Genomic Annotation Resources
24 April 2018
Contents
1 Version Info
R version: R version 4.1.3 (2022-03-10)
Bioconductor version: 3.14
Package version: 1.18.1
2 Introduction
Annotation resources make up a significant proportion of the Bioconductor project[1]. And there are also a diverse set of online resources available which are accessed using specific packages. This walkthrough will describe the most popular of these resources and give some high level examples on how to use them.
Bioconductor annotation resources have traditionally been used near the end of an analysis. After the bulk of the data analysis, annotations would be used interpretatively to learn about the most significant results. But increasingly, they are also used as a starting point or even as an intermediate step to help guide a study that is still in progress. In addition to this, what it means for something to be an annotation is also becoming less clear than it once was. It used to be clear that annotations were only those things that had been established after multiple different studies had been performed (such as the primary role of a gene product). But today many large data sets are treated by communities in much the same way that classic annotations once were: as a reference for additional comparisons.
Another change that is underway with annotations in Bioconductor is in the way that they are obtained. In the past annotations existed almost exclusively as separate annotation packages[2,3,4]. Today packages are still an enormous source of annotations. The current release repository contains over eight hundred annotation packages. This table summarizes some of the more important classes of annotation objects that are often accessed using packages:
| Object Type | Example Package Name | Contents |
|---|---|---|
| TxDb |
TxDb.Hsapiens.UCSC.hg19.knownGene
|
Transcriptome ranges for the known gene track of Homo sapiens, e.g., introns, exons, UTR regions. |
| OrgDb |
org.Hs.eg.db
|
Gene-based information for Homo sapiens; useful for mapping between gene IDs, Names, Symbols, GO and KEGG identifiers, etc. |
| BSgenome |
BSgenome.Hsapiens.UCSC.hg19
|
Full genome sequence for Homo sapiens. |
| Organism.dplyr |
src_organism
|
Collection of multiple annotations for a common organism and genome build. |
| AnnotationHub |
AnnotationHub
|
Provides a convenient interface to annotations from many different sources; objects are returned as fully parsed Bioconductor data objects or as the name of a file on disk. |
But in spite of the popularity of annotation packages, annotations are increasingly also being pulled down from web services like biomaRt[5,6,7] or from the AnnotationHub[8]. And both of these represent enormous resources for annotation data.
In part because of the rapidly evolving landscape, it is currently impossible in a single document to cover every possible annotation or even every kind of annotation present in Bioconductor. So here we will instead go over the most popular annotation resources and describe them in a way intended to expose common patterns used for accessing them. The hope is that a user with this information will be able to make educated guesses about how to find and use additional resources that will inevitably be added later. Topics that will be covered will include the following:
3 Set Up
In this chapter we make use of several Bioconductor packages. You can install
them with BiocManager::install():
if (!"BiocManager" %in% rownames(installed.packages()))
install.packages("BiocManager")
BiocManager::install(c("AnnotationHub", "Homo.sapiens",
"Organism.dplyr",
"TxDb.Hsapiens.UCSC.hg19.knownGene",
"TxDb.Hsapiens.UCSC.hg38.knownGene",
"BSgenome.Hsapiens.UCSC.hg19", "biomaRt",
"TxDb.Athaliana.BioMart.plantsmart22"))The usage of the installed packages will be described in detail within the Usage section.
4 Using AnnotationHub
The top of the list for learning about annotation resources is the relatively new AnnotationHub package[8]. The AnnotationHub was created to provide a convenient access point for end users to find a large range of different annotation objects for use with Bioconductor. Resources found in the AnnotationHub are easy to discover and are presented to the user as familiar Bioconductor data objects. Because it is a recent addition, the AnnotationHub allows access to a broad range of annotation like objects, some of which may not have been considered annotations even a few years ago. To get started with the AnnotationHub users only need to load the package and then create a local AnnotationHub object like this:
## snapshotDate(): 2021-10-20ah <- AnnotationHub()The very 1st time that you call the AnnotationHub, it will create a cache directory on your system and download the latest metadata for the hubs current contents. From that time forward, whenever you download one of the hubs data objects, it will also cache those files in the local directory so that if you request the information again, you will be able to access it quickly.
The show method of an AnnotationHub object will tell you how many resources are currently accessible using that object as well as give a high level overview of the most common kinds of data present.
ah## AnnotationHub with 62386 records
## # snapshotDate(): 2021-10-20
## # $dataprovider: Ensembl, BroadInstitute, UCSC, ftp://ftp.ncbi.nlm.nih.gov/g...
## # $species: Homo sapiens, Mus musculus, Drosophila melanogaster, Bos taurus,...
## # $rdataclass: GRanges, TwoBitFile, BigWigFile, EnsDb, Rle, OrgDb, ChainFile...
## # additional mcols(): taxonomyid, genome, description,
## # coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
## # rdatapath, sourceurl, sourcetype
## # retrieve records with, e.g., 'object[["AH5012"]]'
##
## title
## AH5012 | Chromosome Band
## AH5013 | STS Markers
## AH5014 | FISH Clones
## AH5015 | Recomb Rate
## AH5016 | ENCODE Pilot
## ... ...
## AH100299 | Zonotrichia_albicollis.Zonotrichia_albicollis-1.0.1.ncrna.2bit
## AH100300 | Zosterops_lateralis_melanops.ASM128173v1.cdna.all.2bit
## AH100301 | Zosterops_lateralis_melanops.ASM128173v1.dna_rm.toplevel.2bit
## AH100302 | Zosterops_lateralis_melanops.ASM128173v1.dna_sm.toplevel.2bit
## AH100303 | Zosterops_lateralis_melanops.ASM128173v1.ncrna.2bitAs you can see from the object above, there are a LOT of different resources available. So normally when you get an AnnotationHub object the 1st thing you want to do is to filter it to remove unwanted resources.
Fortunately, the AnnotationHub has several different kinds of metadata that you can use for searching and subsetting. To see the different categories all you need to do is to type the name of your AnnotationHub object and then tab complete from the ‘$’ operator. And to see all possible contents of one of these categories you can pass that value in to unique like this:
unique(ah$dataprovider)## [1] "UCSC"
## [2] "Ensembl"
## [3] "RefNet"
## [4] "Inparanoid8"
## [5] "NHLBI"
## [6] "ChEA"
## [7] "Pazar"
## [8] "NIH Pathway Interaction Database"
## [9] "Haemcode"
## [10] "BroadInstitute"
## [11] "PRIDE"
## [12] "Gencode"
## [13] "CRIBI"
## [14] "Genoscope"
## [15] "MISO, VAST-TOOLS, UCSC"
## [16] "UWashington"
## [17] "Stanford"
## [18] "dbSNP"
## [19] "BioMart"
## [20] "GeneOntology"
## [21] "KEGG"
## [22] "URGI"
## [23] "EMBL-EBI"
## [24] "MicrosporidiaDB"
## [25] "FungiDB"
## [26] "TriTrypDB"
## [27] "ToxoDB"
## [28] "AmoebaDB"
## [29] "PlasmoDB"
## [30] "PiroplasmaDB"
## [31] "CryptoDB"
## [32] "TrichDB"
## [33] "GiardiaDB"
## [34] "The Gene Ontology Consortium"
## [35] "ENCODE Project"
## [36] "SchistoDB"
## [37] "NCBI/UniProt"
## [38] "GENCODE"
## [39] "http://www.pantherdb.org"
## [40] "RMBase v2.0"
## [41] "snoRNAdb"
## [42] "tRNAdb"
## [43] "NCBI"
## [44] "DrugAge, DrugBank, Broad Institute"
## [45] "DrugAge"
## [46] "DrugBank"
## [47] "Broad Institute"
## [48] "HMDB, EMBL-EBI, EPA"
## [49] "STRING"
## [50] "OMA"
## [51] "OrthoDB"
## [52] "PathBank"
## [53] "EBI/EMBL"
## [54] "NCBI,DBCLS"
## [55] "FANTOM5,DLRP,IUPHAR,HPRD,STRING,SWISSPROT,TREMBL,ENSEMBL,CELLPHONEDB,BADERLAB,SINGLECELLSIGNALR,HOMOLOGENE"
## [56] "WikiPathways"
## [57] "UCSC Jaspar"
## [58] "VAST-TOOLS"
## [59] "pyGenomeTracks "
## [60] "NA"
## [61] "UoE"
## [62] "mitra.stanford.edu/kundaje/akundaje/release/blacklists/"
## [63] "ENCODE"
## [64] "ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/"One of the most valuable ways in which the data is labeled is according to the kind of R object that will be returned to you.
unique(ah$rdataclass)## [1] "GRanges" "data.frame"
## [3] "Inparanoid8Db" "TwoBitFile"
## [5] "ChainFile" "SQLiteConnection"
## [7] "biopax" "BigWigFile"
## [9] "AAStringSet" "MSnSet"
## [11] "mzRpwiz" "mzRident"
## [13] "list" "TxDb"
## [15] "Rle" "EnsDb"
## [17] "VcfFile" "igraph"
## [19] "data.frame, DNAStringSet, GRanges" "sqlite"
## [21] "data.table" "character"
## [23] "SQLite" "SQLiteFile"
## [25] "Tibble" "Rda"
## [27] "FaFile" "String"
## [29] "OrgDb"Once you have identified which sorts of metadata you would like to use to find your data of interest, you can then use the subset or query methods to reduce the size of the hub object to something more manageable. For example you could select only those records where the string ‘GRanges’ was in the metadata. As you can see GRanges are one of the more popular formats for data that comes from the AnnotationHub.
