (Cavpor3.0)               Cavpor3.0
    ## 80      mochrogaster_gene_ensembl                               Prairie vole genes (MicOch1.0)               MicOch1.0
    ## 81     dmelanogaster_gene_ensembl                                       Fruitfly genes (BDGP6)                   BDGP6
    ## 82         clanigera_gene_ensembl                     Long-tailed chinchilla genes (ChiLan1.0)               ChiLan1.0
    ## 83       cfamiliaris_gene_ensembl                                        Dog genes (CanFam3.1)               CanFam3.1
    ## 84        cchok1gshd_gene_ensembl                 Chinese hamster CHOK1GS genes (CHOK1GS_HDv1)            CHOK1GS_HDv1
    ## 85       xtropicalis_gene_ensembl                                      Xenopus genes (JGI 4.2)                 JGI 4.2
    ## 86        oniloticus_gene_ensembl                                    Tilapia genes (Orenil1.0)               Orenil1.0
    ## 87     dnovemcinctus_gene_ensembl                                  Armadillo genes (Dasnov3.0)               Dasnov3.0
    ## 88           sscrofa_gene_ensembl                                      Pig genes (Sscrofa11.1)             Sscrofa11.1
    ## 89         ecaballus_gene_ensembl                                      Horse genes (Equ Cab 2)               Equ Cab 2
    ## 90         mmusculus_gene_ensembl                                      Mouse genes (GRCm38.p5)               GRCm38.p5
    ## 91         oanatinus_gene_ensembl                                       Platypus genes (OANA5)                   OANA5
    ## 92          tguttata_gene_ensembl                              Zebra Finch genes (taeGut3.2.4)             taeGut3.2.4
    ## 93            fcatus_gene_ensembl                                  Cat genes (Felis_catus_8.0)         Felis_catus_8.0
    ## 94        amexicanus_gene_ensembl                                  Cave fish genes (AstMex102)               AstMex102
    ## 95        eeuropaeus_gene_ensembl                                     Hedgehog genes (eriEur1)                 eriEur1
    ## 96           ccrigri_gene_ensembl                    Chinese hamster CriGri genes (CriGri_1.0)              CriGri_1.0
    ## 97      sboliviensis_gene_ensembl                   Bolivian squirrel monkey genes (SaiBol1.0)               SaiBol1.0

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 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")

# 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 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 End

Attributes 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_page

The `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           202763_at        836
    ## 2         209310_s_at        837
    ## 3           207500_at        838

# 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 Annotate a set of Affymetrix identifiers with HUGO symbol and chromosomal locations of corresponding genes

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           202763_at       CASP3               4      184627696    184649509 q35.1
    ## 2         209310_s_at       CASP4              11      104942866    104969436 q22.3
    ## 3           207500_at       CASP5              11      104994235    105023168 q22.3

## 4.2 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 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:0000166
    ## 2        673 GO:0004672
    ## 3        673 GO:0004674
    ## 4        673 GO:0005524
    ## 5        673 GO:0006468
    ## 6        673 GO:0010628

## 4.3 Retrieve all HUGO gene symbols of genes that are located on chromosomes 17,20 or Y, and are associated with specific GO terms

The 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 names

## 4.4 Annotate set of idenfiers with INTERPRO protein 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.

    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
    ## 6    NM_000546 IPR013872                             p53 transactivation domain
    ## 7    NM_005359 IPR001132                              SMAD domain, Dwarfin-type
    ## 8    NM_005359 IPR003619                           MAD homology 1, Dwarfin-type
    ## 9    NM_005359 IPR008984                                        SMAD/FHA domain
    ## 10   NM_005359 IPR013019                                      MAD homology, MH1
    ## 11   NM_005359 IPR013790                                                Dwarfin
    ## 12   NM_005359 IPR017855                                       SMAD domain-like

## 4.5 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 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 ENSG00000172236

## 4.6 Retrieve all entrezgene identifiers and HUGO gene symbols of genes which have a “MAP kinase activity” GO term associated with it.

