--- title: "Functional Annotation with biomartr" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Functional Annotation with biomartr} %\VignetteEngine{knitr::rmarkdown} %\usepackage[utf8]{inputenc} --- ```{r, echo = FALSE, message = FALSE} options(width = 750) knitr::opts_chunk$set( comment = "#>", error = FALSE, tidy = FALSE) ``` ## Functional Annotation Retrieval from `Ensembl Biomart` > **_NOTE:_** To make sure that you have a sufficiently stable (internet) connection between R and the respective databases, please set the default `timeout` setting __on your local machine__ from 60sec to at least 30000sec before running any retrieval functions via: ```r options(timeout = 30000) ``` ### Getting Started The `Ensembl Biomart` database enables users to retrieve a vast diversity of annotation data for specific organisms. Initially, Steffen Durinck and Wolfgang Huber provided a powerful interface between the R language and `Ensembl Biomart` by implementing the R package [biomaRt](https://www.bioconductor.org/packages/release/bioc/html/biomaRt.html). The purpose of the `biomaRt` package was to mimic the ENSEMBL BioMart database structure to construct queries that can be sent to the Application Programming Interface (API) of BioMart. Although, this procedure was very useful in the past, it seems not intuitive from an organism centric point of view. Usually, users wish to download functional annotation for a particular organism of interest. However, the BioMart and thus the `biomaRt` package require that users already know in which `mart` and `dataset` the organism of interest will be found which requires significant efforts of searching and screening. In addition, once the `mart` and `dataset` of a particular organism of interest were found and specified the user must again learn which `attribute` has to be specified to retrieve the functional annotation information of interest. The new functionality implemented in the `biomartr` package aims to overcome this search bottleneck by extending the functionality of the [biomaRt](https://www.bioconductor.org/packages/release/bioc/html/biomaRt.html) package. The new `biomartr` package introduces a more organism cantered annotation retrieval concept which does not require to screen for `marts`, `datasets`, and `attributes` beforehand. With `biomartr` users only need to specify the `scientific name` of the organism of interest to then retrieve available `marts`, `datasets`, and `attributes` for the corresponding organism of interest. This paradigm shift enables users to quickly construct queries to the BioMart database without having to learn the particular database structure and organization of BioMart. The following sections will introduce users to the functionality and data retrieval precedures of `biomartr` and will show how `biomartr` extends the functionality of the initial [biomaRt](https://www.bioconductor.org/packages/release/bioc/html/biomaRt.html) package. ### The old `biomaRt` query methodology The best way to get started with the _old_ methodology presented by the established [biomaRt](https://www.bioconductor.org/packages/release/bioc/html/biomaRt.html) package is to understand the workflow of its data retrieval process. The query logic of the `biomaRt` package derives from the database organization of `Ensembl Biomart` which stores a vast diversity of annotation data for specific organisms. In detail, the `Ensembl Biomart` database is organized into so called: `marts`, `datasets`, and `attributes`. `Marts` denote a higher level category of functional annotation such as `SNP` (e.g. for functional annotation of particular single nucleotide polymorphisms (SNPs)) or `FUNCGEN` (e.g. for functional annotation of regulatory regions or relationsships of genes). `Datasets` denote the particular species of interest for which functional annotation is available __within__ this specific `mart`. It can happen that `datasets` (= particular species of interest) are available in one `mart` (= higher category of functional annotation) but not in an other `mart`. For the actual retrieval of functional annotation information users must then specify the `type` of functional annotation information they wish to retrieve. These `types` are called `attributes` in the `biomaRt` notation. Hence, when users wish to retrieve information for a specific organism of interest, they first need to specify a particular `mart` and `dataset` in which the information of the corresponding organism of interest can be found. Subsequently they can specify the `attributes` argument to retrieve a particular type of functional annotation (e.g. Gene Ontology terms). The following section shall illustrate how `marts`, `datasets`, and `attributes` could be explored using `biomaRt` before the `biomartr` package existed. The availability of `marts`, `datasets`, and `attributes` can be checked by the following functions: ```{r,eval=FALSE} # install the biomaRt package # source("https://bioconductor.org/biocLite.R") # biocLite("biomaRt") # load biomaRt library(biomaRt) # look at top 10 databases head(biomaRt::listMarts(host = "https://www.ensembl.org"), 10) ``` Users will observe that several `marts` providing annotation for specific classes of organisms or groups of organisms are available. For our example, we will choose the `hsapiens_gene_ensembl` `mart` and list all available datasets that are element of this `mart`. ```{r,eval=FALSE} head(biomaRt::listDatasets(biomaRt::useMart("ENSEMBL_MART_ENSEMBL", host = "https://www.ensembl.org")), 10) ``` The `useMart()` function is a wrapper function provided by `biomaRt` to connect a selected BioMart database (`mart`) with a corresponding dataset stored within this `mart`. We select dataset `hsapiens_gene_ensembl` and now check for available attributes (annotation data) that can be accessed for `Homo sapiens` genes. ```{r,eval=FALSE} head(biomaRt::listAttributes(biomaRt::useDataset( dataset = "hsapiens_gene_ensembl", mart = useMart("ENSEMBL_MART_ENSEMBL", host = "https://www.ensembl.org"))), 10) ``` Please note the nested structure of this attribute query. For an attribute query procedure an additional wrapper function named `useDataset()` is needed in which `useMart()` and a corresponding dataset needs to be specified. The result is a table storing the name of available attributes for _Homo sapiens_ as well as a short description. Furthermore, users can retrieve all filters for _Homo sapiens_ that can be specified by the actual BioMart query process. ```{r,eval=FALSE} head(biomaRt::listFilters(biomaRt::useDataset(dataset = "hsapiens_gene_ensembl", mart = useMart("ENSEMBL_MART_ENSEMBL", host = "https://www.ensembl.org"))), 10) ``` After accumulating all this information, it is now possible to perform an actual BioMart query by using the `getBM()` function. In this example we will retrieve attributes: `start_position`,`end_position` and `description` for the _Homo sapiens_ gene `"GUCA2A"`. Since the input genes are `ensembl gene ids`, we need to specify the `filters` argument `filters = "hgnc_symbol"`. ```{r,eval=FALSE} # 1) select a mart and data set mart <- biomaRt::useDataset(dataset = "hsapiens_gene_ensembl", mart = useMart("ENSEMBL_MART_ENSEMBL", host = "https://www.ensembl.org")) # 2) run a biomart query using the getBM() function # and specify the attributes and filter arguments geneSet <- "GUCA2A" resultTable <- biomaRt::getBM(attributes = c("start_position","end_position","description"), filters = "hgnc_symbol", values = geneSet, mart = mart) resultTable ``` When using `getBM()` users can pass all attributes retrieved by `listAttributes()` to the `attributes` argument of the `getBM()` function. ## Extending `biomaRt` using the new query system of the `biomartr` package ### Getting Started with `biomartr` This query methodology provided by `Ensembl Biomart` and the `biomaRt` package is a very well defined approach for accurate annotation retrieval. Nevertheless, when learning this query methodology it (subjectively) seems non-intuitive from the user perspective. Therefore, the `biomartr` package provides another query methodology that aims to be more organism centric. Taken together, the following workflow allows users to perform fast BioMart queries for attributes using the `biomart()` function implemented in this `biomartr` package: 1) get attributes, datasets, and marts via : `organismAttributes()` 2) choose available biological features (filters) via: `organismFilters()` 3) specify a set of query genes: e.g. retrieved with `getGenome()`, `getProteome()` or `getCDS()` 4) specify all arguments of the `biomart()` function using steps 1) - 3) and perform a BioMart query __Note that dataset names change very frequently due to the update of dataset versions. So in case some query functions do not work properly, users should check with `organismAttributes(update = TRUE)` whether or not their dataset name has been changed. For example, `organismAttributes("Homo sapiens", topic = "id", update = TRUE)` might reveal that the dataset `ENSEMBL_MART_ENSEMBL` has changed.__ ## Retrieve marts, datasets, attributes, and filters with biomartr ### Retrieve Available Marts The `getMarts()` function allows users to list all available databases that can be accessed through BioMart interfaces. ```{r,eval=FALSE} # load the biomartr package library(biomartr) # list all available databases biomartr::getMarts() ``` ``` mart version 1 ENSEMBL_MART_ENSEMBL Ensembl Genes 104 2 ENSEMBL_MART_MOUSE Mouse strains 104 3 ENSEMBL_MART_SEQUENCE Sequence 4 ENSEMBL_MART_ONTOLOGY Ontology 5 ENSEMBL_MART_GENOMIC Genomic features 104 6 ENSEMBL_MART_SNP Ensembl Variation 104 7 ENSEMBL_MART_FUNCGEN Ensembl Regulation 104 8 plants_mart Ensembl Plants Genes 51 9 plants_variations Ensembl Plants Variations 51 10 fungi_mart Ensembl Fungi Genes 51 11 fungi_variations Ensembl Fungi Variations 51 12 protists_mart Ensembl Protists Genes 51 13 protists_variations Ensembl Protists Variations 51 14 metazoa_mart Ensembl Metazoa Genes 51 15 metazoa_variations Ensembl Metazoa Variations 51 ``` ### Retrieve Available Datasets from a Specific Mart Now users can select a specific database to list all available data sets that can be accessed through this database. In this example we choose the `ENSEMBL_MART_ENSEMBL` database. ```{r,eval=FALSE} head(biomartr::getDatasets(mart = "ENSEMBL_MART_ENSEMBL") , 5) ``` ``` dataset description version 1 fcatus_gene_ensembl Cat genes (Felis_catus_9.0) Felis_catus 2 umaritimus_gene_ensembl Polar bear genes (UrsMar_1.0) UrsMar_1.0 3 ogarnettii_gene_ensembl Bushbaby genes (OtoGar3) OtoGar3 4 lcrocea_gene_ensembl Large yellow croaker genes (L_cro L_crocea_2.0 5 sformosus_gene_ensembl Asian bonytongue genes (fSclFor1. fSclFor1.1 ``` Now you can select the dataset `hsapiens_gene_ensembl` and list all available attributes that can be retrieved from this dataset. ```{r,eval=FALSE} tail(biomartr::getDatasets(mart = "ENSEMBL_MART_ENSEMBL") , 38) ``` ``` 1 csabaeus_gene_ensembl Vervet-AGM genes (ChlSab1.1) ChlSab1.1 2 chircus_gene_ensembl Goat genes (ARS1) ARS1 3 mmulatta_gene_ensembl Macaque genes (Mmul_10) Mmul_10 4 mmonoceros_gene_ensembl Narwhal genes (NGI_Narwhal_1) NGI_Narwhal_1 5 csemilaevis_gene_ensembl Tongue sole genes (Cse_v1.0) Cse_v1.0 6 cpbellii_gene_ensembl Painted turtle genes (Chryse Chrysemys_pict 7 clanigera_gene_ensembl Long-tailed chinchilla genes ChiLan1.0 8 catys_gene_ensembl Sooty mangabey genes (Caty_1 Caty_1.0 9 tguttata_gene_ensembl Zebra finch genes (bTaeGut1_ bTaeGut1_v1.p 10 nleucogenys_gene_ensembl Gibbon genes (Nleu_3.0) Nleu_3.0 # with 28 more rows ``` ### Retrieve Available Attributes from a Specific Dataset Now that you have selected a database (`hsapiens_gene_ensembl`) and a dataset (`hsapiens_gene_ensembl`), users can list