grs <- query(ah, "GRanges")
grs## AnnotationHub with 27360 records
## # snapshotDate(): 2021-10-20
## # $dataprovider: Ensembl, BroadInstitute, UCSC, Haemcode, FungiDB, Pazar, Tr...
## # $species: Homo sapiens, Mus musculus, Bos taurus, Pan troglodytes, Danio r...
## # $rdataclass: GRanges, data.frame, DNAStringSet, GRanges
## # additional mcols(): taxonomyid, genome, description,
## # coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
## # rdatapath, sourceurl, sourcetype
## # retrieve records with, e.g., 'object[["AH5012"]]'
##
## title
## AH5012 | Chromosome Band
## AH5013 | STS Markers
## AH5014 | FISH Clones
## AH5015 | Recomb Rate
## AH5016 | ENCODE Pilot
## ... ...
## AH98953 | Zalophus_californianus.mZalCal1.pri.105.gtf
## AH98954 | Zonotrichia_albicollis.Zonotrichia_albicollis-1.0.1.105.abinitio...
## AH98955 | Zonotrichia_albicollis.Zonotrichia_albicollis-1.0.1.105.gtf
## AH98956 | Zosterops_lateralis_melanops.ASM128173v1.105.abinitio.gtf
## AH98957 | Zosterops_lateralis_melanops.ASM128173v1.105.gtfOr you can use subsetting to only select for matches on a specific field
grs <- ah[ah$rdataclass == "GRanges",]The subset function is also provided.
orgs <- subset(ah, ah$rdataclass == "OrgDb")
orgs## AnnotationHub with 1760 records
## # snapshotDate(): 2021-10-20
## # $dataprovider: ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/
## # $species: Escherichia coli, greater Indian_fruit_bat, Zythia rabiei, Zymos...
## # $rdataclass: OrgDb
## # additional mcols(): taxonomyid, genome, description,
## # coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
## # rdatapath, sourceurl, sourcetype
## # retrieve records with, e.g., 'object[["AH95950"]]'
##
## title
## AH95950 | org.Ag.eg.db.sqlite
## AH95951 | org.At.tair.db.sqlite
## AH95952 | org.Bt.eg.db.sqlite
## AH95953 | org.Cf.eg.db.sqlite
## AH95954 | org.Gg.eg.db.sqlite
## ... ...
## AH97709 | org.Westerdykella_ornata.eg.sqlite
## AH97710 | org.Verticillium_alfalfae_VaMs.102.eg.sqlite
## AH97711 | org.Capronia_epimyces_CBS_606.96.eg.sqlite
## AH97712 | org.Sporothrix_schenckii_1099-18.eg.sqlite
## AH97713 | org.Cladophialophora_carrionii_CBS_160.54.eg.sqliteAnd if you really need access to all the metadata you can extract it as a DataFrame using mcols() like so:
meta <- mcols(ah)Also if you are a fan of GUI’s you can use the display method to look at your data in a browser and return selected rows back as a smaller AnnotationHub object like this:
sah <- display(ah)Calling this method will produce a web based interface like the one pictured here:
Once you have the AnnotationHub object pared down to a reasonable size, and are sure about which records you want to retrieve, then you only need to use the ‘[[’ operator to extract them. Using the ‘[[’ operator, you can extract by numeric index (1,2,3) or by AnnotationHub ID. If you choose to use the former, you simply extract the element that you are interested in. So for our chain example, you might just want to 1st one like this:
res <- grs[[1]]## loading from cachehead(res, n=3)## UCSC track 'cytoBand'
## UCSCData object with 3 ranges and 1 metadata column:
## seqnames ranges strand | name
## <Rle> <IRanges> <Rle> | <character>
## [1] chr1 1-2300000 * | p36.33
## [2] chr1 2300001-5400000 * | p36.32
## [3] chr1 5400001-7200000 * | p36.31
## -------
## seqinfo: 93 sequences (1 circular) from hg19 genome4.1 AnnotationHub exercises
Exercise 1: Use the AnnotationHub to extract UCSC data that is from Homo sapiens and also specifically from the hg19 genome. What happens to the hub object as you filter data at each step?
Exercise 2 Now that you have basically narrowed things down to the hg19 annotations from UCSC genome browser, lets get one of these annotations. Find the oreganno track and save it into a local variable.
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5 OrgDb objects
At this point you might be wondering: What is this OrgDb object about? OrgDb objects are one member of a family of annotation objects that all represent hidden data through a shared set of methods. So if you look closely at the dog object created below you can see it contains data for Canis familiaris (taxonomy ID = 9615). You can learn a little more about it by learning about the columns method.
dog <- query(orgs, "Canis familiaris")[[1]]## loading from cachedog## OrgDb object:
## | DBSCHEMAVERSION: 2.1
## | Db type: OrgDb
## | Supporting package: AnnotationDbi
## | DBSCHEMA: CANINE_DB
## | ORGANISM: Canis familiaris
## | SPECIES: Canine
## | EGSOURCEDATE: 2021-Sep13
## | EGSOURCENAME: Entrez Gene
## | EGSOURCEURL: ftp://ftp.ncbi.nlm.nih.gov/gene/DATA
## | CENTRALID: EG
## | TAXID: 9615
## | GOSOURCENAME: Gene Ontology
## | GOSOURCEURL: http://current.geneontology.org/ontology/go-basic.obo
## | GOSOURCEDATE: 2021-09-01
## | GOEGSOURCEDATE: 2021-Sep13
## | GOEGSOURCENAME: Entrez Gene
## | GOEGSOURCEURL: ftp://ftp.ncbi.nlm.nih.gov/gene/DATA
## | KEGGSOURCENAME: KEGG GENOME
## | KEGGSOURCEURL: ftp://ftp.genome.jp/pub/kegg/genomes
## | KEGGSOURCEDATE: 2011-Mar15
## | GPSOURCENAME: UCSC Genome Bioinformatics (Canis familiaris)
## | GPSOURCEURL:
## | GPSOURCEDATE: 2020-Nov23
## | UNIPROTGOSOURCENAME: UNIPROTGO
## | UNIPROTGOSOURCEDATE: Wed Sep 15 15:15:52 2021
## | UNIPROTGOSOURCEURL: ftp://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/idmapping/idmapping_selected.tab.gz
## | ENSOURCEDATE: 2021-Apr13
## | ENSOURCENAME: Ensembl
## | ENSOURCEURL: ftp://ftp.ensembl.org/pub/current_fasta##
## Please see: help('select') for usage informationcolumns(dog)## [1] "ACCNUM" "ALIAS" "ENSEMBL" "ENSEMBLPROT" "ENSEMBLTRANS"
## [6] "ENTREZID" "ENZYME" "EVIDENCE" "EVIDENCEALL" "GENENAME"
## [11] "GENETYPE" "GO" "GOALL" "ONTOLOGY" "ONTOLOGYALL"
## [16] "PATH" "PMID" "REFSEQ" "SYMBOL" "UNIPROT"The columns method gives you a vector of data types that can be retrieved from the object that you call it on. So the above call indicates that there are several different data types that can be retrieved from the tetra object.
A very similar method is the keytypes method, which will list all the data types that can also be used as keys.
keytypes(dog)## [1] "ACCNUM" "ALIAS" "ENSEMBL" "ENSEMBLPROT" "ENSEMBLTRANS"
## [6] "ENTREZID" "ENZYME" "EVIDENCE" "EVIDENCEALL" "GENENAME"
## [11] "GENETYPE" "GO" "GOALL" "ONTOLOGY" "ONTOLOGYALL"
## [16] "PATH" "PMID" "REFSEQ" "SYMBOL" "UNIPROT"In many cases most of the things that are listed as columns will also come back from a keytypes call, but since these two things are not guaranteed to be identical, we maintain two separate methods.
Now that you can see what kinds of things can be used as keys, you can call the keys method to extract out all the keys of a given key type.
head(keys(dog, keytype="ENTREZID"))## [1] "399518" "399530" "399544" "399545" "399653" "403152"This is useful if you need to get all the IDs of a particular kind but the keys method has a few extra arguments that can make it even more flexible. For example, using the keys method you could also extract the gene SYMBOLS that contain “COX” like this:
keys(dog, keytype="SYMBOL", pattern="COX")## [1] "COX5B" "COX7A2L" "COX8A" "COX15" "COX5A" "COX4I1" "COX6A2"
## [8] "COX20" "COX18" "ACOX1" "COX4I2" "ACOX3" "COX10" "COX17"
## [15] "COX11" "ACOXL" "COX7A1" "COX1" "COX2" "COX3" "COX19"
## [22] "COX7B2" "COX14" "ACOX2" "COX16"Or if you really needed an other keytype, you can use the column argument to extract the ENTREZ GENE IDs for those gene SYMBOLS that contain the string “COX”:
keys(dog, keytype="ENTREZID", pattern="COX", column="SYMBOL")## 'select()' returned 1:1 mapping between keys and columns## [1] "474567" "475739" "476040" "477792" "478370" "479623"
## [7] "479780" "480099" "482193" "483322" "485825" "488790"
## [13] "489515" "503668" "609555" "611729" "612614" "804478"
## [19] "804479" "804480" "100685945" "100687434" "100688544" "100855488"
## [25] "119863880"But often, you will really want to extract other data that matches a particular key or set of keys. For that there are two methods which you can use. The more powerful of these is probably select. Here is how you would look up the gene SYMBOL, and REFSEQ id for specific entrez gene ID.
select(dog, keys="804478", columns=c("SYMBOL","REFSEQ"), keytype="ENTREZID")## 'select()' returned 1:1 mapping between keys and columns## ENTREZID SYMBOL REFSEQ
## 1 804478 COX1 NP_008473When you call it, select will return a data.frame that attempts to fill in matching values for all the columns you requested. However, if you ask select for things that have a many to one relationship to your keys it can result in an expansion of the data object that is returned. For example, watch what happens when we ask for the GO terms for the same entrez gene ID:
select(dog, keys="804478", columns="GO", keytype="ENTREZID")## 'select()' returned 1:many mapping between keys and columns## ENTREZID GO EVIDENCE ONTOLOGY
## 1 804478 GO:0004129 IEA MF
## 2 804478 GO:0005750 IEA CC
## 3 804478 GO:0005751 IEA CC
## 4 804478 GO:0006119 IEA BP
## 5 804478 GO:0006123 IEA BP
## 6 804478 GO:0009060 IEA BP
## 7 804478 GO:0015990 IEA BP
## 8 804478 GO:0016021 IEA CC
## 9 804478 GO:0020037 IEA MF
## 10 804478 GO:0022904 IEA BP
## 11 804478 GO:0045277 IEA CC
## 12 804478 GO:0046872 IEA MFBecause there are several GO terms associated with the gene “804478”, you end up with many rows in the data.frame. This can become problematic if you then ask for several columns that have a many to one relationship to the original key. If you were to do that, not only would the result multiply in size, it would also become really hard to use. A better strategy is to be selective when using select.