The 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      225689      MAPK15
    ## 2        5594       MAPK1
    ## 3        5595       MAPK3
    ## 4        6300      MAPK12
    ## 5        5600      MAPK11
    ## 6       51701         NLK
    ## 7        5598       MAPK7
    ## 8        5596       MAPK4
    ## 9        1432      MAPK14
    ## 10       5603      MAPK13
    ## 11       5597       MAPK6
    ## 12       5599       MAPK8
    ## 13       5601       MAPK9
    ## 14       5602      MAPK10

## 4.7 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.

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:

- 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 exons + 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
- coding_gene_flank gives the flanking region of the gene including the UTRs, this must be accompanied 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 excluding 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 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 CCTCCGCCTCCGCCTCCGCCTCCGCCTCCCCCAGCTCTCCGCCTCCCTTCCCCCTCCCCGCCCGACAGCGGCCGCTCGGGCCCCGGCTCTCGGTTATAAG        673
    ## 2 CACGTTTCCGCCCTTTGCAATAAGGAAATACATAGTTTACTTTCATTTTTGACTCTGAGGCTCTTTCCAACGCTGTAAAAAAGGACAGAGGCTGTTCCCT        837
    ## 3 TCCTTCTCTGCAGGCCCAGGTGACCCAGGGTTGGAAGTGTCTCATGCTGGATCCCCACTTTTCCTCTTGCAGCAGCCAGACTGCCTTCCGGGTCACTGCC       7157

## 4.8 Retrieve all 5’ UTR sequences of all genes that are located on chromosome 3 between the positions 185,514,033 and 185,535,839

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.

    utr5 = getSequence(chromosome=3, start=185514033, end=185535839,
                       type="entrezgene",
                       seqType="5utr",
                       mart=ensembl)
    utr5

    ##                                                                                                                                             5utr
    ## 1                                                                                                                           Sequence unavailable
    ## 2                                                        TGAGCAAAATCCCACAGTGGAAACTCTTAAGCCTCTGCGAAGTAAATCATTCTTGTGAATGTGACACACGATCTCTCCAGTTTCCAT
    ## 3                                                                                                        ATTCTTGTGAATGTGACACACGATCTCTCCAGTTTCCAT
    ## 4 AGTCCCTAGGGAACTTCCTGTTGTCACCACACCTCTGAGTCGTCTGAGCTCACTGTGAGCAAAATCCCACAGTGGAAACTCTTAAGCCTCTGCGAAGTAAATCATTCTTGTGAATGTGACACACGATCTCTCCAGTTTCCAT
    ##   entrezgene
    ## 1     200879
    ## 2     200879
    ## 3     200879
    ## 4     200879

## 4.9 Retrieve protein sequences 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.

    protein = getSequence(id=c(100, 5728),
                          type="entrezgene",
                          seqType="peptide",
                          mart=ensembl)
    protein

    ##                                                                                                                                                                                                                                                                                                                                                                                                                peptide
    ## 1 MTAIIKEIVSRNKRRYQEDGFDLDLTYIYPNIIAMGFPAERLEGVYRNNIDDVVRFLDSKHKNHYKIYNLCAERHYDTAKFNCRVAQYPFEDHNPPQLELIKPFCEDLDQWLSEDDNHVAAIHCKAGKGRTGVMICAYLLHRGKFLKAQEALDFYGEVRTRDKKGVTIPSQRRYVYYYSYLLKNHLDYRPVALLFHKMMFETIPMFSGGTCNPQFVVCQLKVKIYSSNSGPTRREDKFMYFEFPQPLPVCGDIKVEFFHKQNKMLKKDKMFHFWVNTFFIPGPEETSEKVENGSLCDQEIDSICSIERADNDKEYLVLTLTKNDLDKANKDKANRYFSPNFKVKLYFTKTVEEPSNPEASSSTSVTPDVSDNEPDHYRYSDTTDSDPENEPFDEDQHTQITKV*
    ## 2                                                                                                                                                                                                                                                           ALLFHKMMFETIPMFSGGTCNPQFVVCQLKVKIYSSNSGPTRREDKFMYFEFPQPLPVCGDIKVEFFHKQNKMLKKDKMFHFWVNTFFIPGPEETSEKVENGSLCDQEIDSICSIERADNDKEYLVLTLTKNDLDKANKDKANRYFSPNFKVS*
    ## 3                                                                                                                                                                                                                                                                                                                                                                                                 Sequence unavailable
    ## 4                                                                                                                                                                                                                                                                                                                                                                                                 Sequence unavailable
    ## 5                                         MAQTPAFDKPKVELHVHLDGSIKPETILYYGRRRGIALPANTAEGLLNVIGMDKPLTLPDFLAKFDYYMPAIAGCREAIKRIAYEFVEMKAKEGVVYVEVRYSPHLLANSKVEPIPWNQAEGDLTPDEVVALVGQGLQEGERDFGVKARSILCCMRHQPNWSPKVVELCKKYQQQTVVAIDLAGDETIPGSSLLPGHVQAYQEAVKSGIHRTVHAGEVGSAEVVKEAVDILKTERLGHGYHTLEDQALYNRLRQENMHFEICPWSSYLTGAWKPDTEHAVIRLKNDQANYSLNTDDPLIFKSTLDTDYQMTKRDMGFTEEEFKRLNINAAKSSFLPEDEKRELLDLLYKAYGMPPSASAGQNL*
    ## 6                                                                                                                                                                                                                                                                                                                                         MAQTPAFDKPKVELHVHLDGSIKPETILYYGRRRGIALPANTAEGLLNVIGMDKPLTLPDFLAKFDYYMPAIARL*
    ## 7                                                                 MAQTPAFDKPKVELHVHLDGSIKPETILYYGRRRGIALPANTAEGLLNVIGMDKPLTLPDFLAKFDYYMPAIAGCREAIKRIAYEFVEMKAKEGVVYVEVRYSPHLLANSKVEPIPWNQAEGDLTPDEVVALVGQGLQEGERDFGVKARSILCCMRHQPNWSPKVVELCKKYQQQTVVAIDLAGDETIPGSSLLPGHVQAYQAVDILKTERLGHGYHTLEDQALYNRLRQENMHFEICPWSSYLTGAWKPDTEHAVIRLKNDQANYSLNTDDPLIFKSTLDTDYQMTKRDMGFTEEEFKRLNINAAKSSFLPEDEKRELLDLLYKAYGMPPSASAGQNL*
    ## 8                                                                                                                                             MAQTPAFDKPKVELHVHLDGSIKPETILYYGRRRGIALPANTAEGLLNVIGMDKPLTLPDFLAKFDYYMPAIAGCREAIKRIAYEFVEMKAKEGVVYVEVRYSPHLLANSKVEPIPWNQAEGDLTPDEVVALVGQGLQEGERDFGVKARSILCCMRHQPNWSPKVVELCKKYQQQTVVAIDLAGDETIPGSSLLPGHVQAYQEAVKSGIHRTVHAGEVGSAEVVKEAVDILKTERLGHGYHTLEDQALYNRLRQENMHFEAQK*
    ##   entrezgene
    ## 1       5728
    ## 2       5728
    ## 3       5728
    ## 4        100
    ## 5        100
    ## 6        100
    ## 7        100
    ## 8        100