all available attributes for this dataset using the `getAttributes()` function. ```{r,eval=FALSE} # show all elements of the data.frame options(tibble.print_max = Inf) # list all available attributes for dataset: hsapiens_gene_ensembl head( biomartr::getAttributes(mart = "ENSEMBL_MART_ENSEMBL", dataset = "hsapiens_gene_ensembl"), 10 ) ``` ``` Starting retrieval of attribute information from mart ENSEMBL_MART_ENSEMBL and dataset hsapiens_gene_ensembl ... name description 1 ensembl_gene_id Gene stable ID 2 ensembl_gene_id_version Gene stable ID version 3 ensembl_transcript_id Transcript stable ID 4 ensembl_transcript_id_version Transcript stable ID version 5 ensembl_peptide_id Protein stable ID 6 ensembl_peptide_id_version Protein stable ID version 7 ensembl_exon_id Exon stable ID 8 description Gene description 9 chromosome_name Chromosome/scaffold name 10 start_position Gene start (bp) ``` ### Retrieve Available Filters from a Specific Dataset Finally, the `getFilters()` function allows users to list available filters for a specific dataset that can be used for a `biomart()` query. ```{r,eval=FALSE} # show all elements of the data.frame options(tibble.print_max = Inf) # list all available filters for dataset: hsapiens_gene_ensembl head( biomartr::getFilters(mart = "ENSEMBL_MART_ENSEMBL", dataset = "hsapiens_gene_ensembl"), 10 ) ``` ``` Starting retrieval of filters information from mart ENSEMBL_MART_ENSEMBL and dataset hsapiens_gene_ensembl ... name description 1 chromosome_name Chromosome/scaffold name 2 start Start 3 end End 4 band_start Band Start 5 band_end Band End 6 marker_start Marker Start 7 marker_end Marker End 8 encode_region Encode region 9 strand Strand 10 chromosomal_region e.g. 1:100:10000:-1, 1:100000:200000:1 ``` ## Organism Specific Retrieval of Information In most use cases, users will work with a single or a set of model organisms. In this process they will mostly be interested in specific annotations for this particular model organism. The `organismBM()` function addresses this issue and provides users with an organism centric query to `marts` and `datasets` which are available for a particular organism of interest. __Note__ that when running the following functions for the first time, the data retrieval procedure will take some time, due to the remote access to BioMart. The corresponding result is then saved in a `*.txt` file named `_biomart/listDatasets.txt` within the `tempdir()` folder, allowing subsequent queries to be performed much faster. The `tempdir()` folder, however, will be deleted after a new R session was established. In this case the inital call of the subsequent functions again will take time to retrieve all organism specific data from the BioMart database. This concept of locally storing all organism specific database linking information available in BioMart into an internal file allows users to significantly speed up subsequent retrieval queries for that particular organism. ```{r,eval=FALSE} # show all elements of the data.frame options(tibble.print_max = Inf) # retrieving all available datasets and biomart connections for # a specific query organism (scientific name) biomartr::organismBM(organism = "Homo sapiens") ``` ``` Starting retrieval of all available BioMart datasets for Homo sapiens ... Datasets for the following marts will be retrieved: mart version 1 ENSEMBL_MART_ENSEMBL Ensembl Genes 104 2 ENSEMBL_MART_MOUSE Mouse strains 104 3 ENSEMBL_MART_SEQUENCE Sequence 4 ENSEMBL_MART_ONTOLOGY Ontology 5 ENSEMBL_MART_GENOMIC Genomic features 104 6 ENSEMBL_MART_SNP Ensembl Variation 104 7 ENSEMBL_MART_FUNCGEN Ensembl Regulation 104 8 plants_mart Ensembl Plants Genes 51 9 plants_variations Ensembl Plants Variations 51 10 fungi_mart Ensembl Fungi Genes 51 11 fungi_variations Ensembl Fungi Variations 51 12 protists_mart Ensembl Protists Genes 51 13 protists_variations Ensembl Protists Variations 51 14 metazoa_mart Ensembl Metazoa