Sometimes you might want to look up matching results in a way that is simpler than the data.frame object that select returns. This is especially true when you only want to look up one kind of value per key. For these cases, we recommend that you look at the mapIds method. Lets look at what happens if request the same basic information as in our recent select call, but instead using the mapIds method:
mapIds(dog, keys="804478", column="GO", keytype="ENTREZID")## 'select()' returned 1:many mapping between keys and columns## 804478
## "GO:0004129"As you can see, the mapIds method allows you to simplify the result that is returned. And by default, mapIds only returns the 1st matching element for each key. But what if you really need all those GO terms returned when you call mapIds? Well then you can make use of the mapIds multiVals argument. There are several options for this argument, we have already seen how by default you can return only the ‘first’ element. But you can also return a ‘list’ or ‘CharacterList’ object, or you can ‘filter’ out or return ‘asNA’ any keys that have multiple matches. You can even define your own rule (as a function) and pass that in as an argument to multiVals. Lets look at what happens when you return a list:
mapIds(dog, keys="804478", column="GO", keytype="ENTREZID", multiVals="list")## 'select()' returned 1:many mapping between keys and columns## $`804478`
## [1] "GO:0004129" "GO:0005750" "GO:0005751" "GO:0006119" "GO:0006123"
## [6] "GO:0009060" "GO:0015990" "GO:0016021" "GO:0020037" "GO:0022904"
## [11] "GO:0045277" "GO:0046872"Now you know how to extract information from an OrgDb object, you might find it helpful to know that there is a whole family of other AnnotationDb derived objects that you can also use with these same five methods (keytypes(), columns(), keys(), select(), and mapIds()). For example there are ChipDb objects, InparanoidDb objects and TxDb objects which contain data about microarray probes, inparanoid homology partners or transcript range information respectively. And there are also more specialized objects like GODb or ReactomeDb objects which offer access to data from GO and reactome. In the next section, we will be looking at one of the more popular classes of these objects: the TxDb object.
5.1 OrgDb exercises
Exercise 3: Look at the help page for the different columns and keytypes values with: help(“SYMBOL”). Now use this information and what we just described to look up the entrez gene and chromosome for the gene symbol “MSX2”.
Exercise 4: In the previous exercise we had to use gene symbols as keys. But in the past this kind of behavior has sometimes been inadvisable because some gene symbols are used as the official symbol for more than one gene. To learn if this is still happening take advantage of the fact that entrez gene ids are uniquely assigned, and extract all of the gene symbols and their associated entrez gene ids from the org.Hs.eg.db package. Then check the symbols for redundancy.
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6 TxDb Objects
As mentioned before, TxDb objects can be accessed using the standard set of methods: keytypes(), columns(), keys(), select(), and mapIds(). But because these objects contain information about a transcriptome, they are often used to compare range based information to these important features of the genome[3,4]. As a result they also have specialized accessors for extracting out ranges that correspond to important transcriptome characteristics.
Lets start by loading a TxDb object from an annotation package based on the UCSC ensembl genes track for Drosophila. A common practice when loading these is to shorten the long name to ‘txdb’ (just as a convenience).
txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene
txdb## TxDb object:
## # Db type: TxDb
## # Supporting package: GenomicFeatures
## # Data source: UCSC
## # Genome: hg19
## # Organism: Homo sapiens
## # Taxonomy ID: 9606
## # UCSC Table: knownGene
## # Resource URL: http://genome.ucsc.edu/
## # Type of Gene ID: Entrez Gene ID
## # Full dataset: yes
## # miRBase build ID: GRCh37
## # transcript_nrow: 82960
## # exon_nrow: 289969
## # cds_nrow: 237533
## # Db created by: GenomicFeatures package from Bioconductor
## # Creation time: 2015-10-07 18:11:28 +0000 (Wed, 07 Oct 2015)
## # GenomicFeatures version at creation time: 1.21.30
## # RSQLite version at creation time: 1.0.0
## # DBSCHEMAVERSION: 1.1Just by looking at the TxDb object, we can learn a lot about what data it contains including where the data came from, which build of the UCSC genome it was based on and the last time that the object was updated. One of the most common uses for a TxDb object is to extract various kinds of transcript data out of it. So for example you can extract all the transcripts out of the TxDb as a GRanges object like this:
txs <- transcripts(txdb)
txs## GRanges object with 5506 ranges and 2 metadata columns:
## seqnames ranges strand | tx_id tx_name
## <Rle> <IRanges> <Rle> | <integer> <character>
## [1] chr3 238279-451097 + | 13060 uc003bot.3
## [2] chr3 238279-451097 + | 13061 uc003bou.3
## [3] chr3 239326-290282 + | 13062 uc003bov.2
## [4] chr3 239326-440831 + | 13063 uc003bow.2
## [5] chr3 361366-451097 + | 13064 uc011asi.2
## ... ... ... ... . ... ...
## [5502] chr18 77732867-77748532 - | 65761 uc002lnr.3
## [5503] chr18 77732867-77748532 - | 65762 uc010drf.3
## [5504] chr18 77732867-77793915 - | 65763 uc010drg.3
## [5505] chr18 77915117-78005397 - | 65764 uc002lny.3
## [5506] chr18 77941005-78005397 - | 65765 uc010xfp.2
## -------
## seqinfo: 2 sequences from hg19 genomeSimilarly, there are also extractors for exons(), cds(), genes() and promoters(). Which kind of feature you choose to extract just depends on what information you are after. These basic extractors are fine if you only want a flat representation of these data, but many of these features are inherently nested. So instead of extracting a flat GRanges object, you might choose instead to extract a GRangesList object that groups the transcripts by the genes that they are associated with like this:
txby <- transcriptsBy(txdb, by="gene")
txby## GRangesList object of length 1612:
## $`1000`
## GRanges object with 2 ranges and 2 metadata columns:
## seqnames ranges strand | tx_id tx_name
## <Rle> <IRanges> <Rle> | <integer> <character>
## [1] chr18 25530930-25616539 - | 65378 uc010xbn.1
## [2] chr18 25530930-25757445 - | 65379 uc002kwg.2
## -------
## seqinfo: 2 sequences from hg19 genome
##
## $`100009676`
## GRanges object with 1 range and 2 metadata columns:
## seqnames ranges strand | tx_id tx_name
## <Rle> <IRanges> <Rle> | <integer> <character>
## [1] chr3 101395274-101398057 + | 14200 uc003dvg.3
## -------
## seqinfo: 2 sequences from hg19 genome
##
## $`100101467`
## GRanges object with 3 ranges and 2 metadata columns:
## seqnames ranges strand | tx_id tx_name
## <Rle> <IRanges> <Rle> | <integer> <character>
## [1] chr18 32831023-32870196 - | 65418 uc002kyl.3
## [2] chr18 32831023-32870196 - | 65419 uc002kym.3
## [3] chr18 32843361-32870165 - | 65420 uc002kyn.1
## -------
## seqinfo: 2 sequences from hg19 genome
##
## ...
## <1609 more elements>Just as with the flat extractors, there is a whole family of extractors available depending on what you want to extract and how you want it grouped. They include transcriptsBy(), exonsBy(), cdsBy(), intronsByTranscript(), fiveUTRsByTranscript() and threeUTRsByTranscript().
When dealing with genomic data it is almost inevitable that you will run into problems with the way that different groups have adopted alternate ways of naming chromosomes. This is because almost every major repository has cooked up their own slightly different way of labeling these important features.
