Contents

1 Version Info

R version: R version 4.4.0 RC (2024-04-16 r86468)
Bioconductor version: 3.19
Package version: 1.29.0

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:

ah <- 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 71308 records
## # snapshotDate(): 2024-04-29
## # $dataprovider: Ensembl, BroadInstitute, UCSC, ftp://ftp.ncbi.nlm.nih.gov/g...
## # $species: Homo sapiens, Mus musculus, Drosophila melanogaster, Rattus norv...
## # $rdataclass: GRanges, TwoBitFile, BigWigFile, EnsDb, Rle, OrgDb, SQLiteFil...
## # 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                          
##   ...        ...                                   
##   AH116725 | TENET_consensus_open_chromatin_regions
##   AH116726 | TENET_consensus_promoter_regions      
##   AH116727 | ENCODE_dELS_regions                   
##   AH116728 | ENCODE_pELS_regions                   
##   AH116729 | ENCODE_PLS_regions

As 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] "Stanford"                                                                                                  
## [17] "dbSNP"                                                                                                     
## [18] "BioMart"                                                                                                   
## [19] "GeneOntology"                                                                                              
## [20] "KEGG"                                                                                                      
## [21] "URGI"                                                                                                      
## [22] "EMBL-EBI"                                                                                                  
## [23] "MicrosporidiaDB"                                                                                           
## [24] "FungiDB"                                                                                                   
## [25] "TriTrypDB"                                                                                                 
## [26] "ToxoDB"                                                                                                    
## [27] "AmoebaDB"                                                                                                  
## [28] "PlasmoDB"                                                                                                  
## [29] "PiroplasmaDB"                                                                                              
## [30] "CryptoDB"                                                                                                  
## [31] "TrichDB"                                                                                                   
## [32] "GiardiaDB"                                                                                                 
## [33] "The Gene Ontology Consortium"                                                                              
## [34] "ENCODE Project"                                                                                            
## [35] "SchistoDB"                                                                                                 
## [36] "NCBI/UniProt"                                                                                              
## [37] "GENCODE"                                                                                                   
## [38] "http://www.pantherdb.org"                                                                                  
## [39] "RMBase v2.0"                                                                                               
## [40] "snoRNAdb"                                                                                                  
## [41] "tRNAdb"                                                                                                    
## [42] "NCBI"                                                                                                      
## [43] "DrugAge, DrugBank, Broad Institute"                                                                        
## [44] "DrugAge"                                                                                                   
## [45] "DrugBank"                                                                                                  
## [46] "Broad Institute"                                                                                           
## [47] "HMDB, EMBL-EBI, EPA"                                                                                       
## [48] "STRING"                                                                                                    
## [49] "OMA"                                                                                                       
## [50] "OrthoDB"                                                                                                   
## [51] "PathBank"                                                                                                  
## [52] "EBI/EMBL"                                                                                                  
## [53] "NCBI,DBCLS"                                                                                                
## [54] "FANTOM5,DLRP,IUPHAR,HPRD,STRING,SWISSPROT,TREMBL,ENSEMBL,CELLPHONEDB,BADERLAB,SINGLECELLSIGNALR,HOMOLOGENE"
## [55] "WikiPathways"                                                                                              
## [56] "VAST-TOOLS"                                                                                                
## [57] "pyGenomeTracks "                                                                                           
## [58] "NA"                                                                                                        
## [59] "UoE"                                                                                                       
## [60] "TargetScan,miRTarBase,USCS,ENSEMBL"                                                                        
## [61] "TargetScan"                                                                                                
## [62] "QuickGO"                                                                                                   
## [63] "CIS-BP"                                                                                                    
## [64] "CTCFBSDB 2.0"                                                                                              
## [65] "HOCOMOCO v11"                                                                                              
## [66] "JASPAR 2022"                                                                                               
## [67] "Jolma 2013"                                                                                                
## [68] "SwissRegulon"                                                                                              
## [69] "ENCODE SCREEN v3"                                                                                          
## [70] "MassBank"                                                                                                  
## [71] "excluderanges"                                                                                             
## [72] "ENCODE"                                                                                                    
## [73] "GitHub"                                                                                                    
## [74] "Stanford.edu"                                                                                              
## [75] "Publication"                                                                                               
## [76] "CHM13"                                                                                                     
## [77] "UCSChub"                                                                                                   
## [78] "MGI"                                                                                                       
## [79] "The Human Phenotype Ontology"                                                                              
## [80] "Google DeepMind"                                                                                           
## [81] "UWashington"                                                                                               
## [82] "ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/"                                                                     
## [83] "Bioconductor"

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] "mzRident"                          "list"                             
## [13] "TxDb"                              "Rle"                              
## [15] "EnsDb"                             "VcfFile"                          
## [17] "igraph"                            "data.frame, DNAStringSet, GRanges"
## [19] "sqlite"                            "data.table"                       
## [21] "character"                         "SQLite"                           
## [23] "SQLiteFile"                        "Tibble"                           
## [25] "Rda"                               "FaFile"                           
## [27] "String"                            "CompDb"                           
## [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 30539 records
## # snapshotDate(): 2024-04-29
## # $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                          
##   ...        ...                                   
##   AH116725 | TENET_consensus_open_chromatin_regions
##   AH116726 | TENET_consensus_promoter_regions      
##   AH116727 | ENCODE_dELS_regions                   
##   AH116728 | ENCODE_pELS_regions                   
##   AH116729 | ENCODE_PLS_regions

Or 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 1987 records
## # snapshotDate(): 2024-04-29
## # $dataprovider: ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/
## # $species: Escherichia coli, greater Indian_fruit_bat, Zootoca vivipara, Zo...
## # $rdataclass: OrgDb
## # additional mcols(): taxonomyid, genome, description,
## #   coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
## #   rdatapath, sourceurl, sourcetype 
## # retrieve records with, e.g., 'object[["AH114195"]]' 
## 
##              title                                          
##   AH114195 | org.Triticum_aestivum.eg.sqlite                
##   AH114196 | org.Triticum_aestivum_subsp._aestivum.eg.sqlite
##   AH114197 | org.Triticum_vulgare.eg.sqlite                 
##   AH114198 | org.Brassica_napus.eg.sqlite                   
##   AH114199 | org.Arachis_hypogaea.eg.sqlite                 
##   ...        ...                                            
##   AH116714 | org.Mmu.eg.db.sqlite                           
##   AH116715 | org.Ce.eg.db.sqlite                            
##   AH116716 | org.Xl.eg.db.sqlite                            
##   AH116717 | org.Sc.sgd.db.sqlite                           
##   AH116718 | org.Dr.eg.db.sqlite

And 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 cache
head(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 genome

4.1 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.