## 4.10 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.

    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            1

## 4.11 Given the human gene TP53, retrieve the human chromosomal location of this gene and also retrieve the chromosomal location and RefSeq id of its 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 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          69580359

# 5 Using archived versions of Ensembl

It 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()

    ##       version          date       url
    ##  [1,] "Ensembl GRCh37" "Feb 2014" "http://grch37.ensembl.org"
    ##  [2,] "Ensembl 91"     "Dec 2017" "http://Dec2017.archive.ensembl.org"
    ##  [3,] "Ensembl 90"     "Aug 2017" "http://Aug2017.archive.ensembl.org"
    ##  [4,] "Ensembl 89"     "May 2017" "http://May2017.archive.ensembl.org"
    ##  [5,] "Ensembl 88"     "Mar 2017" "http://Mar2017.archive.ensembl.org"
    ##  [6,] "Ensembl 87"     "Dec 2016" "http://Dec2016.archive.ensembl.org"
    ##  [7,] "Ensembl 86"     "Oct 2016" "http://Oct2016.archive.ensembl.org"
    ##  [8,] "Ensembl 85"     "Jul 2016" "http://Jul2016.archive.ensembl.org"
    ##  [9,] "Ensembl 84"     "Mar 2016" "http://Mar2016.archive.ensembl.org"
    ## [10,] "Ensembl 83"     "Dec 2015" "http://Dec2015.archive.ensembl.org"
    ## [11,] "Ensembl 82"     "Sep 2015" "http://Sep2015.archive.ensembl.org"
    ## [12,] "Ensembl 81"     "Jul 2015" "http://Jul2015.archive.ensembl.org"
    ## [13,] "Ensembl 80"     "May 2015" "http://May2015.archive.ensembl.org"
    ## [14,] "Ensembl 79"     "Mar 2015" "http://Mar2015.archive.ensembl.org"
    ## [15,] "Ensembl 78"     "Dec 2014" "http://Dec2014.archive.ensembl.org"
    ## [16,] "Ensembl 77"     "Oct 2014" "http://Oct2014.archive.ensembl.org"
    ## [17,] "Ensembl 76"     "Aug 2014" "http://Aug2014.archive.ensembl.org"
    ## [18,] "Ensembl 75"     "Feb 2014" "http://Feb2014.archive.ensembl.org"
    ## [19,] "Ensembl 74"     "Dec 2013" "http://Dec2013.archive.ensembl.org"
    ## [20,] "Ensembl 67"     "May 2012" "http://May2012.archive.ensembl.org"
    ## [21,] "Ensembl 54"     "May 2009" "http://May2009.archive.ensembl.org"