To cope with this, the Seqinfo object was invented and is attached to TxDb objects as well as the GenomicRanges extracted from these objects. You can extract it using the seqinfo() method like this:
si <- seqinfo(txdb)
si## Seqinfo object with 2 sequences from hg19 genome:
## seqnames seqlengths isCircular genome
## chr3 198022430 NA hg19
## chr18 78077248 NA hg19And since the seqinfo information is also attached to the GRanges objects produced by the TxDb extractors, you can also call seqinfo on the results of those methods like this:
txby <- transcriptsBy(txdb, by="gene")
si <- seqinfo(txby)The Seqinfo object contains a lot of valuable data about which chromosome features are present, whether they are circular or linear, and how long each one is. It is also something that will be checked against if you try to do an operation like ‘findOverlaps’ to compute overlapping ranges etc. So it’s a valuable way to make sure that the chromosomes and genome are the same for your annotations as the range that you are comparing them to. But sometimes you may have a situation where your annotation object contains data that is comparable to your data object, but where it is simply named with a different naming style. For those cases, there are helpers that you can use to discover what the current name style is for an object. And there is also a setter method to allow you to change the value to something more appropriate. So in the following example, we are going to change the seqlevelStyle from ‘UCSC’ to ‘ensembl’ based naming convention (and then back again).
head(seqlevels(txdb))## [1] "chr3" "chr18"seqlevelsStyle(txdb)## [1] "UCSC"seqlevelsStyle(txdb) <- "NCBI"
head(seqlevels(txdb))## [1] "3" "18"## then change it back
seqlevelsStyle(txdb) <- "UCSC"
head(seqlevels(txdb))## [1] "chr3" "chr18"In addition to being able to change the naming style used for an object with seqinfo data, you can also toggle which of the chromosomes are ‘active’ so that the software will ignore certain chromosomes. By default, all of the chromosomes are set to be ‘active’.
head(isActiveSeq(txdb), n=30)## chr3 chr18
## TRUE TRUEBut sometimes you might wish to ignore some of them. For example, lets suppose that you wanted to ignore the Y chromosome from our txdb. You could do that like so:
isActiveSeq(txdb)["chrY"] <- FALSE
head(isActiveSeq(txdb), n=26)6.1 TxDb exercises
Exercise 5: Use the accessors for the TxDb.Hsapiens.UCSC.hg19.knownGene package to retrieve the gene id, transcript name and transcript chromosome for all the transcripts. Do this using both the select() method and also using the transcripts() method. What is the difference in the output?
Exercise 6: Load the TxDb.Athaliana.BioMart.plantsmart22 package. This package is not from UCSC and it is based on plantsmart. Now use select or one of the range based accessors to look at the gene ids from this TxDb object. How do they compare to what you saw in the TxDb.Hsapiens.UCSC.hg19.knownGene package?
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7 Organism.dplyr src_organism Objects
So what happens if you have data from multiple different Annotation objects. For example, what if you had gene SYMBOLS (found in an OrgDb object) and you wanted to easily match those up with known gene transcript names from a UCSC based TxDb object? There is an ideal tool that can help with this kind of problem and it’s called an src_organism object from the Organism.dplyr package. src_organism objects and their related methods are able to query each of OrgDb and TxDb resources for you and then merge the results back together in way that lets you pretend that you only have one source for all your annotations.
library(Organism.dplyr)src_organism objects can be created for organisms that have both an OrgDb and a TxDb. To see organisms that can have src_organism objects made, use the function supportOrganisms():
supported <- supportedOrganisms()
print(supported, n=Inf)## # A tibble: 21 × 3
## organism OrgDb TxDb
## <chr> <chr> <chr>
## 1 Bos taurus org.Bt.eg.db TxDb.Btaurus.UCSC.bosTau8.refGene
## 2 Caenorhabditis elegans org.Ce.eg.db TxDb.Celegans.UCSC.ce11.refGene
## 3 Caenorhabditis elegans org.Ce.eg.db TxDb.Celegans.UCSC.ce6.ensGene
## 4 Canis familiaris org.Cf.eg.db TxDb.Cfamiliaris.UCSC.canFam3.refGene
## 5 Drosophila melanogaster org.Dm.eg.db TxDb.Dmelanogaster.UCSC.dm3.ensGene
## 6 Drosophila melanogaster org.Dm.eg.db TxDb.Dmelanogaster.UCSC.dm6.ensGene
## 7 Danio rerio org.Dr.eg.db TxDb.Drerio.UCSC.danRer10.refGene
## 8 Gallus gallus org.Gg.eg.db TxDb.Ggallus.UCSC.galGal4.refGene
## 9 Homo sapiens org.Hs.eg.db TxDb.Hsapiens.UCSC.hg18.knownGene
## 10 Homo sapiens org.Hs.eg.db TxDb.Hsapiens.UCSC.hg19.knownGene
## 11 Homo sapiens org.Hs.eg.db TxDb.Hsapiens.UCSC.hg38.knownGene
## 12 Mus musculus org.Mm.eg.db TxDb.Mmusculus.UCSC.mm10.ensGene
## 13 Mus musculus org.Mm.eg.db TxDb.Mmusculus.UCSC.mm10.knownGene
## 14 Mus musculus org.Mm.eg.db TxDb.Mmusculus.UCSC.mm9.knownGene
## 15 Macaca mulatta org.Mmu.eg.db TxDb.Mmulatta.UCSC.rheMac3.refGene
## 16 Macaca mulatta org.Mmu.eg.db TxDb.Mmulatta.UCSC.rheMac8.refGene
## 17 Pan troglodytes org.Pt.eg.db TxDb.Ptroglodytes.UCSC.panTro4.refGene
## 18 Rattus norvegicus org.Rn.eg.db TxDb.Rnorvegicus.UCSC.rn4.ensGene
## 19 Rattus norvegicus org.Rn.eg.db TxDb.Rnorvegicus.UCSC.rn5.refGene
## 20 Rattus norvegicus org.Rn.eg.db TxDb.Rnorvegicus.UCSC.rn6.refGene
## 21 Sus scrofa org.Ss.eg.db TxDb.Sscrofa.UCSC.susScr3.refGeneNotice how there are multiple entries for a single organism (e.g. three for Homo sapiens). There is only one OrgDb per organism, but different TxDbs can be used. To specify a certain version of a TxDb to use, we can use the src_organism() function to create an src_organism object.
library(org.Hs.eg.db)
library(TxDb.Hsapiens.UCSC.hg38.knownGene)src <- src_organism("TxDb.Hsapiens.UCSC.hg38.knownGene")## creating 'src_organism' database...src## src: sqlite 3.38.2 [/tmp/RtmpGxMwmM/file1a1cb836c40aff]
## tbls: id, id_accession, id_go, id_go_all, id_omim_pm, id_protein,
## id_transcript, ranges_cds, ranges_exon, ranges_gene, ranges_txWe can also create one using the src_ucsc() function. This will create an src_organism object using the most recent TxDb version available:
src <- src_ucsc("Homo sapiens")src## src: sqlite 3.38.2 [/tmp/RtmpGxMwmM/file1a1cb836c40aff]
## tbls: id, id_accession, id_go, id_go_all, id_omim_pm, id_protein,
## id_transcript, ranges_cds, ranges_exon, ranges_gene, ranges_txThe five methods that worked for all of the other Db objects that we have discussed (keytypes(), columns(), keys(), select(), and mapIds()) all work for src_organism objects. Here, we use keytypes() to show which keytypes can be passed to the keytype argument of select().
keytypes(src)## [1] "accnum" "alias" "cds_chrom" "cds_end" "cds_id"
## [6] "cds_name" "cds_start" "cds_strand" "ensembl" "ensemblprot"
## [11] "ensembltrans" "entrez" "enzyme" "evidence" "evidenceall"
## [16] "exon_chrom" "exon_end" "exon_id" "exon_name" "exon_rank"
## [21] "exon_start" "exon_strand" "gene_chrom" "gene_end" "gene_start"
## [26] "gene_strand" "genename" "go" "goall" "ipi"
## [31] "map" "omim" "ontology" "ontologyall" "pfam"
## [36] "pmid" "prosite" "refseq" "symbol" "tx_chrom"
## [41] "tx_end" "tx_id" "tx_name" "tx_start" "tx_strand"
## [46] "tx_type" "uniprot"Use columns() to show which keytypes can be passed to the keytype argument of select().
columns(src)## [1] "accnum" "alias" "cds_chrom" "cds_end" "cds_id"
## [6] "cds_name" "cds_start" "cds_strand" "ensembl" "ensemblprot"
## [11] "ensembltrans" "entrez" "enzyme" "evidence" "evidenceall"
## [16] "exon_chrom" "exon_end" "exon_id" "exon_name" "exon_rank"
## [21] "exon_start" "exon_strand" "gene_chrom" "gene_end" "gene_start"
## [26] "gene_strand" "genename" "go" "goall" "ipi"
## [31] "map" "omim" "ontology" "ontologyall" "pfam"
## [36] "pmid" "prosite" "refseq" "symbol" "tx_chrom"
## [41] "tx_end" "tx_id" "tx_name" "tx_start" "tx_strand"
## [46] "tx_type" "uniprot"And that’s it. You can now use these objects in the same way that you use OrgDb or TxDb objects. It works the same as the base objects that it contains:
select(src, keys="4488", columns=c("symbol", "tx_name"), keytype="entrez")## Joining, by = "entrez"## entrez symbol tx_name
## 1 4488 MSX2 ENST00000239243.7
## 2 4488 MSX2 ENST00000507785.2
## 3 4488 MSX2 ENST00000239243.7
## 4 4488 MSX2 ENST00000507785.2
## 5 4488 MSX2 ENST00000239243.7
## 6 4488 MSX2 ENST00000507785.2
## 7 4488 MSX2 ENST00000239243.7
## 8 4488 MSX2 ENST00000507785.2
## 9 4488 MSX2 ENST00000239243.7
## 10 4488 MSX2 ENST00000507785.2
## 11 4488 MSX2 ENST00000239243.7
## 12 4488 MSX2 ENST00000507785.2
## 13 4488 MSX2 ENST00000239243.7
## 14 4488 MSX2 ENST00000507785.2Organism.dplyr also supports numerous Genomic Extractor functions allowing users to filter based on information contained in the OrgDb and TxDb objects. To see the filters supported by a src_organism() object, use supportedFIlters():
head(supportedFilters(src))## filter field
## 1 AccnumFilter accnum
## 2 AliasFilter alias
## 3 CdsChromFilter cds_chrom
## 44 CdsEndFilter cds_end
## 42 CdsIdFilter cds_id
## 4 CdsNameFilter cds_nameThe ranged based accessors such as those in GenomicFeatures will also work. There are also "_tbl" functions (e.g. transcripts_tbl()) that return tbl objects instead of GRanges objects. Complex filter statements can be given as input. Here we declare a GRangesFilter and use two different type-returning accessors to query transcripts that either start with “SNORD” and are within our given GRangesFilter, or have symbol with symbol “ADA”:
gr <- GRangesFilter(GenomicRanges::GRanges("chr1:44000000-55000000"))
transcripts(src, filter=~(symbol %startsWith% "SNORD" & gr) | symbol == "ADA")## Warning: Using `across()` in `filter()` is deprecated, use `if_any()` or