[ Back to top ]

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.

dogquery <- query(orgs, c("Canis familiaris", "9615"))
dogquery
## AnnotationHub with 1 record
## # snapshotDate(): 2024-04-29
## # names(): AH116704
## # $dataprovider: ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/
## # $species: Canis familiaris
## # $rdataclass: OrgDb
## # $rdatadateadded: 2024-04-02
## # $title: org.Cf.eg.db.sqlite
## # $description: NCBI gene ID based annotations about Canis familiaris
## # $taxonomyid: 9615
## # $genome: NCBI genomes
## # $sourcetype: NCBI/ensembl
## # $sourceurl: ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/, ftp://ftp.ensembl.org/p...
## # $sourcesize: NA
## # $tags: c("NCBI", "Gene", "Annotation") 
## # retrieve record with 'object[["AH116704"]]'
ah_id <- dogquery$ah_id
ah_id
## [1] "AH116704"
dog <- ah[[ah_id]]
## loading from cache
columns(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_008473

When 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:0005743      IEA       CC
## 3   804478 GO:0005751      IEA       CC
## 4   804478 GO:0006119      IEA       BP
## 5   804478 GO:0006123      IEA       BP
## 6   804478 GO:0015990      IEA       BP
## 7   804478 GO:0020037      IEA       MF
## 8   804478 GO:0045277      IEA       CC
## 9   804478 GO:0046872      IEA       MF

Because 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:0005743" "GO:0005751" "GO:0006119" "GO:0006123"
## [6] "GO:0015990" "GO:0020037" "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.

[ Back to top ]

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: 1.1

Just 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 genome

Similarly, 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   hg19

And 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  TRUE

But 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?

[ Back to top ]

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.refGene

Notice 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 3.45.2 [/tmp/Rtmpc3al55/file26103f4d57b800]
## tbls: id, id_accession, id_go, id_go_all, id_omim_pm, id_protein,
##   id_transcript, ranges_cds, ranges_exon, ranges_gene, ranges_tx

We 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 3.45.2 [/tmp/Rtmpc3al55/file26103f4d57b800]
## tbls: id, id_accession, id_go, id_go_all, id_omim_pm, id_protein,
##   id_transcript, ranges_cds, ranges_exon, ranges_gene, ranges_tx

The 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 with `by = join_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 ENST00000507785.2

Organism.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_name

The 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")
## GRanges object with 66 ranges and 3 metadata columns:
##        seqnames            ranges strand |     tx_id           tx_name
##           <Rle>         <IRanges>  <Rle> | <integer>       <character>
##    [1]     chr1 44775864-44775943      + |      3436 ENST00000581525.1
##    [2]     chr1 44776490-44776593      + |      3437 ENST00000364043.1
##    [3]     chr1 44777843-44777912      + |      3440 ENST00000365161.1
##    [4]     chr1 44778390-44778456      + |      3442 ENST00000384690.1
##    [5]     chr1 44778390-44778458      + |      3443 ENST00000625943.1
##    ...      ...               ...    ... .       ...               ...
##   [62]    chr20 44623752-44651678      - |    234171 ENST00000695997.1
##   [63]    chr20 44623972-44651718      - |    234172 ENST00000696009.1
##   [64]    chr20 44626323-44651661      - |    234173 ENST00000545776.5
##   [65]    chr20 44627547-44651720      - |    234174 ENST00000696010.1
##   [66]    chr20 44636071-44652233      - |    234175 ENST00000535573.1
##             symbol
##        <character>
##    [1]     SNORD55
##    [2]     SNORD46
##    [3]    SNORD38A
##    [4]    SNORD38B
##    [5]    SNORD38B
##    ...         ...
##   [62]         ADA
##   [63]         ADA
##   [64]         ADA
##   [65]         ADA
##   [66]         ADA
##   -------
##   seqinfo: 711 sequences (1 circular) from hg38 genome
transcripts_tbl(src, filter=~(symbol %startsWith% "SNORD" & gr) | symbol == "ADA")
## # A tibble: 66 × 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 +           3436 ENST00000581525.1 SNORD55 
##  2 chr1     44776490 44776593 +           3437 ENST00000364043.1 SNORD46 
##  3 chr1     44777843 44777912 +           3440 ENST00000365161.1 SNORD38A
##  4 chr1     44778390 44778456 +           3442 ENST00000384690.1 SNORD38B
##  5 chr1     44778390 44778458 +           3443 ENST00000625943.1 SNORD38B
##  6 chr20    44584896 44651702 -         234115 ENST00000696034.1 ADA     
##  7 chr20    44618605 44651745 -         234116 ENST00000537820.2 ADA     
##  8 chr20    44618618 44651699 -         234117 ENST00000696003.1 ADA     
##  9 chr20    44618625 44651699 -         234118 ENST00000696004.1 ADA     
## 10 chr20    44619521 44651678 -         234119 ENST00000695991.1 ADA     
## # ℹ 56 more rows

7.1 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’.

[ Back to top ]

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
## | BSgenome object for Human
## | - organism: Homo sapiens
## | - provider: UCSC
## | - genome: hg19
## | - release date: June 2013
## | - 298 sequence(s):
## |     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                                               
## | 
## | Tips: call 'seqnames()' on the object to get all the sequence names, call
## | 'seqinfo()' to get the full sequence info, use the '$' or '[[' operator to
## | access a given sequence, see '?BSgenome' for more information.

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 chr2

Whereas 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)
res

Additionally, 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.

[ Back to top ]

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. Historically these marts were maintained by various projects around the world, however the majority are now maintained as part of Ensembl and we’ll focus on that resource here. If you wish to access another BioMart instance see the biomaRt vignette Using a BioMart other than Ensembl.

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 “genes” mart.

listEnsembl()
##         biomart                version
## 1         genes      Ensembl Genes 111
## 2 mouse_strains      Mouse strains 111
## 3          snps  Ensembl Variation 111
## 4    regulation Ensembl Regulation 111
ensembl <- useEnsembl(biomart = "genes")
## Ensembl site unresponsive, trying asia mirror
ensembl
## Object of class 'Mart':
##   Using the ENSEMBL_MART_ENSEMBL BioMart database
##   No dataset selected.