Alternatively, 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')

# 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 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 ParaSite Mart

    wormbase = useMart(biomart = "parasite_mart", host = "parasite.wormbase.org")
    listDatasets(wormbase)

    ##     dataset         description version
    ## 1 wbps_gene All Species (WBPS9)       9

    wormbase <- useDataset(mart = wormbase, dataset = "wbps_gene")
    head(listFilters(wormbase))

    ##                  name     description
    ## 1     species_id_1010          Genome
    ## 2 nematode_clade_1010  Nematode Clade
    ## 3     chromosome_name Chromosome name
    ## 4               start           Start
    ## 5                 end             End
    ## 6              strand          Strand

    head(listAttributes(wormbase))

    ##                      name        description         page
    ## 1          species_id_key      Internal Name feature_page
    ## 2    production_name_1010     Genome project feature_page
    ## 3       display_name_1010        Genome name feature_page
    ## 4        taxonomy_id_1010        Taxonomy ID feature_page
    ## 5 assembly_accession_1010 Assembly accession feature_page
    ## 6     nematode_clade_1010     Nematode clade feature_page

    getBM(attributes = c("external_gene_id", "wbps_transcript_id", "transcript_biotype"),
          filters="gene_name",
          values=c("unc-26","his-33"),
          mart=wormbase)

    ##   external_gene_id wbps_transcript_id transcript_biotype
    ## 1           his-33           F17E9.13     protein_coding
    ## 2           unc-26            JC8.10a     protein_coding
    ## 3           unc-26            JC8.10b     protein_coding
    ## 4           unc-26          JC8.10c.1     protein_coding
    ## 5           unc-26          JC8.10c.2     protein_coding
    ## 6           unc-26            JC8.10d     protein_coding

# 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 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"

### 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 `filterOptions()` function to retrieve the predetermed values of the respective filter.

    filterOptions("biotype",ensembl)

    ## [1] "[3prime_overlapping_ncRNA,antisense_RNA,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)

## 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()` 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_page

We now get a short list of attributes related to the region where the genes are located.

# 8 Local BioMart databases

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.

# 9 Using `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           202763_at        836
    ## 2         209310_s_at        837
    ## 3           207500_at        838

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 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.

# 10 Session Info

    sessionInfo()

    ## R version 3.4.3 (2017-11-30)
    ## Platform: x86_64-pc-linux-gnu (64-bit)
    ## Running under: Ubuntu 16.04.3 LTS
    ##
    ## Matrix products: default
    ## BLAS: /home/biocbuild/bbs-3.6-bioc/R/lib/libRblas.so
    ## LAPACK: /home/biocbuild/bbs-3.6-bioc/R/lib/libRlapack.so
    ##
    ## locale:
    ##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8        LC_COLLATE=C
    ##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8    LC_PAPER=en_US.UTF-8       LC_NAME=C
    ##  [9] LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
    ##
    ## attached base packages:
    ## [1] stats     graphics  grDevices utils     datasets  methods   base
    ##
    ## other attached packages:
    ## [1] biomaRt_2.34.1  BiocStyle_2.6.1
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] Rcpp_0.12.14         AnnotationDbi_1.40.0 knitr_1.17           magrittr_1.5         progress_1.1.2
    ##  [6] IRanges_2.12.0       BiocGenerics_0.24.0  bit_1.1-12           R6_2.2.2             rlang_0.1.4
    ## [11] httr_1.3.1           stringr_1.2.0        blob_1.1.0           tools_3.4.3          parallel_3.4.3
    ## [16] Biobase_2.38.0       DBI_0.7              htmltools_0.3.6      assertthat_0.2.0     yaml_2.1.16
    ## [21] bit64_0.9-7          rprojroot_1.2        digest_0.6.13        tibble_1.3.4         bookdown_0.5
    ## [26] S4Vectors_0.16.0     bitops_1.0-6         curl_3.1             RCurl_1.95-4.8       memoise_1.1.0
    ## [31] evaluate_0.10.1      RSQLite_2.0          rmarkdown_1.8        stringi_1.1.6        compiler_3.4.3
    ## [36] prettyunits_1.0.2    backports_1.1.2      stats4_3.4.3         XML_3.98-1.9

    warnings()

    ## NULL