## `if_all()`.## GRanges object with 14 ranges and 3 metadata columns:
## seqnames ranges strand | tx_id tx_name
## <Rle> <IRanges> <Rle> | <integer> <character>
## [1] chr1 44775864-44775943 + | 3192 ENST00000581525.1
## [2] chr1 44776490-44776593 + | 3193 ENST00000364043.1
## [3] chr1 44777843-44777912 + | 3196 ENST00000365161.1
## [4] chr1 44778390-44778456 + | 3198 ENST00000384690.1
## [5] chr1 44778390-44778458 + | 3199 ENST00000625943.1
## ... ... ... ... . ... ...
## [10] chr20 44619810-44651691 - | 219675 ENST00000537820.1
## [11] chr20 44619810-44651691 - | 219676 ENST00000539235.5
## [12] chr20 44626323-44651661 - | 219677 ENST00000545776.5
## [13] chr20 44626517-44652114 - | 219678 ENST00000536076.1
## [14] chr20 44636071-44652233 - | 219679 ENST00000535573.1
## symbol
## <character>
## [1] SNORD55
## [2] SNORD46
## [3] SNORD38A
## [4] SNORD38B
## [5] SNORD38B
## ... ...
## [10] ADA
## [11] ADA
## [12] ADA
## [13] ADA
## [14] ADA
## -------
## seqinfo: 640 sequences (1 circular) from hg38 genometranscripts_tbl(src, filter=~(symbol %startsWith% "SNORD" & gr) | symbol == "ADA")## # A tibble: 14 × 7
## tx_chrom tx_start tx_end tx_strand tx_id tx_name symbol
## <chr> <int> <int> <chr> <int> <chr> <chr>
## 1 chr1 44775864 44775943 + 3192 ENST00000581525.1 SNORD55
## 2 chr1 44776490 44776593 + 3193 ENST00000364043.1 SNORD46
## 3 chr1 44777843 44777912 + 3196 ENST00000365161.1 SNORD38A
## 4 chr1 44778390 44778456 + 3198 ENST00000384690.1 SNORD38B
## 5 chr1 44778390 44778458 + 3199 ENST00000625943.1 SNORD38B
## 6 chr20 44619522 44626491 - 219671 ENST00000464097.5 ADA
## 7 chr20 44619522 44651699 - 219672 ENST00000372874.9 ADA
## 8 chr20 44619579 44651681 - 219673 ENST00000536532.5 ADA
## 9 chr20 44619810 44651691 - 219674 ENST00000492931.5 ADA
## 10 chr20 44619810 44651691 - 219675 ENST00000537820.1 ADA
## 11 chr20 44619810 44651691 - 219676 ENST00000539235.5 ADA
## 12 chr20 44626323 44651661 - 219677 ENST00000545776.5 ADA
## 13 chr20 44626517 44652114 - 219678 ENST00000536076.1 ADA
## 14 chr20 44636071 44652233 - 219679 ENST00000535573.1 ADA7.1 Organism.dplyr exercises
Exercise 7: Use the src_organism object to look up the gene symbol, transcript start and chromosome using select(). Then do the same thing using transcripts. You might expect that this call to transcripts will look the same as it did for the TxDb object, but (temporarily) it will not.
Exercise 8: Look at the results from call the columns method on the src_organism object and compare that to what happens when you call columns on the org.Hs.eg.db object and then look at a call to columns on the TxDb.Hsapiens.UCSC.hg19.knownGene object.
Exercise 9: Use the src_organism object with the transcripts method to look up the entrez gene IDs for all gene symbols that contain the letter ‘X’.
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8 BSgenome Objects
Another important annotation resource type is a BSgenome package[10]. There are many BSgenome packages in the repository for you to choose from. And you can learn which organisms are already supported by using the available.genomes() function.
head(available.genomes())## [1] "BSgenome.Alyrata.JGI.v1"
## [2] "BSgenome.Amellifera.BeeBase.assembly4"
## [3] "BSgenome.Amellifera.NCBI.AmelHAv3.1"
## [4] "BSgenome.Amellifera.UCSC.apiMel2"
## [5] "BSgenome.Amellifera.UCSC.apiMel2.masked"
## [6] "BSgenome.Aofficinalis.NCBI.V1"Unlike the other resources that we have discussed here, these packages are meant to contain sequence data for a specific genome build of an organism. You can load one of these packages in the usual way. And each of them normally has an alias for the primary object that is shorter than the full package name (as a convenience):
ls(2)## character(0)Hsapiens## Human genome:
## # organism: Homo sapiens (Human)
## # genome: hg19
## # provider: UCSC
## # release date: June 2013
## # 298 sequences:
## # chr1 chr2 chr3
## # chr4 chr5 chr6
## # chr7 chr8 chr9
## # chr10 chr11 chr12
## # chr13 chr14 chr15
## # ... ... ...
## # chr19_gl949749_alt chr19_gl949750_alt chr19_gl949751_alt
## # chr19_gl949752_alt chr19_gl949753_alt chr20_gl383577_alt
## # chr21_gl383578_alt chr21_gl383579_alt chr21_gl383580_alt
## # chr21_gl383581_alt chr22_gl383582_alt chr22_gl383583_alt
## # chr22_kb663609_alt
## # (use 'seqnames()' to see all the sequence names, use the '$' or '[[' operator
## # to access a given sequence)The getSeq method is a useful way of extracting data from these packages. This method takes several arguments but the important ones are the 1st two. The 1st argument specifies the BSgenome object to use and the second argument (names) specifies what data you want back out. So for example, if you call it and give a character vector that names the seqnames for the object then you will get the sequences from those chromosomes as a DNAStringSet object.
seqNms <- seqnames(Hsapiens)
head(seqNms)## [1] "chr1" "chr2" "chr3" "chr4" "chr5" "chr6"getSeq(Hsapiens, seqNms[1:2])## DNAStringSet object of length 2:
## width seq names
## [1] 249250621 NNNNNNNNNNNNNNNNNNNNN...NNNNNNNNNNNNNNNNNNNNN chr1
## [2] 243199373 NNNNNNNNNNNNNNNNNNNNN...NNNNNNNNNNNNNNNNNNNNN chr2Whereas if you give the a GRanges object for the 2nd argument, you can instead get a DNAStringSet that corresponds to those ranges. This can be a powerful way to learn what sequence was present from a particular range. For example, here we can extract the range of a specific gene of interest like this.
txby <- transcriptsBy(txdb, by="gene")
geneOfInterest <- txby[["4488"]]
res <- getSeq(Hsapiens, geneOfInterest)
resAdditionally, the Biostrings[11] package has many useful functions for finding a pattern in a string set etc. You may not have noticed when it happened, but the Biostrings package was loaded when you loaded the BSgenome object, so these functions will already be available for you to explore.
8.1 BSgenome exercises
Exercise 10: Use what you have just learned to extract the sequence for the PTEN gene.
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9 biomaRt
Another great annotation resource is the biomaRt package[5,6,7]. The biomaRt package exposes a huge family of different online annotation resources called marts. Each mart is another of a set of online web resources that are following a convention that allows them to work with this package. So the first step in using biomaRt is always to load the package and then decide which “mart” you want to use. Once you have made your decision, you will then use the useEnsembl() method to create a mart object in your R session. Here we are looking at the marts available and then choosing to use one of the most popular marts: the “ensembl”" mart.
head(listMarts())## biomart version
## 1 ENSEMBL_MART_ENSEMBL Ensembl Genes 106
## 2 ENSEMBL_MART_MOUSE Mouse strains 106
## 3 ENSEMBL_MART_SNP Ensembl Variation 106
## 4 ENSEMBL_MART_FUNCGEN Ensembl Regulation 106ensembl <- useEnsembl("ensembl")
ensembl## Object of class 'Mart':
## Using the ENSEMBL_MART_ENSEMBL BioMart database
## No dataset selected.Each ‘mart’ can contain datasets for multiple different things. So the next step is that you need to decide on a dataset. Once you have chosen one, you will need to specify that dataset using the dataset argument when you call the useEnsembl() constructor method. Here we will point to the dataset for humans.
head(listDatasets(ensembl))## dataset description
## 1 abrachyrhynchus_gene_ensembl Pink-footed goose genes (ASM259213v1)
## 2 acalliptera_gene_ensembl Eastern happy genes (fAstCal1.2)
## 3 acarolinensis_gene_ensembl Green anole genes (AnoCar2.0v2)
## 4 acchrysaetos_gene_ensembl Golden eagle genes (bAquChr1.2)
## 5 acitrinellus_gene_ensembl Midas cichlid genes (Midas_v5)
## 6 amelanoleuca_gene_ensembl Giant panda genes (ASM200744v2)
## version
## 1 ASM259213v1
## 2 fAstCal1.2
## 3 AnoCar2.0v2
## 4 bAquChr1.2
## 5 Midas_v5
## 6 ASM200744v2ensembl <- useEnsembl("ensembl",dataset="hsapiens_gene_ensembl")
ensembl## Object of class 'Mart':
## Using the ENSEMBL_MART_ENSEMBL BioMart database
## Using the hsapiens_gene_ensembl datasetNext we need to think about attributes, values and filters. Lets start with attributes. You can get a listing of the different kinds of attributes from biomaRt buy using the listAttributes method:
head(listAttributes(ensembl))## 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_pageAnd you can see what the values for a particular attribute are by using the getBM method:
head(getBM(attributes="chromosome_name", mart=ensembl))## chromosome_name
## 1 1
## 2 10
## 3 11
## 4 12
## 5 13
## 6 14Attributes are the things that you can have returned from biomaRt. They are analogous to what you get when you use the columns method with other objects.