Each ‘mart’ can contain datasets for multiple different things. In our example here the “genes” mart contains separate datasets for a large number of organisms. 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 ASM200744v2
ensembl <- useEnsembl(biomart="genes", dataset="hsapiens_gene_ensembl")
ensembl
## Object of class 'Mart':
##   Using the ENSEMBL_MART_ENSEMBL BioMart database
##   Using the hsapiens_gene_ensembl dataset

Next 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_page

And 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              14

Attributes 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. Just as there is an accessor for attributes, there is also an accessor to list all available 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 Start

So 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 NCBI Gene (formerly called ‘entrezgene’) IDs that are found on chromosome 1 of humans:

res <- getBM(attributes = c("hgnc_symbol", "entrezgene_id"),
             filters = "chromosome_name",
             values = "1", 
             mart = ensembl)
head(res)
##   hgnc_symbol entrezgene_id
## 1                 102725121
## 2                 100287596
## 3                 100287102
## 4                    727856
## 5                     84771
## 6     DDX11L1            NA

Of course you may have noticed that a lot of the arguments for getBM are very similar to what you do when working with OrgDb objects. So if it’s your preference you can also use the standard select(), columns(), keytypes() etc 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?

[ Back to top ]

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 4.4.0 RC (2024-04-16 r86468)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.20-bioc/R/lib/libRblas.so 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
## 
## 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       
## 
## time zone: America/New_York
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] annotation_1.29.0                        
##  [2] TxDb.Athaliana.BioMart.plantsmart22_3.0.1
##  [3] biomaRt_2.59.1                           
##  [4] BSgenome.Hsapiens.UCSC.hg19_1.4.3        
##  [5] BSgenome_1.71.4                          
##  [6] rtracklayer_1.63.3                       
##  [7] BiocIO_1.13.1                            
##  [8] Homo.sapiens_1.3.1                       
##  [9] GO.db_3.19.1                             
## [10] OrganismDbi_1.45.1                       
## [11] org.Mm.eg.db_3.19.1                      
## [12] org.Hs.eg.db_3.19.1                      
## [13] TxDb.Mmusculus.UCSC.mm10.ensGene_3.4.0   
## [14] TxDb.Hsapiens.UCSC.hg38.knownGene_3.18.0 
## [15] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2  
## [16] GenomicFeatures_1.55.4                   
## [17] AnnotationDbi_1.65.2                     
## [18] Organism.dplyr_1.31.1                    
## [19] AnnotationFilter_1.27.0                  
## [20] dplyr_1.1.4                              
## [21] AnnotationHub_3.11.5                     
## [22] BiocFileCache_2.11.2                     
## [23] dbplyr_2.5.0                             
## [24] VariantAnnotation_1.49.7                 
## [25] Rsamtools_2.19.4                         
## [26] Biostrings_2.71.6                        
## [27] XVector_0.43.1                           
## [28] SummarizedExperiment_1.33.3              
## [29] Biobase_2.63.1                           
## [30] GenomicRanges_1.55.4                     
## [31] GenomeInfoDb_1.39.14                     
## [32] IRanges_2.37.1                           
## [33] S4Vectors_0.41.7                         
## [34] MatrixGenerics_1.15.1                    
## [35] matrixStats_1.3.0                        
## [36] BiocGenerics_0.49.1                      
## [37] BiocStyle_2.31.0                         
## 
## loaded via a namespace (and not attached):
##  [1] DBI_1.2.2                bitops_1.0-7             RBGL_1.79.0             
##  [4] httr2_1.0.1              rlang_1.1.3              magrittr_2.0.3          
##  [7] compiler_4.4.0           RSQLite_2.3.6            png_0.1-8               
## [10] vctrs_0.6.5              txdbmaker_0.99.9         stringr_1.5.1           
## [13] pkgconfig_2.0.3          crayon_1.5.2             fastmap_1.1.1           
## [16] utf8_1.2.4               rmarkdown_2.26           graph_1.81.1            
## [19] UCSC.utils_0.99.7        purrr_1.0.2              bit_4.0.5               
## [22] xfun_0.43                zlibbioc_1.49.3          cachem_1.0.8            
## [25] jsonlite_1.8.8           progress_1.2.3           blob_1.2.4              
## [28] DelayedArray_0.29.9      BiocParallel_1.37.1      parallel_4.4.0          
## [31] prettyunits_1.2.0        R6_2.5.1                 bslib_0.7.0             
## [34] stringi_1.8.3            jquerylib_0.1.4          bookdown_0.39           
## [37] knitr_1.46               Matrix_1.7-0             tidyselect_1.2.1        
## [40] abind_1.4-5              yaml_2.3.8               codetools_0.2-20        
## [43] curl_5.2.1               lattice_0.22-6           tibble_3.2.1            
## [46] withr_3.0.0              KEGGREST_1.43.1          evaluate_0.23           
## [49] xml2_1.3.6               pillar_1.9.0             BiocManager_1.30.22     
## [52] filelock_1.0.3           generics_0.1.3           RCurl_1.98-1.14         
## [55] BiocVersion_3.19.1       hms_1.1.3                glue_1.7.0              
## [58] lazyeval_0.2.2           tools_4.4.0              GenomicAlignments_1.39.5
## [61] XML_3.99-0.16.1          grid_4.4.0               GenomeInfoDbData_1.2.12 
## [64] restfulr_0.0.15          cli_3.6.2                rappdirs_0.3.3          
## [67] fansi_1.0.6              S4Arrays_1.3.7           sass_0.4.9              
## [70] digest_0.6.35            SparseArray_1.3.7        rjson_0.2.21            
## [73] memoise_2.0.1            htmltools_0.5.8.1        lifecycle_1.0.4         
## [76] httr_1.4.7               mime_0.12                bit64_4.0.5

13 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

Appendix

  1. 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

  2. Pages H, Carlson M, Falcon S and Li N. AnnotationDbi: Annotation Database Interface. R package version 1.30.0.

  3. 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.

  4. 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

  5. Steffen Durinck, Wolfgang Huber biomaRt: Interface to BioMart databases (e.g. Ensembl, COSMIC ,Wormbase and Gramene) version 2.23.5.

  6. 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.

  7. 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.

  8. Morgan M, Carlson M, Tenenbaum D and Arora S. AnnotationHub: Client to access AnnotationHub resources. R package version 2.0.1.

  9. 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.

  10. Pages H. BSgenome: Infrastructure for Biostrings-based genome data packages. R package version 1.36.0.

  11. Pages H, Aboyoun P, Gentleman R and DebRoy S. Biostrings: String objects representing biological sequences, and matching algorithms. R package version 2.36.0.

  12. Carlson M, and Pages H. AnnotationForge: Code for Building Annotation Database Packages. R package version 1.10.0.

A Answers for exercises

A.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$genome=='hg19')
length(ahs)
## [1] 5908
ahs <- subset(ahs, ahs$species=='Homo sapiens')
length(ahs)
## [1] 5908

You 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.