Genes 51 Processing mart ENSEMBL_MART_ENSEMBL ... Processing mart ENSEMBL_MART_MOUSE ... Processing mart ENSEMBL_MART_SEQUENCE ... Processing mart ENSEMBL_MART_ONTOLOGY ... Processing mart ENSEMBL_MART_GENOMIC ... Processing mart ENSEMBL_MART_SNP ... Processing mart ENSEMBL_MART_FUNCGEN ... Processing mart plants_mart ... Processing mart plants_variations ... Processing mart fungi_mart ... Processing mart fungi_variations ... Processing mart protists_mart ... Processing mart protists_variations ... Processing mart metazoa_mart ... A tibble: 15 x 5 organism_name description mart dataset version 1 hsapiens Human genes (GRCh38.p13) ENSEMBL_ hsapiens_ge GRCh38 2 hsapiens Human sequences (GRCh38. ENSEMBL_ hsapiens_ge GRCh38 3 hsapiens encode ENSEMBL_ hsapiens_en GRCh38 4 hsapiens marker_feature_end ENSEMBL_ hsapiens_ma GRCh38 5 hsapiens marker_feature ENSEMBL_ hsapiens_ma GRCh38 6 hsapiens karyotype_end ENSEMBL_ hsapiens_ka GRCh38 7 hsapiens karyotype_start ENSEMBL_ hsapiens_ka GRCh38 8 hsapiens Human Somatic Short Vari ENSEMBL_ hsapiens_sn GRCh38 9 hsapiens Human Structural Variant ENSEMBL_ hsapiens_st GRCh38 10 hsapiens Human Short Variants (SN ENSEMBL_ hsapiens_snp GRCh38 11 hsapiens Human Somatic Structural ENSEMBL_ hsapiens_st GRCh38 12 hsapiens Human Regulatory Evidenc ENSEMBL_ hsapiens_pe GRCh38 13 hsapiens Human Regulatory Feature ENSEMBL_ hsapiens_re GRCh38 14 hsapiens Human Other Regulatory R ENSEMBL_ hsapiens_ex GRCh38 15 hsapiens Human miRNA Target Regio ENSEMBL_ hsapiens_mi GRCh38 ``` The result is a table storing all `marts` and `datasets` from which annotations can be retrieved for _Homo sapiens_. Furthermore, a short description as well as the version of the data set being accessed (very useful for publications) is returned. Users will observe that 3 different `marts` provide 6 different `datasets` storing annotation information for _Homo sapiens_. > **_Please note__*, however, that scientific names of organisms must be written correctly! For ex. "Homo Sapiens" will be treated differently (not recognized) than "Homo sapiens" (recognized).__ Similar to the `biomaRt` package query methodology, users need to specify `attributes` and `filters` to be able to perform accurate BioMart queries. Here the functions `organismAttributes()` and `organismFilters()` provide useful and intuitive concepts to obtain this information. ```{r,eval=FALSE} # show all elements of the data.frame options(tibble.print_max = Inf) # return available attributes for "Homo sapiens" head(biomartr::organismAttributes("Homo sapiens"), 20) ``` ``` 1 ensembl_gene_id Gene stable ID hsapiens_ge ENSEMBL_M 2 ensembl_gene_id_version Gene stable ID version hsapiens_ge ENSEMBL_M 3 ensembl_transcript_id Transcript stable ID hsapiens_ge ENSEMBL_M 4 ensembl_transcript_id_version Transcript stable ID hsapiens_ge ENSEMBL_M 5 ensembl_peptide_id Protein stable ID hsapiens_ge ENSEMBL_M 6 ensembl_peptide_id_version Protein stable ID ver hsapiens_ge ENSEMBL_M 7 ensembl_exon_id Exon stable ID hsapiens_ge ENSEMBL_M 8 description Gene description hsapiens_ge ENSEMBL_M 9 chromosome_name Chromosome/scaffold n hsapiens_ge ENSEMBL_M 10 start_position Gene start (bp) hsapiens_ge ENSEMBL_M 11 end_position Gene end (bp) hsapiens_ge ENSEMBL_M 12 strand Strand hsapiens_ge ENSEMBL_M 13 band Karyotype band hsapiens_ge ENSEMBL_M 14 transcript_start Transcript start (bp) hsapiens_ge ENSEMBL_M 15 transcript_end Transcript end (bp) hsapiens_ge ENSEMBL_M 16 transcription_start_site Transcription start s hsapiens_ge ENSEMBL_M 17 transcript_length Transcript length (in hsapiens_ge ENSEMBL_M 18 transcript_tsl Transcript support le hsapiens_ge ENSEMBL_M 19 transcript_gencode_basic GENCODE basic annotat hsapiens_ge ENSEMBL_M 20 transcript_appris APPRIS annotation hsapiens_ge ENSEMBL_M ``` Users will observe that the `organismAttributes()` function returns a data.frame storing attribute names, data sets, and marts which are