In the biomaRt package, filters are things that can be used with values to restrict or choose what comes back. The ‘values’ here are treated as keys that you are passing in and which you would like to know more information about. In contrast, the filter represents the kind of key that you are searching for. So for example, you might choose a filter name of “chromosome_name” to go with specific value of “1”. Together these two argument values would request whatever attributes matched things on the 1st chromosome. And just as there here is an accessor for attributes, there is also an accessor for filters:
head(listFilters(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 StartSo now you know about attributes, values and filters, you can call the getBM method to put it all together and request specific data from the mart. So for example, the following requests gene symbols and entrez gene IDs that are found on chromosome 1 of flies:
res <- getBM(attributes=c("hgnc_symbol", "entrezgene_id"),
filters = "chromosome_name",
values = "1", mart = ensembl)
head(res)## hgnc_symbol entrezgene_id
## 1 DDX11L1 84771
## 2 DDX11L1 727856
## 3 DDX11L1 100287102
## 4 DDX11L1 100287596
## 5 DDX11L1 102725121
## 6 WASH7P NAOf course you may have noticed that a lot of the arguments for getBM are very similar to what you do when you call shsapienselect. So if it’s your preference you can now also use the standard select methods with mart objects.
head(columns(ensembl))## [1] "3_utr_end" "3_utr_end" "3_utr_start" "3_utr_start" "3utr"
## [6] "5_utr_end"9.1 biomaRt exercises
Exercise 11: Pull down GO terms for entrez gene id “1” from human by using the ensembl “hsapiens_gene_ensembl” dataset.
Exercise 12: Now compare the GO terms you just pulled down to the same GO terms from the org.Hs.eg.db package (which you can now retrieve using select()). What differences do you notice? Why do you suspect that is?
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10 Creating annotation objects
By now you are aware that Bioconductor has a lot of annotation resources. But it is still completely impossible to have every annotation resource pre-packaged for every conceivable use. Because of this, almost all annotation objects have special functions that can be called to create those objects (or the packages that load them) from generalized data resources or specific file types. Below is a table with a few of the more popular options.
| If you want this | And you have this | Then you could call this to help |
|---|---|---|
| TxDb | tracks from UCSC | GenomicFeatures::makeTxDbPackageFromUCSC |
| TxDb | data from biomaRt | GenomicFeatures::makeTxDbPackageFromBiomaRt |
| TxDb | gff or gtf file | GenomicFeatures::makeTxDbFromGFF |
| OrgDb | custom data.frames | AnnotationForge::makeOrgPackage |
| OrgDb | valid Taxonomy ID | AnnotationForge::makeOrgPackageFromNCBI |
| ChipDb | org package & data.frame | AnnotationForge::makeChipPackage |
| BSgenome | fasta or twobit sequence files | BSgenome::forgeBSgenomeDataPkg |
In most cases the output for resource creation functions will be an annotation package that you can install.
And there is unfortunately not enough space to demonstrate how to call each of these functions here. But to do so is actually pretty straightforward and most such functions will be well documented with their associated manual pages and vignettes[3,4,10,12]. As usual, you can see the help page for any function right inside of R.
help("makeTxDbPackageFromUCSC")If you plan to make use of these kinds of functions then you should expect to consult the associated documentation first. These kinds of functions tend to have a lot of arguments and most of them also require that their input data meet some fairly specific criteria. Finally, you should know that even after you have succeeded at creating an annotation package, you will also have to make use of the install.packages() function (with the repos argument=NULL) to install whatever package source directory has just been created.
11 Important considerations
The bioconductor project represents a very large and active codebase from an active and engaged community. Because of this, you should expect that the software described in this walkthrough will change over time and often in dramatic ways. As an example, the getSeq function that is described in this chapter is expected to a big overhaul in the coming months. When this happens the older function will be deprecated for a full release cycle (6 months) and then labeled as defunct for another release cycle before it is removed. This cycle is in place so that active users can be warned about what is happening and where they should look for the appropriate replacement functionality. But obviously, this system cannot warn end users if they have not been vigilant about updating their software to the latest version. So please take the time to always update your software to the latest version.
To stay abreast of new developments users are encouraged to explore the bioconductor website which contains many current walkthroughs and vignettes. Also visit the support site where you can ask questions and engage in discussions.
12 sessionInfo()
Package versions used in this tutorial:
sessionInfo()## R version 4.1.3 (2022-03-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.14-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.14-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] annotation_1.18.1
## [2] TxDb.Athaliana.BioMart.plantsmart22_3.0.1
## [3] biomaRt_2.50.3
## [4] BSgenome.Hsapiens.UCSC.hg19_1.4.3
## [5] BSgenome_1.62.0
## [6] rtracklayer_1.54.0
## [7] Homo.sapiens_1.3.1
## [8] GO.db_3.14.0
## [9] OrganismDbi_1.36.0
## [10] org.Mm.eg.db_3.14.0
## [11] org.Hs.eg.db_3.14.0
## [12] TxDb.Mmusculus.UCSC.mm10.ensGene_3.4.0
## [13] TxDb.Hsapiens.UCSC.hg38.knownGene_3.14.0
## [14] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
## [15] GenomicFeatures_1.46.5
## [16] AnnotationDbi_1.56.2
## [17] Organism.dplyr_1.22.1
## [18] AnnotationFilter_1.18.0
## [19] dplyr_1.0.8
## [20] AnnotationHub_3.2.2
## [21] BiocFileCache_2.2.1
## [22] dbplyr_2.1.1
## [23] VariantAnnotation_1.40.0
## [24] Rsamtools_2.10.0
## [25] Biostrings_2.62.0
## [26] XVector_0.34.0
## [27] SummarizedExperiment_1.24.0
## [28] Biobase_2.54.0
## [29] GenomicRanges_1.46.1
## [30] GenomeInfoDb_1.30.1
## [31] IRanges_2.28.0
## [32] S4Vectors_0.32.4
## [33] MatrixGenerics_1.6.0
## [34] matrixStats_0.61.0
## [35] BiocGenerics_0.40.0
## [36] BiocStyle_2.22.0
##
## loaded via a namespace (and not attached):
## [1] rjson_0.2.21 ellipsis_0.3.2
## [3] bit64_4.0.5 interactiveDisplayBase_1.32.0
## [5] fansi_1.0.3 xml2_1.3.3
## [7] cachem_1.0.6 knitr_1.38
## [9] jsonlite_1.8.0 png_0.1-7
## [11] graph_1.72.0 shiny_1.7.1
## [13] BiocManager_1.30.16 compiler_4.1.3
## [15] httr_1.4.2 assertthat_0.2.1
## [17] Matrix_1.4-1 fastmap_1.1.0
## [19] lazyeval_0.2.2 cli_3.2.0
## [21] later_1.3.0 htmltools_0.5.2
## [23] prettyunits_1.1.1 tools_4.1.3
## [25] glue_1.6.2 GenomeInfoDbData_1.2.7
## [27] rappdirs_0.3.3 Rcpp_1.0.8.3
## [29] jquerylib_0.1.4 vctrs_0.4.0
## [31] xfun_0.30 stringr_1.4.0
## [33] mime_0.12 lifecycle_1.0.1
## [35] restfulr_0.0.13 XML_3.99-0.9
## [37] zlibbioc_1.40.0 hms_1.1.1
## [39] promises_1.2.0.1 parallel_4.1.3
## [41] RBGL_1.70.0 yaml_2.3.5
## [43] curl_4.3.2 memoise_2.0.1
## [45] sass_0.4.1 stringi_1.7.6
## [47] RSQLite_2.2.12 BiocVersion_3.14.0
## [49] BiocIO_1.4.0 filelock_1.0.2
## [51] BiocParallel_1.28.3 rlang_1.0.2
## [53] pkgconfig_2.0.3 bitops_1.0-7
## [55] evaluate_0.15 lattice_0.20-45
## [57] purrr_0.3.4 GenomicAlignments_1.30.0
## [59] bit_4.0.4 tidyselect_1.1.2
## [61] magrittr_2.0.3 bookdown_0.25
## [63] R6_2.5.1 generics_0.1.2
## [65] DelayedArray_0.20.0 DBI_1.1.2
## [67] pillar_1.7.0 withr_2.5.0
## [69] KEGGREST_1.34.0 RCurl_1.98-1.6
## [71] tibble_3.1.6 crayon_1.5.1
## [73] utf8_1.2.2 rmarkdown_2.13
## [75] progress_1.2.2 grid_4.1.3
## [77] blob_1.2.3 digest_0.6.29
## [79] xtable_1.8-4 httpuv_1.6.5
## [81] bslib_0.3.113 Acknowledgments
Research reported in this chapter was supported by the National Human Genome Research Institute of the National Institutes of Health under Award Number U41HG004059 and by the National Cancer Institute of the National Institutes of Health under Award Number U24CA180996. We also want to thank the numerous institutions who produced and maintained the data that is used for generating and updating the annotation resources described here.