A.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(): 2024-04-29
## # $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.csv
ahs[1]
## AnnotationHub with 1 record
## # snapshotDate(): 2024-04-29
## # 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 cache
oreg
## 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 genome

A.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   5

A.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 columns
length(egr$ENTREZID)
## [1] 193382
length(unique(egr$ENTREZID))
## [1] 193382
## VS:
length(egr$SYMBOL)
## [1] 193382
length(unique(egr$SYMBOL))
## [1] 193279
## 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         MMD2    221938
## 10        MMD2 100505381
## 11     DEL1P36 100240737
## 12     DEL1P36 123670537
## 13    DEL11P13 100528024
## 14    DEL11P13 107648861
## 15   TRNAV-CAC 107985614
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## 745  TRNAD-GUC 124905866
## 746  TRNAD-GUC 124905869
## 747  TRNAD-GUC 124905872
## 748  TRNAD-GUC 124905875
## 749  TRNAD-GUC 124905878
## 750  TRNAD-GUC 124905881
## 751  TRNAD-GUC 124905884
## 752  TRNAD-GUC 124905887
## 753  TRNAD-GUC 124905890
## 754  TRNAD-GUC 124905893
## 755  TRNAD-GUC 124905896
## 756  TRNAD-GUC 124905899
## 757  TRNAD-GUC 124905902
## 758  TRNAD-GUC 124905854
## 759  TRNAD-GUC 124905857
## 760  TRNAD-GUC 124905860
## 761  TRNAD-GUC 124905863
## 762  TRNAD-GUC 124905866
## 763  TRNAD-GUC 124905869
## 764  TRNAD-GUC 124905872
## 765  TRNAD-GUC 124905875
## 766  TRNAD-GUC 124905878
## 767  TRNAD-GUC 124905881
## 768  TRNAD-GUC 124905884
## 769  TRNAD-GUC 124905887
## 770  TRNAD-GUC 124905890
## 771  TRNAD-GUC 124905893
## 772  TRNAD-GUC 124905896
## 773  TRNAD-GUC 124905899
## 774  TRNAD-GUC 124905902
## 775  TRNAD-GUC 124905854
## 776  TRNAD-GUC 124905857
## 777  TRNAD-GUC 124905860
## 778  TRNAD-GUC 124905863
## 779  TRNAD-GUC 124905866
## 780  TRNAD-GUC 124905869
## 781  TRNAD-GUC 124905872
## 782  TRNAD-GUC 124905875
## 783  TRNAD-GUC 124905878
## 784  TRNAD-GUC 124905881
## 785  TRNAD-GUC 124905884
## 786  TRNAD-GUC 124905887
## 787  TRNAD-GUC 124905890
## 788  TRNAD-GUC 124905893
## 789  TRNAD-GUC 124905896
## 790  TRNAD-GUC 124905899
## 791  TRNAD-GUC 124905902
## 792  TRNAD-GUC 124905854
## 793  TRNAD-GUC 124905857
## 794  TRNAD-GUC 124905860
## 795  TRNAD-GUC 124905863
## 796  TRNAD-GUC 124905866
## 797  TRNAD-GUC 124905869
## 798  TRNAD-GUC 124905872
## 799  TRNAD-GUC 124905875
## 800  TRNAD-GUC 124905878
## 801  TRNAD-GUC 124905881
## 802  TRNAD-GUC 124905884
## 803  TRNAD-GUC 124905887
## 804  TRNAD-GUC 124905890
## 805  TRNAD-GUC 124905893
## 806  TRNAD-GUC 124905896
## 807  TRNAD-GUC 124905899
## 808  TRNAD-GUC 124905902
## 809  TRNAD-GUC 124905854
## 810  TRNAD-GUC 124905857
## 811  TRNAD-GUC 124905860
## 812  TRNAD-GUC 124905863
## 813  TRNAD-GUC 124905866
## 814  TRNAD-GUC 124905869
## 815  TRNAD-GUC 124905872
## 816  TRNAD-GUC 124905875
## 817  TRNAD-GUC 124905878
## 818  TRNAD-GUC 124905881
## 819  TRNAD-GUC 124905884
## 820  TRNAD-GUC 124905887
## 821  TRNAD-GUC 124905890
## 822  TRNAD-GUC 124905893
## 823  TRNAD-GUC 124905896
## 824  TRNAD-GUC 124905899
## 825  TRNAD-GUC 124905902
## 826  TRNAD-GUC 124905854
## 827  TRNAD-GUC 124905857
## 828  TRNAD-GUC 124905860
## 829  TRNAD-GUC 124905863
## 830  TRNAD-GUC 124905866
## 831  TRNAD-GUC 124905869
## 832  TRNAD-GUC 124905872
## 833  TRNAD-GUC 124905875
## 834  TRNAD-GUC 124905878
## 835  TRNAD-GUC 124905881
## 836  TRNAD-GUC 124905884
## 837  TRNAD-GUC 124905887
## 838  TRNAD-GUC 124905890
## 839  TRNAD-GUC 124905893
## 840  TRNAD-GUC 124905896
## 841  TRNAD-GUC 124905899
## 842  TRNAD-GUC 124905902
## 843  TRNAD-GUC 124905854
## 844  TRNAD-GUC 124905857
## 845  TRNAD-GUC 124905860
## 846  TRNAD-GUC 124905863
## 847  TRNAD-GUC 124905866
## 848  TRNAD-GUC 124905869
## 849  TRNAD-GUC 124905872
## 850  TRNAD-GUC 124905875
## 851  TRNAD-GUC 124905878
## 852  TRNAD-GUC 124905881
## 853  TRNAD-GUC 124905884
## 854  TRNAD-GUC 124905887
## 855  TRNAD-GUC 124905890
## 856  TRNAD-GUC 124905893
## 857  TRNAD-GUC 124905896
## 858  TRNAD-GUC 124905899
## 859  