available for `Homo sapiens`. After the ENSEMBL release 87 the `ENSEMBL_MART_SEQUENCE` service provided by Ensembl does not work properly and thus the `organismAttributes()` function prints out warning messages to make the user aware when certain marts provided by Ensembl do not work properly, yet. An additional feature provided by `organismAttributes()` is the `topic` argument. The `topic` argument allows users to to search for specific attributes, topics, or categories for faster filtering. ```{r,eval=FALSE} # show all elements of the data.frame options(tibble.print_max = Inf) # search for attribute topic "id" head(biomartr::organismAttributes("Homo sapiens", topic = "id"), 20) ``` ``` name description dataset mart 1 ensembl_gene_id Gene stable ID hsapiens_ge ENSEMBL_M 2 ensembl_gene_id_version Gene stable ID version hsapiens_ge ENSEMBL_M 3 ensembl_transcript_id Transcript stable ID hsapiens_ge ENSEMBL_M 4 ensembl_transcript_id_version Transcript stable ID hsapiens_ge ENSEMBL_M 5 ensembl_peptide_id Protein stable ID hsapiens_ge ENSEMBL_M 6 ensembl_peptide_id_version Protein stable ID ver hsapiens_ge ENSEMBL_M 7 ensembl_exon_id Exon stable ID hsapiens_ge ENSEMBL_M 8 study_external_id Study external refere hsapiens_ge ENSEMBL_M 9 go_id GO term accession hsapiens_ge ENSEMBL_M 10 dbass3_id DataBase of Aberrant hsapiens_ge ENSEMBL_M 11 dbass5_id DataBase of Aberrant hsapiens_ge ENSEMBL_M 12 hgnc_id HGNC ID hsapiens_ge ENSEMBL_M 13 protein_id INSDC protein ID hsapiens_ge ENSEMBL_M 14 mim_morbid_description MIM morbid description hsapiens_ge ENSEMBL_M 15 mim_morbid_accession MIM morbid accession hsapiens_ge ENSEMBL_M 16 mirbase_id miRBase ID hsapiens_ge ENSEMBL_M 17 refseq_peptide RefSeq peptide ID hsapiens_ge ENSEMBL_M 18 refseq_peptide_predicted RefSeq peptide predic hsapiens_ge ENSEMBL_M 19 wikigene_id WikiGene ID hsapiens_ge ENSEMBL_M 20 mobidblite MobiDBLite hsapiens_ge ENSEMBL_M ``` Now, all `attribute names` having `id` as part of their `name` are being returned. Another example is `topic = "homolog"`. ```{r,eval=FALSE} # show all elements of the data.frame options(tibble.print_max = Inf) # search for attribute topic "homolog" head(biomartr::organismAttributes("Homo sapiens", topic = "homolog"), 20) ``` ``` 1 mspretus_homolog_ensembl_gene Algerian mouse hsapie ENSEM 2 mspretus_homolog_associated_gene_name Algerian mouse hsapie ENSEM 3 mspretus_homolog_ensembl_peptide Algerian mouse hsapie ENSEM 4 mspretus_homolog_chromosome Algerian mouse hsapie ENSEM 5 mspretus_homolog_chrom_start Algerian mouse hsapie ENSEM 6 mspretus_homolog_chrom_end Algerian mouse hsapie ENSEM 7 mspretus_homolog_canonical_transcript_protein Query protein hsapie ENSEM 8 mspretus_homolog_subtype Last common an hsapie ENSEM 9 mspretus_homolog_orthology_type Algerian mouse hsapie ENSEM 10 mspretus_homolog_perc_id %id. target Al hsapie ENSEM 11 mspretus_homolog_perc_id_r1 %id. query gen hsapie ENSEM 12 mspretus_homolog_goc_score Algerian mouse hsapie ENSEM 13 mspretus_homolog_wga_coverage Algerian mouse hsapie ENSEM 14 mspretus_homolog_dn dN with Algeri hsapie ENSEM 15 mspretus_homolog_ds dS with Algeri hsapie ENSEM 16 mspretus_homolog_orthology_confidence Algerian mouse hsapie ENSEM 17 vpacos_homolog_ensembl_gene Alpaca gene st hsapie ENSEM 18 vpacos_homolog_associated_gene_name Alpaca gene na hsapie ENSEM 19 vpacos_homolog_ensembl_peptide Alpaca protein hsapie ENSEM 20 vpacos_homolog_chromosome Alpaca chromos hsapie ENSEM ``` Or `topic = "dn"` and `topic = "ds"` for `dn` and `ds` value retrieval. ```{r,eval=FALSE} # show all elements of the data.frame options(tibble.print_max = Inf) # search for attribute topic "dn" head(biomartr::organismAttributes("Homo sapiens", topic = "dn")) ``` ``` name description dataset mart 1 cdna_coding_start cDNA coding start hsapiens_gene_ensembl ENSEMBL_M 2 cdna_coding_end cDNA coding end hsapiens_gene_ensembl ENSEMBL_M 3 mspretus_homolog_dn dN with Algerian mouse