14 References
Wolfgang Huber, Vincent J Carey, Robert Gentleman, Simon Anders, Marc Carlson, Benilton S Carvalho, Hector Corrada Bravo, Sean Davis, Laurent Gatto, Thomas Girke, Raphael Gottardo, Florian Hahne, Kasper D Hansen, Rafael A Irizarry, Michael Lawrence, Michael I Love, James MacDonald, Valerie Obenchain, Andrzej K Oleś, Hervé Pagès, Alejandro Reyes, Paul Shannon, Gordon K Smyth, Dan Tenenbaum, Levi Waldron & Martin Morgan (2015) Orchestrating high-throughput genomic analysis with Bioconductor Nature Methods 12:115-121
Pages H, Carlson M, Falcon S and Li N. AnnotationDbi: Annotation Database Interface. R package version 1.30.0.
M. Carlson, H. Pages, P. Aboyoun, S. Falcon, M. Morgan, D. Sarkar, M. Lawrence GenomicFeatures: Tools for making and manipulating transcript centric annotations version 1.19.38.
Lawrence M, Huber W, Pagès H, Aboyoun P, Carlson M, Gentleman R, Morgan M and Carey V (2013). Software for Computing and Annotating Genomic Ranges. PLoS Computational Biology, 9. http://dx.doi.org/10.1371/journal.pcbi.1003118, http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003118
Steffen Durinck, Wolfgang Huber biomaRt: Interface to BioMart databases (e.g. Ensembl, COSMIC ,Wormbase and Gramene) version 2.23.5.
Durinck S, Spellman P, Birney E and Huber W (2009). Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nature Protocols, 4, pp. 1184-1191.
Durinck S, Moreau Y, Kasprzyk A, Davis S, De Moor B, Brazma A and Huber W (2005). BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis. Bioinformatics, 21, pp. 3439-3440.
Morgan M, Carlson M, Tenenbaum D and Arora S. AnnotationHub: Client to access AnnotationHub resources. R package version 2.0.1.
Carlson M, Pages H, Morgan M and Obenchain V. OrganismDbi: Software to enable the smooth interfacing of different database packages. R package version 1.10.0.
Pages H. BSgenome: Infrastructure for Biostrings-based genome data packages. R package version 1.36.0.
Pages H, Aboyoun P, Gentleman R and DebRoy S. Biostrings: String objects representing biological sequences, and matching algorithms. R package version 2.36.0.
Carlson M, and Pages H. AnnotationForge: Code for Building Annotation Database Packages. R package version 1.10.0.
15 Answers for exercises
15.1 Exercise 1:
The 1st thing you need to do is look for thing from UCSC
ahs <- query(ah, "UCSC")Then you can look for Genome values that match ‘hg19’ and a species that matches ‘Homo sapiens’.
ahs <- subset(ahs, ahs$genome=='hg19')
length(ahs)## [1] 5909ahs <- subset(ahs, ahs$species=='Homo sapiens')
length(ahs)## [1] 5909You might notice that the last two filtering steps are redundant (IOW doing the 1st of them is the same as doing both of them.) If this were not the case, we might suspect that there was a problem with the metadata.
15.2 Exercise 2:
This pulls down the oreganno annotations. Which are described on the UCSC site thusly: “This track displays literature-curated regulatory regions, transcription factor binding sites, and regulatory polymorphisms from ORegAnno (Open Regulatory Annotation). For more detailed information on a particular regulatory element, follow the link to ORegAnno from the details page.”
ahs <- query(ah, 'oreganno')
ahs## AnnotationHub with 9 records
## # snapshotDate(): 2021-10-20
## # $dataprovider: Pazar, UCSC
## # $species: Saccharomyces cerevisiae, Homo sapiens, NA
## # $rdataclass: GRanges
## # additional mcols(): taxonomyid, genome, description,
## # coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
## # rdatapath, sourceurl, sourcetype
## # retrieve records with, e.g., 'object[["AH5087"]]'
##
## title
## AH5087 | ORegAnno
## AH5213 | ORegAnno
## AH7053 | ORegAnno
## AH7061 | ORegAnno
## AH22286 | pazar_ORegAnno_20120522.csv
## AH22287 | pazar_ORegAnno_ENCODEprom_20120522.csv
## AH22288 | pazar_ORegAnno_Erythroid_20120522.csv
## AH22289 | pazar_ORegAnno_STAT1_ChIP_20120522.csv
## AH22290 | pazar_ORegAnno_STAT1_lit_20120522.csvahs[1]## AnnotationHub with 1 record
## # snapshotDate(): 2021-10-20
## # names(): AH5087
## # $dataprovider: UCSC
## # $species: Homo sapiens
## # $rdataclass: GRanges
## # $rdatadateadded: 2013-03-26
## # $title: ORegAnno
## # $description: GRanges object from UCSC track 'ORegAnno'
## # $taxonomyid: 9606
## # $genome: hg19
## # $sourcetype: UCSC track
## # $sourceurl: rtracklayer://hgdownload.cse.ucsc.edu/goldenpath/hg19/database...
## # $sourcesize: NA
## # $tags: c("oreganno", "UCSC", "track", "Gene", "Transcript",
## # "Annotation")
## # retrieve record with 'object[["AH5087"]]'oreg <- ahs[['AH5087']]## loading from cacheoreg## GRanges object with 23118 ranges and 2 metadata columns:
## seqnames ranges strand | name score
## <Rle> <IRanges> <Rle> | <character> <numeric>
## [1] chr1 873499-873849 + | OREG0012989 0
## [2] chr1 886764-887214 + | OREG0012990 0
## [3] chr1 886938-886958 + | OREG0007909 0
## [4] chr1 919400-919950 + | OREG0012991 0
## [5] chr1 919695-919715 + | OREG0007910 0
## ... ... ... ... . ... ...
## [23114] chr7_gl000195_random 1-851 + | OREG0026736 0
## [23115] chr7_gl000195_random 103427-103447 + | OREG0012963 0
## [23116] chr7_gl000195_random 121139-121159 + | OREG0012964 0
## [23117] chr17_gl000204_random 58370-58955 + | OREG0026769 0
## [23118] chr17_gl000205_random 117492-118442 + | OREG0026772 0
## -------
## seqinfo: 93 sequences (1 circular) from hg19 genome15.3 Exercise 3:
keys <- "MSX2"
columns <- c("ENTREZID", "CHR")
select(org.Hs.eg.db, keys, columns, keytype="SYMBOL")## Warning in .deprecatedColsMessage(): Accessing gene location information via 'CHR','CHRLOC','CHRLOCEND' is
## deprecated. Please use a range based accessor like genes(), or select()
## with columns values like TXCHROM and TXSTART on a TxDb or OrganismDb
## object instead.## 'select()' returned 1:1 mapping between keys and columns## SYMBOL ENTREZID CHR
## 1 MSX2 4488 515.4 Exercise 4:
## 1st get all the gene symbols
orgSymbols <- keys(org.Hs.eg.db, keytype="SYMBOL")
## and then use that to get all gene symbols matched to all entrez gene IDs
egr <- select(org.Hs.eg.db, keys=orgSymbols, "ENTREZID", "SYMBOL")## 'select()' returned 1:many mapping between keys and columnslength(egr$ENTREZID)## [1] 63901length(unique(egr$ENTREZID))## [1] 63901## VS:
length(egr$SYMBOL)## [1] 63901length(unique(egr$SYMBOL))## [1] 63891## So lets trap these symbols that are redundant and look more closely...
redund <- egr$SYMBOL
badSymbols <- redund[duplicated(redund)]
select(org.Hs.eg.db, badSymbols, "ENTREZID", "SYMBOL")## 'select()' returned many:many mapping between keys and columns## SYMBOL ENTREZID
## 1 HBD 3045
## 2 HBD 100187828
## 3 RNR1 4549
## 4 RNR1 6052
## 5 RNR2 4550
## 6 RNR2 6053
## 7 TEC 7006
## 8 TEC 100124696
## 9 MEMO1 7795
## 10 MEMO1 51072
## 11 MMD2 221938
## 12 MMD2 100505381
## 13 TMSB15B 286527
## 14 TMSB15B 122394733
## 15 DEL11P13 100528024
## 16 DEL11P13 107648861
## 17 TRNAV-CAC 107985614
## 18 TRNAV-CAC 107985615
## 19 TRNAV-CAC 107985753
## 20 TRNAV-CAC 107985614
## 21 TRNAV-CAC 107985615
## 22 TRNAV-CAC 10798575315.5 Exercise 5:
So to retrieve this information using select you need to do it like this:
res1 <- select(TxDb.Hsapiens.UCSC.hg19.knownGene,
keys(TxDb.Hsapiens.UCSC.hg19.knownGene, keytype="TXID"),
columns=c("GENEID","TXNAME","TXCHROM"), keytype="TXID")## 'select()' returned 1:1 mapping between keys and columnshead(res1)## TXID GENEID TXNAME TXCHROM
## 1 1 100287102 uc001aaa.3 chr1
## 2 2 100287102 uc010nxq.1 chr1
## 3 3 100287102 uc010nxr.1 chr1
## 4 4 79501 uc001aal.1 chr1
## 5 5 <NA> uc001aaq.2 chr1
## 6 6 <NA> uc001aar.2 chr1And to do it using transcripts you do it like this:
res2 <- transcripts(TxDb.Hsapiens.UCSC.hg19.knownGene,
columns = c("gene_id","tx_name"))
head(res2)## GRanges object with 6 ranges and 2 metadata columns:
## seqnames ranges strand | gene_id tx_name
## <Rle> <IRanges> <Rle> | <CharacterList> <character>
## [1] chr3 238279-451097 + | 10752 uc003bot.3
## [2] chr3 238279-451097 + | 10752 uc003bou.3
## [3] chr3 239326-290282 + | 10752 uc003bov.2
## [4] chr3 239326-440831 + | 10752 uc003bow.2
## [5] chr3 361366-451097 + | 10752 uc011asi.2
## [6] chr3 577914-887698 + | uc003boy.1
## -------
## seqinfo: 2 sequences from hg19 genomeNotice that in the 2nd case we don’t have to ask for the chromosome, as transcripts() returns a GRanges object, so the chromosome will automatically be returned as part of the object.