TRNAD-GUC 124905902
## 860  TRNAD-GUC 124905854
## 861  TRNAD-GUC 124905857
## 862  TRNAD-GUC 124905860
## 863  TRNAD-GUC 124905863
## 864  TRNAD-GUC 124905866
## 865  TRNAD-GUC 124905869
## 866  TRNAD-GUC 124905872
## 867  TRNAD-GUC 124905875
## 868  TRNAD-GUC 124905878
## 869  TRNAD-GUC 124905881
## 870  TRNAD-GUC 124905884
## 871  TRNAD-GUC 124905887
## 872  TRNAD-GUC 124905890
## 873  TRNAD-GUC 124905893
## 874  TRNAD-GUC 124905896
## 875  TRNAD-GUC 124905899
## 876  TRNAD-GUC 124905902
## 877  TRNAD-GUC 124905854
## 878  TRNAD-GUC 124905857
## 879  TRNAD-GUC 124905860
## 880  TRNAD-GUC 124905863
## 881  TRNAD-GUC 124905866
## 882  TRNAD-GUC 124905869
## 883  TRNAD-GUC 124905872
## 884  TRNAD-GUC 124905875
## 885  TRNAD-GUC 124905878
## 886  TRNAD-GUC 124905881
## 887  TRNAD-GUC 124905884
## 888  TRNAD-GUC 124905887
## 889  TRNAD-GUC 124905890
## 890  TRNAD-GUC 124905893
## 891  TRNAD-GUC 124905896
## 892  TRNAD-GUC 124905899
## 893  TRNAD-GUC 124905902
## 894  TRNAD-GUC 124905854
## 895  TRNAD-GUC 124905857
## 896  TRNAD-GUC 124905860
## 897  TRNAD-GUC 124905863
## 898  TRNAD-GUC 124905866
## 899  TRNAD-GUC 124905869
## 900  TRNAD-GUC 124905872
## 901  TRNAD-GUC 124905875
## 902  TRNAD-GUC 124905878
## 903  TRNAD-GUC 124905881
## 904  TRNAD-GUC 124905884
## 905  TRNAD-GUC 124905887
## 906  TRNAD-GUC 124905890
## 907  TRNAD-GUC 124905893
## 908  TRNAD-GUC 124905896
## 909  TRNAD-GUC 124905899
## 910  TRNAD-GUC 124905902
## 911  TRNAE-CUC 124905855
## 912  TRNAE-CUC 124905858
## 913  TRNAE-CUC 124905861
## 914  TRNAE-CUC 124905864
## 915  TRNAE-CUC 124905867
## 916  TRNAE-CUC 124905870
## 917  TRNAE-CUC 124905873
## 918  TRNAE-CUC 124905876
## 919  TRNAE-CUC 124905879
## 920  TRNAE-CUC 124905882
## 921  TRNAE-CUC 124905885
## 922  TRNAE-CUC 124905888
## 923  TRNAE-CUC 124905891
## 924  TRNAE-CUC 124905894
## 925  TRNAE-CUC 124905897
## 926  TRNAE-CUC 124905900
## 927  TRNAE-CUC 124905903
## 928  TRNAE-CUC 124905855
## 929  TRNAE-CUC 124905858
## 930  TRNAE-CUC 124905861
## 931  TRNAE-CUC 124905864
## 932  TRNAE-CUC 124905867
## 933  TRNAE-CUC 124905870
## 934  TRNAE-CUC 124905873
## 935  TRNAE-CUC 124905876
## 936  TRNAE-CUC 124905879
## 937  TRNAE-CUC 124905882
## 938  TRNAE-CUC 124905885
## 939  TRNAE-CUC 124905888
## 940  TRNAE-CUC 124905891
## 941  TRNAE-CUC 124905894
## 942  TRNAE-CUC 124905897
## 943  TRNAE-CUC 124905900
## 944  TRNAE-CUC 124905903
## 945  TRNAE-CUC 124905855
## 946  TRNAE-CUC 124905858
## 947  TRNAE-CUC 124905861
## 948  TRNAE-CUC 124905864
## 949  TRNAE-CUC 124905867
## 950  TRNAE-CUC 124905870
## 951  TRNAE-CUC 124905873
## 952  TRNAE-CUC 124905876
## 953  TRNAE-CUC 124905879
## 954  TRNAE-CUC 124905882
## 955  TRNAE-CUC 124905885
## 956  TRNAE-CUC 124905888
## 957  TRNAE-CUC 124905891
## 958  TRNAE-CUC 124905894
## 959  TRNAE-CUC 124905897
## 960  TRNAE-CUC 124905900
## 961  TRNAE-CUC 124905903
## 962  TRNAE-CUC 124905855
## 963  TRNAE-CUC 124905858
## 964  TRNAE-CUC 124905861
## 965  TRNAE-CUC 124905864
## 966  TRNAE-CUC 124905867
## 967  TRNAE-CUC 124905870
## 968  TRNAE-CUC 124905873
## 969  TRNAE-CUC 124905876
## 970  TRNAE-CUC 124905879
## 971  TRNAE-CUC 124905882
## 972  TRNAE-CUC 124905885
## 973  TRNAE-CUC 124905888
## 974  TRNAE-CUC 124905891
## 975  TRNAE-CUC 124905894
## 976  TRNAE-CUC 124905897
## 977  TRNAE-CUC 124905900
## 978  TRNAE-CUC 124905903
## 979  TRNAE-CUC 124905855
## 980  TRNAE-CUC 124905858
## 981  TRNAE-CUC 124905861
## 982  TRNAE-CUC 124905864
## 983  TRNAE-CUC 124905867
## 984  TRNAE-CUC 124905870
## 985  TRNAE-CUC 124905873
## 986  TRNAE-CUC 124905876
## 987  TRNAE-CUC 124905879
## 988  TRNAE-CUC 124905882
## 989  TRNAE-CUC 124905885
## 990  TRNAE-CUC 124905888
## 991  TRNAE-CUC 124905891
## 992  TRNAE-CUC 124905894
## 993  TRNAE-CUC 124905897
## 994  TRNAE-CUC 124905900
## 995  TRNAE-CUC 124905903
## 996  TRNAE-CUC 124905855
## 997  TRNAE-CUC 124905858
## 998  TRNAE-CUC 124905861
## 999  TRNAE-CUC 124905864
## 1000 TRNAE-CUC 124905867
## 1001 TRNAE-CUC 124905870
## 1002 TRNAE-CUC 124905873
## 1003 TRNAE-CUC 124905876
## 1004 TRNAE-CUC 124905879
## 1005 TRNAE-CUC 124905882
## 1006 