hsapiens_gene_ensembl ENSEMBL_M 4 vpacos_homolog_dn dN with Alpaca hsapiens_gene_ensembl ENSEMBL_M 5 pformosa_homolog_dn dN with Amazon molly hsapiens_gene_ensembl ENSEMBL_M 6 cpalliatus_homolog_dn dN with Angola colobus hsapiens_gene_ensembl ENSEMBL_M ``` ```{r,eval=FALSE} # show all elements of the data.frame options(tibble.print_max = Inf) # search for attribute topic "ds" head(biomartr::organismAttributes("Homo sapiens", topic = "ds")) ``` ``` name description dataset mart 1 ccds CCDS ID hsapiens_gene_ensembl ENSEMBL_MAR 2 cds_length CDS Length hsapiens_gene_ensembl ENSEMBL_MAR 3 cds_start CDS start hsapiens_gene_ensembl ENSEMBL_MAR 4 cds_end CDS end hsapiens_gene_ensembl ENSEMBL_MAR 5 mspretus_homolog_ds dS with Algerian mouse hsapiens_gene_ensembl ENSEMBL_MAR 6 vpacos_homolog_ds dS with Alpaca hsapiens_gene_ensembl ENSEMBL_MAR ``` Analogous to the `organismAttributes()` function, the `organismFilters()` function returns all filters that are available for a query organism of interest. ```{r,eval=FALSE} # show all elements of the data.frame options(tibble.print_max = Inf) # return available filters for "Homo sapiens" head(biomartr::organismFilters("Homo sapiens"), 20) ``` ``` name description dataset mart 1 chromosome_name Chromosome/scaffold hsapiens_ ENSEMBL 2 start Start hsapiens_ ENSEMBL 3 end End hsapiens_ ENSEMBL 4 band_start Band Start hsapiens_ ENSEMBL 5 band_end Band End hsapiens_ ENSEMBL 6 marker_start Marker Start hsapiens_ ENSEMBL 7 marker_end Marker End hsapiens_ ENSEMBL 8 encode_region Encode region hsapiens_ ENSEMBL 9 strand Strand hsapiens_ ENSEMBL 10 chromosomal_region e.g. 1:100:10000:-1 hsapiens_ ENSEMBL 11 with_ccds With CCDS ID(s) hsapiens_ ENSEMBL 12 with_chembl With ChEMBL ID(s) hsapiens_ ENSEMBL 13 with_clone_based_ensembl_gene With Clone-based (E hsapiens_ ENSEMBL 14 with_clone_based_ensembl_transcript With Clone-based (E hsapiens_ ENSEMBL 15 with_dbass3 With DataBase of Ab hsapiens_ ENSEMBL 16 with_dbass5 With DataBase of Ab hsapiens_ ENSEMBL 17 with_ens_hs_transcript With Ensembl Human hsapiens_ ENSEMBL 18 with_ens_hs_translation With Ensembl Human hsapiens_ ENSEMBL 19 with_entrezgene_trans_name With EntrezGene tra hsapiens_ ENSEMBL 20 with_embl With European Nucle hsapiens_ ENSEMBL ``` The `organismFilters()` function also allows users to search for filters that correspond to a specific topic or category. ```{r,eval=FALSE} # show all elements of the data.frame options(tibble.print_max = Inf) # search for filter topic "id" head(biomartr::organismFilters("Homo sapiens", topic = "id"), 20) ``` ``` name description dataset mart 1 with_protein_id With INSDC protein ID ID hsapiens_g ENSEMBL 2 with_mim_morbid With MIM morbid ID(s) hsapiens_g ENSEMBL 3 with_refseq_peptide With RefSeq peptide ID(s) hsapiens_g ENSEMBL 4 with_refseq_peptide_predicted With RefSeq peptide pred hsapiens_g ENSEMBL 5 ensembl_gene_id Gene stable ID(s) [e.g. hsapiens_g ENSEMBL 6 ensembl_gene_id_version Gene stable ID(s) with v hsapiens_g ENSEMBL 7 ensembl_transcript_id Transcript stable ID(s) hsapiens_g ENSEMBL 8 ensembl_transcript_id_version Transcript stable ID(s) hsapiens_g ENSEMBL 9 ensembl_peptide_id Protein stable ID(s) [e. hsapiens_g ENSEMBL 10 ensembl_peptide_id_version Protein stable ID(s) wit hsapiens_g ENSEMBL 11 ensembl_exon_id Exon ID(s) [e.g. ENSE000 hsapiens_g ENSEMBL 12 dbass3_id DataBase of Aberrant 3' hsapiens_g ENSEMBL 13 dbass5_id DataBase of Aberrant 5' hsapiens_g ENSEMBL 14 hgnc_id HGNC ID(s) [e.g. HGNC:10 hsapiens_g ENSEMBL 15 protein_id INSDC protein ID(s) [e.g hsapiens_g ENSEMBL 16 mim_morbid_accession MIM morbid accession(s) hsapiens_g ENSEMBL 17 mirbase_id miRBase ID(s) [e.g. hsa- hsapiens_g ENSEMBL 18 refseq_peptide RefSeq peptide ID(s) [e. hsapiens_g ENSEMBL 19 refseq_peptide_predicted RefSeq peptide predicted hsapiens_g ENSEMBL 20 wikigene_id WikiGene ID(s) [e.g. 1] hsapiens_g ENSEMBL ``` ## Construct BioMart queries with `biomartr` The short