15.6 Exercise 6:
res <- transcripts(TxDb.Athaliana.BioMart.plantsmart22, columns = c("gene_id"))You will notice that the gene ids for this package are TAIR locus IDs and are NOT entrez gene IDs like what you saw in the TxDb.Hsapiens.UCSC.hg19.knownGene package. It’s important to always pay attention to the kind of gene id is being used by the TxDb you are looking at.
15.7 Exercise 7:
keys <- keys(Homo.sapiens, keytype="TXID")
res1 <- select(Homo.sapiens,
keys= keys,
columns=c("SYMBOL","TXSTART","TXCHROM"), keytype="TXID")
head(res1)And to do it using transcripts you do it like this:
res2 <- transcripts(Homo.sapiens, columns="SYMBOL")## 'select()' returned 1:1 mapping between keys and columnshead(res2)## GRanges object with 6 ranges and 1 metadata column:
## seqnames ranges strand | SYMBOL
## <Rle> <IRanges> <Rle> | <CharacterList>
## [1] chr3 238279-451097 + | CHL1
## [2] chr3 238279-451097 + | CHL1
## [3] chr3 239326-290282 + | CHL1
## [4] chr3 239326-440831 + | CHL1
## [5] chr3 361366-451097 + | CHL1
## [6] chr3 577914-887698 + | <NA>
## -------
## seqinfo: 2 sequences from hg19 genome15.8 Exercise 8:
columns(Homo.sapiens)## [1] "ACCNUM" "ALIAS" "CDSCHROM" "CDSEND" "CDSID"
## [6] "CDSNAME" "CDSSTART" "CDSSTRAND" "DEFINITION" "ENSEMBL"
## [11] "ENSEMBLPROT" "ENSEMBLTRANS" "ENTREZID" "ENZYME" "EVIDENCE"
## [16] "EVIDENCEALL" "EXONCHROM" "EXONEND" "EXONID" "EXONNAME"
## [21] "EXONRANK" "EXONSTART" "EXONSTRAND" "GENEID" "GENENAME"
## [26] "GENETYPE" "GO" "GOALL" "GOID" "IPI"
## [31] "MAP" "OMIM" "ONTOLOGY" "ONTOLOGYALL" "PATH"
## [36] "PFAM" "PMID" "PROSITE" "REFSEQ" "SYMBOL"
## [41] "TERM" "TXCHROM" "TXEND" "TXID" "TXNAME"
## [46] "TXSTART" "TXSTRAND" "TXTYPE" "UCSCKG" "UNIPROT"columns(org.Hs.eg.db)## [1] "ACCNUM" "ALIAS" "ENSEMBL" "ENSEMBLPROT" "ENSEMBLTRANS"
## [6] "ENTREZID" "ENZYME" "EVIDENCE" "EVIDENCEALL" "GENENAME"
## [11] "GENETYPE" "GO" "GOALL" "IPI" "MAP"
## [16] "OMIM" "ONTOLOGY" "ONTOLOGYALL" "PATH" "PFAM"
## [21] "PMID" "PROSITE" "REFSEQ" "SYMBOL" "UCSCKG"
## [26] "UNIPROT"columns(TxDb.Hsapiens.UCSC.hg19.knownGene)## [1] "CDSCHROM" "CDSEND" "CDSID" "CDSNAME" "CDSSTART"
## [6] "CDSSTRAND" "EXONCHROM" "EXONEND" "EXONID" "EXONNAME"
## [11] "EXONRANK" "EXONSTART" "EXONSTRAND" "GENEID" "TXCHROM"
## [16] "TXEND" "TXID" "TXNAME" "TXSTART" "TXSTRAND"
## [21] "TXTYPE"## You might also want to look at this:
transcripts(Homo.sapiens, columns=c("SYMBOL","CHRLOC"))## 'select()' returned 1:1 mapping between keys and columns## GRanges object with 5506 ranges and 1 metadata column:
## seqnames ranges strand | SYMBOL
## <Rle> <IRanges> <Rle> | <CharacterList>
## [1] chr3 238279-451097 + | CHL1
## [2] chr3 238279-451097 + | CHL1
## [3] chr3 239326-290282 + | CHL1
## [4] chr3 239326-440831 + | CHL1
## [5] chr3 361366-451097 + | CHL1
## ... ... ... ... . ...
## [5502] chr18 77732867-77748532 - | TXNL4A
## [5503] chr18 77732867-77748532 - | TXNL4A
## [5504] chr18 77732867-77793915 - | TXNL4A
## [5505] chr18 77915117-78005397 - | PARD6G
## [5506] chr18 77941005-78005397 - | PARD6G
## -------
## seqinfo: 2 sequences from hg19 genomeThe key difference is that the TXSTART refers to the start of a transcript and originates in the TxDb object from the TxDb.Hsapiens.UCSC.hg19.knownGene package, while the CHRLOC refers to the same thing but originates in the OrgDb object from the org.Hs.eg.db package. The point of origin is significant because the TxDb object represents a transcriptome from UCSC and the OrgDb is primarily gene centric data that originates at NCBI. The upshot is that CHRLOC will not have as many regions represented as TXSTART, since there has to be an official gene for there to even be a record. The CHRLOC data is also locked in for org.Hs.eg.db as data for hg19, whereas you can swap in a different TxDb object to match the genome you are using to make it hg18 etc. For these reasons, we strongly recommend using TXSTART instead of CHRLOC. Howeverm CHRLOC still remains in the org packages for historical reasons.
15.9 Exercise 9:
To find the keys that match, make use of the pattern and column arguments.
xk = head(keys(Homo.sapiens, keytype="ENTREZID", pattern="X", column="SYMBOL"))## 'select()' returned 1:1 mapping between keys and columnsxk## [1] "51" "179" "189" "239" "240" "241"select verifies the results
select(Homo.sapiens, xk, "SYMBOL", "ENTREZID")## 'select()' returned 1:1 mapping between keys and columns## ENTREZID SYMBOL
## 1 51 ACOX1
## 2 179 AGMX2
## 3 189 AGXT
## 4 239 ALOX12
## 5 240 ALOX5
## 6 241 ALOX5AP15.10 Exercise 10:
## Get the transcript ranges grouped by gene
txby <- transcriptsBy(Homo.sapiens, by="gene")
## look up the entrez ID for the gene symbol 'PTEN'
select(Homo.sapiens, keys='PTEN', columns='ENTREZID', keytype='SYMBOL')
## subset that genes transcripts
geneOfInterest <- txby[["5728"]]
## extract the sequence
res <- getSeq(Hsapiens, geneOfInterest)
res15.11 Exercise 11:
ensembl <- useEnsembl("ensembl",dataset="hsapiens_gene_ensembl")
ids=c("1")
getBM(attributes=c('go_id', 'entrezgene_id'),
filters = 'entrezgene_id',
values = ids, mart = ensembl)## go_id entrezgene_id
## 1 1
## 2 GO:0110165 1
## 3 GO:0005576 1
## 4 GO:0005615 1
## 5 GO:0070062 1
## 6 GO:0003674 1
## 7 GO:0008150 1
## 8 GO:0072562 1
## 9 GO:0062023 1
## 10 GO:0034774 1
## 11 GO:1904813 1
## 12 GO:0031093 115.12 Exercise 12:
ids=c("1")
select(org.Hs.eg.db, keys=ids, columns="GO", keytype="ENTREZID")## 'select()' returned 1:many mapping between keys and columns## ENTREZID GO EVIDENCE ONTOLOGY
## 1 1 GO:0003674 ND MF
## 2 1 GO:0005576 HDA CC
## 3 1 GO:0005576 IDA CC
## 4 1 GO:0005576 TAS CC
## 5 1 GO:0005615 HDA CC
## 6 1 GO:0008150 ND BP
## 7 1 GO:0031093 TAS CC
## 8 1 GO:0034774 TAS CC
## 9 1 GO:0062023 HDA CC
## 10 1 GO:0070062 HDA CC
## 11 1 GO:0072562 HDA CC
## 12 1 GO:1904813 TAS CCWhen this exercise was written, there was a different number of GO terms returned from biomaRt than from org.Hs.eg.db. This may not always be true in the future though as both of these resources are updated. It is expected however that this web service, (which is updated continuously) will fall in and out of sync with the org.Hs.eg.db package (which is updated twice a year). This is an important difference as each approach has different advantages and disadvantages. The advantage to updating continuously is that you always have the very latest annotations which are frequently different for something like GO terms. The advantage to using a package is that the results are frozen to a release of Bioconductor. And this can help you to get the same answers that you get today (reproducibility), a few years from now.
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