TRNAE-CUC 124905885
## 1007 TRNAE-CUC 124905888
## 1008 TRNAE-CUC 124905891
## 1009 TRNAE-CUC 124905894
## 1010 TRNAE-CUC 124905897
## 1011 TRNAE-CUC 124905900
## 1012 TRNAE-CUC 124905903
## 1013 TRNAE-CUC 124905855
## 1014 TRNAE-CUC 124905858
## 1015 TRNAE-CUC 124905861
## 1016 TRNAE-CUC 124905864
## 1017 TRNAE-CUC 124905867
## 1018 TRNAE-CUC 124905870
## 1019 TRNAE-CUC 124905873
## 1020 TRNAE-CUC 124905876
## 1021 TRNAE-CUC 124905879
## 1022 TRNAE-CUC 124905882
## 1023 TRNAE-CUC 124905885
## 1024 TRNAE-CUC 124905888
## 1025 TRNAE-CUC 124905891
## 1026 TRNAE-CUC 124905894
## 1027 TRNAE-CUC 124905897
## 1028 TRNAE-CUC 124905900
## 1029 TRNAE-CUC 124905903
## 1030 TRNAE-CUC 124905855
## 1031 TRNAE-CUC 124905858
## 1032 TRNAE-CUC 124905861
## 1033 TRNAE-CUC 124905864
## 1034 TRNAE-CUC 124905867
## 1035 TRNAE-CUC 124905870
## 1036 TRNAE-CUC 124905873
## 1037 TRNAE-CUC 124905876
## 1038 TRNAE-CUC 124905879
## 1039 TRNAE-CUC 124905882
## 1040 TRNAE-CUC 124905885
## 1041 TRNAE-CUC 124905888
## 1042 TRNAE-CUC 124905891
## 1043 TRNAE-CUC 124905894
## 1044 TRNAE-CUC 124905897
## 1045 TRNAE-CUC 124905900
## 1046 TRNAE-CUC 124905903
## 1047 TRNAE-CUC 124905855
## 1048 TRNAE-CUC 124905858
## 1049 TRNAE-CUC 124905861
## 1050 TRNAE-CUC 124905864
## 1051 TRNAE-CUC 124905867
## 1052 TRNAE-CUC 124905870
## 1053 TRNAE-CUC 124905873
## 1054 TRNAE-CUC 124905876
## 1055 TRNAE-CUC 124905879
## 1056 TRNAE-CUC 124905882
## 1057 TRNAE-CUC 124905885
## 1058 TRNAE-CUC 124905888
## 1059 TRNAE-CUC 124905891
## 1060 TRNAE-CUC 124905894
## 1061 TRNAE-CUC 124905897
## 1062 TRNAE-CUC 124905900
## 1063 TRNAE-CUC 124905903
## 1064 TRNAE-CUC 124905855
## 1065 TRNAE-CUC 124905858
## 1066 TRNAE-CUC 124905861
## 1067 TRNAE-CUC 124905864
## 1068 TRNAE-CUC 124905867
## 1069 TRNAE-CUC 124905870
## 1070 TRNAE-CUC 124905873
## 1071 TRNAE-CUC 124905876
## 1072 TRNAE-CUC 124905879
## 1073 TRNAE-CUC 124905882
## 1074 TRNAE-CUC 124905885
## 1075 TRNAE-CUC 124905888
## 1076 TRNAE-CUC 124905891
## 1077 TRNAE-CUC 124905894
## 1078 TRNAE-CUC 124905897
## 1079 TRNAE-CUC 124905900
## 1080 TRNAE-CUC 124905903
## 1081 TRNAE-CUC 124905855
## 1082 TRNAE-CUC 124905858
## 1083 TRNAE-CUC 124905861
## 1084 TRNAE-CUC 124905864
## 1085 TRNAE-CUC 124905867
## 1086 TRNAE-CUC 124905870
## 1087 TRNAE-CUC 124905873
## 1088 TRNAE-CUC 124905876
## 1089 TRNAE-CUC 124905879
## 1090 TRNAE-CUC 124905882
## 1091 TRNAE-CUC 124905885
## 1092 TRNAE-CUC 124905888
## 1093 TRNAE-CUC 124905891
## 1094 TRNAE-CUC 124905894
## 1095 TRNAE-CUC 124905897
## 1096 TRNAE-CUC 124905900
## 1097 TRNAE-CUC 124905903
## 1098 TRNAE-CUC 124905855
## 1099 TRNAE-CUC 124905858
## 1100 TRNAE-CUC 124905861
## 1101 TRNAE-CUC 124905864
## 1102 TRNAE-CUC 124905867
## 1103 TRNAE-CUC 124905870
## 1104 TRNAE-CUC 124905873
## 1105 TRNAE-CUC 124905876
## 1106 TRNAE-CUC 124905879
## 1107 TRNAE-CUC 124905882
## 1108 TRNAE-CUC 124905885
## 1109 TRNAE-CUC 124905888
## 1110 TRNAE-CUC 124905891
## 1111 TRNAE-CUC 124905894
## 1112 TRNAE-CUC 124905897
## 1113 TRNAE-CUC 124905900
## 1114 TRNAE-CUC 124905903
## 1115 TRNAE-CUC 124905855
## 1116 TRNAE-CUC 124905858
## 1117 TRNAE-CUC 124905861
## 1118 TRNAE-CUC 124905864
## 1119 TRNAE-CUC 124905867
## 1120 TRNAE-CUC 124905870
## 1121 TRNAE-CUC 124905873
## 1122 TRNAE-CUC 124905876
## 1123 TRNAE-CUC 124905879
## 1124 TRNAE-CUC 124905882
## 1125 TRNAE-CUC 124905885
## 1126 TRNAE-CUC 124905888
## 1127 TRNAE-CUC 124905891
## 1128 TRNAE-CUC 124905894
## 1129 TRNAE-CUC 124905897
## 1130 TRNAE-CUC 124905900
## 1131 TRNAE-CUC 124905903
## 1132 TRNAE-CUC 124905855
## 1133 TRNAE-CUC 124905858
## 1134 TRNAE-CUC 124905861
## 1135 TRNAE-CUC 124905864
## 1136 TRNAE-CUC 124905867
## 1137 TRNAE-CUC 124905870
## 1138 TRNAE-CUC 124905873
## 1139 TRNAE-CUC 124905876
## 1140 TRNAE-CUC 124905879
## 1141 TRNAE-CUC 124905882
## 1142 TRNAE-CUC 124905885
## 1143 TRNAE-CUC 124905888
## 1144 TRNAE-CUC 124905891
## 1145 TRNAE-CUC 124905894
## 1146 TRNAE-CUC 124905897
## 1147 TRNAE-CUC 124905900
## 1148 TRNAE-CUC 124905903
## 1149 TRNAE-CUC 124905855
## 1150 TRNAE-CUC 124905858
## 1151 TRNAE-CUC 124905861
## 1152 TRNAE-CUC 124905864
## 1153 TRNAE-CUC 