introduction to the functionality of `organismBM()`, `organismAttributes()`, and `organismFilters()` will allow users to perform BioMart queries in a very intuitive organism centric way. The main function to perform BioMart queries is `biomart()`. For the following examples we will assume that we are interested in the annotation of specific genes from the _Homo sapiens_ proteome. We want to map the corresponding refseq gene id to a set of other gene ids used in other databases. For this purpose, first we need consult the `organismAttributes()` function. ```{r,eval=FALSE} # show all elements of the data.frame options(tibble.print_max = Inf) head(biomartr::organismAttributes("Homo sapiens", topic = "id")) ``` ``` name description dataset mart 1 ensembl_gene_id Gene stable ID hsapiens_ ENSEMB 2 ensembl_gene_id_version Gene stable ID version hsapiens_ ENSEMB 3 ensembl_transcript_id Transcript stable ID hsapiens_ ENSEMB 4 ensembl_transcript_id_version Transcript stable ID version hsapiens_ ENSEMB 5 ensembl_peptide_id Protein stable ID hsapiens_ ENSEMB 6 ensembl_peptide_id_version Protein stable ID version hsapiens_ ENSEMB ``` ```{r,eval=FALSE} # show all elements of the data.frame options(tibble.print_max = Inf) # retrieve the proteome of Homo sapiens from refseq file_path <- biomartr::getProteome( db = "refseq", organism = "Homo sapiens", path = file.path("_ncbi_downloads","proteomes") ) Hsapiens_proteome <- biomartr::read_proteome(file_path, format = "fasta") # remove splice variants from id gene_set <- unlist(sapply(strsplit(Hsapiens_proteome@ranges@NAMES[1:5], ".",fixed = TRUE), function(x) x[1])) result_BM <- biomartr::biomart( genes = gene_set, # genes were retrieved using biomartr::getGenome() mart = "ENSEMBL_MART_ENSEMBL", # marts were selected with biomartr::getMarts() dataset = "hsapiens_gene_ensembl", # datasets were selected with biomartr::getDatasets() attributes = c("ensembl_gene_id","ensembl_peptide_id"), # attributes were selected with biomartr::getAttributes() filters = "refseq_peptide") # specify what ID type was stored in the fasta file retrieved with biomartr::getGenome() result_BM ``` ``` refseq_peptide ensembl_gene_id ensembl_peptide_id 1 NP_000005 ENSG00000175899 ENSP00000323929 2 NP_000006 ENSG00000156006 ENSP00000286479 3 NP_000007 ENSG00000117054 ENSP00000359878 4 NP_000008 ENSG00000122971 ENSP00000242592 5 NP_000009 ENSG00000072778 ENSP00000349297 ``` The `biomart()` function takes as arguments a set of genes (gene ids specified in the `filter` argument), the corresponding `mart` and `dataset`, as well as the `attributes` which shall be returned. ## Gene Ontology The `biomartr` package also enables a fast and intuitive retrieval of GO terms and additional information via the `getGO()` function. Several databases can be selected to retrieve GO annotation information for a set of query genes. So far, the `getGO()` function allows GO information retrieval from the `Ensembl Biomart` database. In this example we will retrieve GO information for a set of _Homo sapiens_ genes stored as `hgnc_symbol`. ### GO Annotation Retrieval via BioMart The `getGO()` function takes several arguments as input to retrieve GO information from BioMart. First, the scientific name of the `organism` of interest needs to be specified. Furthermore, a set of `gene ids` as well as their corresponding `filter` notation (`GUCA2A` gene ids have `filter` notation `hgnc_symbol`; see `organismFilters()` for details) need to be specified. The `database` argument then defines the database from which GO information shall be retrieved. ```{r,eval=FALSE} # show all elements of the data.frame options(tibble.print_max = Inf) # search for GO terms of an example Homo sapiens gene GO_tbl <- biomartr::getGO(organism = "Homo sapiens", genes = "GUCA2A", filters = "hgnc_symbol") GO_tbl ``` Hence, for each _gene id_ the resulting table stores all annotated GO terms found in `Ensembl Biomart`.