124905867
## 1154 TRNAE-CUC 124905870
## 1155 TRNAE-CUC 124905873
## 1156 TRNAE-CUC 124905876
## 1157 TRNAE-CUC 124905879
## 1158 TRNAE-CUC 124905882
## 1159 TRNAE-CUC 124905885
## 1160 TRNAE-CUC 124905888
## 1161 TRNAE-CUC 124905891
## 1162 TRNAE-CUC 124905894
## 1163 TRNAE-CUC 124905897
## 1164 TRNAE-CUC 124905900
## 1165 TRNAE-CUC 124905903
## 1166 TRNAE-CUC 124905855
## 1167 TRNAE-CUC 124905858
## 1168 TRNAE-CUC 124905861
## 1169 TRNAE-CUC 124905864
## 1170 TRNAE-CUC 124905867
## 1171 TRNAE-CUC 124905870
## 1172 TRNAE-CUC 124905873
## 1173 TRNAE-CUC 124905876
## 1174 TRNAE-CUC 124905879
## 1175 TRNAE-CUC 124905882
## 1176 TRNAE-CUC 124905885
## 1177 TRNAE-CUC 124905888
## 1178 TRNAE-CUC 124905891
## 1179 TRNAE-CUC 124905894
## 1180 TRNAE-CUC 124905897
## 1181 TRNAE-CUC 124905900
## 1182 TRNAE-CUC 124905903
## 1183 TRNAG-UCC 124905856
## 1184 TRNAG-UCC 124905859
## 1185 TRNAG-UCC 124905862
## 1186 TRNAG-UCC 124905865
## 1187 TRNAG-UCC 124905868
## 1188 TRNAG-UCC 124905871
## 1189 TRNAG-UCC 124905874
## 1190 TRNAG-UCC 124905877
## 1191 TRNAG-UCC 124905880
## 1192 TRNAG-UCC 124905883
## 1193 TRNAG-UCC 124905886
## 1194 TRNAG-UCC 124905889
## 1195 TRNAG-UCC 124905892
## 1196 TRNAG-UCC 124905895
## 1197 TRNAG-UCC 124905898
## 1198 TRNAG-UCC 124905901
## 1199 TRNAG-UCC 124905904
## 1200 TRNAG-UCC 124905856
## 1201 TRNAG-UCC 124905859
## 1202 TRNAG-UCC 124905862
## 1203 TRNAG-UCC 124905865
## 1204 TRNAG-UCC 124905868
## 1205 TRNAG-UCC 124905871
## 1206 TRNAG-UCC 124905874
## 1207 TRNAG-UCC 124905877
## 1208 TRNAG-UCC 124905880
## 1209 TRNAG-UCC 124905883
## 1210 TRNAG-UCC 124905886
## 1211 TRNAG-UCC 124905889
## 1212 TRNAG-UCC 124905892
## 1213 TRNAG-UCC 124905895
## 1214 TRNAG-UCC 124905898
## 1215 TRNAG-UCC 124905901
## 1216 TRNAG-UCC 124905904
## 1217 TRNAG-UCC 124905856
## 1218 TRNAG-UCC 124905859
## 1219 TRNAG-UCC 124905862
## 1220 TRNAG-UCC 124905865
## 1221 TRNAG-UCC 124905868
## 1222 TRNAG-UCC 124905871
## 1223 TRNAG-UCC 124905874
## 1224 TRNAG-UCC 124905877
## 1225 TRNAG-UCC 124905880
## 1226 TRNAG-UCC 124905883
## 1227 TRNAG-UCC 124905886
## 1228 TRNAG-UCC 124905889
## 1229 TRNAG-UCC 124905892
## 1230 TRNAG-UCC 124905895
## 1231 TRNAG-UCC 124905898
## 1232 TRNAG-UCC 124905901
## 1233 TRNAG-UCC 124905904
## 1234 TRNAG-UCC 124905856
## 1235 TRNAG-UCC 124905859
## 1236 TRNAG-UCC 124905862
## 1237 TRNAG-UCC 124905865
## 1238 TRNAG-UCC 124905868
## 1239 TRNAG-UCC 124905871
## 1240 TRNAG-UCC 124905874
## 1241 TRNAG-UCC 124905877
## 1242 TRNAG-UCC 124905880
## 1243 TRNAG-UCC 124905883
## 1244 TRNAG-UCC 124905886
## 1245 TRNAG-UCC 124905889
## 1246 TRNAG-UCC 124905892
## 1247 TRNAG-UCC 124905895
## 1248 TRNAG-UCC 124905898
## 1249 TRNAG-UCC 124905901
## 1250 TRNAG-UCC 124905904
## 1251 TRNAG-UCC 124905856
## 1252 TRNAG-UCC 124905859
## 1253 TRNAG-UCC 124905862
## 1254 TRNAG-UCC 124905865
## 1255 TRNAG-UCC 124905868
## 1256 TRNAG-UCC 124905871
## 1257 TRNAG-UCC 124905874
## 1258 TRNAG-UCC 124905877
## 1259 TRNAG-UCC 124905880
## 1260 TRNAG-UCC 124905883
## 1261 TRNAG-UCC 124905886
## 1262 TRNAG-UCC 124905889
## 1263 TRNAG-UCC 124905892
## 1264 TRNAG-UCC 124905895
## 1265 TRNAG-UCC 124905898
## 1266 TRNAG-UCC 124905901
## 1267 TRNAG-UCC 124905904
## 1268 TRNAG-UCC 124905856
## 1269 TRNAG-UCC 124905859
## 1270 TRNAG-UCC 124905862
## 1271 TRNAG-UCC 124905865
## 1272 TRNAG-UCC 124905868
## 1273 TRNAG-UCC 124905871
## 1274 TRNAG-UCC 124905874
## 1275 TRNAG-UCC 124905877
## 1276 TRNAG-UCC 124905880
## 1277 TRNAG-UCC 124905883
## 1278 TRNAG-UCC 124905886
## 1279 TRNAG-UCC 124905889
## 1280 TRNAG-UCC 124905892
## 1281 TRNAG-UCC 124905895
## 1282 TRNAG-UCC 124905898
## 1283 TRNAG-UCC 124905901
## 1284 TRNAG-UCC 124905904
## 1285 TRNAG-UCC 124905856
## 1286 TRNAG-UCC 124905859
## 1287 TRNAG-UCC 124905862
## 1288 TRNAG-UCC 124905865
## 1289 TRNAG-UCC 124905868
## 1290 TRNAG-UCC 124905871
## 1291 TRNAG-UCC 124905874
## 1292 TRNAG-UCC 124905877
## 1293 TRNAG-UCC 124905880
## 1294 TRNAG-UCC 124905883
## 1295 TRNAG-UCC 124905886
## 1296 TRNAG-UCC 124905889
## 1297 TRNAG-UCC 124905892
## 1298 TRNAG-UCC 124905895
## 1299 TRNAG-UCC 124905898
## 1300 TRNAG-UCC 124905901
## 1301 TRNAG-UCC 124905904
## 1302 TRNAG-UCC 124905856
## 1303 TRNAG-UCC 124905859
## 1304 TRNAG-UCC 124905862
## 1305 TRNAG-UCC 124905865
## 1306 TRNAG-UCC 124905868
## 1307 TRNAG-UCC 124905871
## 1308 TRNAG-UCC 124905874
## 1309 TRNAG-UCC 124905877
## 1310 TRNAG-UCC 124905880
## 1311 TRNAG-UCC 124905883
## 1312 TRNAG-UCC 124905886
## 1313 TRNAG-UCC 124905889
## 1314 TRNAG-UCC 124905892
## 1315 TRNAG-UCC 124905895
## 1316 TRNAG-UCC 124905898
## 1317 TRNAG-UCC 124905901
## 1318 TRNAG-UCC 124905904
## 1319 TRNAG-UCC 124905856
## 1320 TRNAG-UCC 124905859
## 1321 TRNAG-UCC 124905862
## 1322 TRNAG-UCC 124905865
## 1323 TRNAG-UCC 124905868
## 1324 TRNAG-UCC 124905871
## 1325 TRNAG-UCC 124905874
## 1326 TRNAG-UCC 124905877
## 1327 TRNAG-UCC 124905880
## 1328 TRNAG-UCC 124905883
## 1329 TRNAG-UCC 124905886
## 1330 TRNAG-UCC 124905889
## 1331 TRNAG-UCC 124905892
## 1332 TRNAG-UCC 124905895
## 1333 TRNAG-UCC 124905898
## 1334 TRNAG-UCC 124905901
## 1335 TRNAG-UCC 124905904
## 1336 TRNAG-UCC 124905856
## 1337 TRNAG-UCC 124905859
## 1338 TRNAG-UCC 124905862
## 1339 TRNAG-UCC 124905865
## 1340 TRNAG-UCC 124905868
## 1341 TRNAG-UCC 124905871
## 1342 TRNAG-UCC 124905874
## 1343 TRNAG-UCC 124905877
## 1344 TRNAG-UCC 124905880
## 1345 TRNAG-UCC 124905883
## 1346 TRNAG-UCC 124905886
## 1347 TRNAG-UCC 124905889
## 1348 TRNAG-UCC 124905892
## 1349 TRNAG-UCC 124905895
## 1350 TRNAG-UCC 124905898
## 1351 TRNAG-UCC 124905901
## 1352 TRNAG-UCC 124905904
## 1353 TRNAG-UCC 124905856
## 1354 TRNAG-UCC 124905859
## 1355 TRNAG-UCC 124905862
## 1356 TRNAG-UCC 124905865
## 1357 TRNAG-UCC 124905868
## 1358 TRNAG-UCC 124905871
## 1359 TRNAG-UCC 124905874
## 1360 TRNAG-UCC 124905877
## 1361 TRNAG-UCC 124905880
## 1362 TRNAG-UCC 124905883
## 1363 TRNAG-UCC 124905886
## 1364 TRNAG-UCC 124905889
## 1365 TRNAG-UCC 124905892
## 1366 TRNAG-UCC 124905895
## 1367 TRNAG-UCC 124905898
## 1368 TRNAG-UCC 124905901
## 1369 TRNAG-UCC 124905904
## 1370 TRNAG-UCC 124905856
## 1371 TRNAG-UCC 124905859
## 1372 TRNAG-UCC 124905862
## 1373 TRNAG-UCC 124905865
## 1374 TRNAG-UCC 124905868
## 1375 TRNAG-UCC 124905871
## 1376 TRNAG-UCC 124905874
## 1377 TRNAG-UCC 124905877
## 1378 TRNAG-UCC 124905880
## 1379 TRNAG-UCC 124905883
## 1380 TRNAG-UCC 124905886
## 1381 TRNAG-UCC 124905889
## 1382 TRNAG-UCC 124905892
## 1383 TRNAG-UCC 124905895
## 1384 TRNAG-UCC 124905898
## 1385 TRNAG-UCC 124905901
## 1386 TRNAG-UCC 124905904
## 1387 TRNAG-UCC 124905856
## 1388 TRNAG-UCC 124905859
## 1389 TRNAG-UCC 124905862
## 1390 TRNAG-UCC 124905865
## 1391 TRNAG-UCC 124905868
## 1392 TRNAG-UCC 124905871
## 1393 TRNAG-UCC 124905874
## 1394 TRNAG-UCC 124905877
## 1395 TRNAG-UCC 124905880
## 1396 TRNAG-UCC 124905883
## 1397 TRNAG-UCC 124905886
## 1398 TRNAG-UCC 124905889
## 1399 TRNAG-UCC 124905892
## 1400 TRNAG-UCC 124905895
## 1401 TRNAG-UCC 124905898
## 1402 TRNAG-UCC 124905901
## 1403 TRNAG-UCC 124905904
## 1404 TRNAG-UCC 124905856
## 1405 TRNAG-UCC 124905859
## 1406 TRNAG-UCC 124905862
## 1407 TRNAG-UCC 124905865
## 1408 TRNAG-UCC 124905868
## 1409 TRNAG-UCC 124905871
## 1410 TRNAG-UCC 124905874
## 1411 TRNAG-UCC 124905877
## 1412 TRNAG-UCC 124905880
## 1413 TRNAG-UCC 124905883
## 1414 TRNAG-UCC 124905886
## 1415 TRNAG-UCC 124905889
## 1416 TRNAG-UCC 124905892
## 1417 TRNAG-UCC 124905895
## 1418 TRNAG-UCC 124905898
## 1419 TRNAG-UCC 124905901
## 1420 TRNAG-UCC 124905904
## 1421 TRNAG-UCC 124905856
## 1422 TRNAG-UCC 124905859
## 1423 TRNAG-UCC 124905862
## 1424 TRNAG-UCC 124905865
## 1425 TRNAG-UCC 124905868
## 1426 TRNAG-UCC 124905871
## 1427 TRNAG-UCC 124905874
## 1428 TRNAG-UCC 124905877
## 1429 TRNAG-UCC 124905880
## 1430 TRNAG-UCC 124905883
## 1431 TRNAG-UCC 124905886
## 1432 TRNAG-UCC 124905889
## 1433 TRNAG-UCC 124905892
## 1434 TRNAG-UCC 124905895
## 1435 TRNAG-UCC 124905898
## 1436 TRNAG-UCC 124905901
## 1437 TRNAG-UCC 124905904
## 1438 TRNAG-UCC 124905856
## 1439 TRNAG-UCC 124905859
## 1440 TRNAG-UCC 124905862
## 1441 TRNAG-UCC 124905865
## 1442 TRNAG-UCC 124905868
## 1443 TRNAG-UCC 124905871
## 1444 TRNAG-UCC 124905874
## 1445 TRNAG-UCC 124905877
## 1446 TRNAG-UCC 124905880
## 1447 TRNAG-UCC 124905883
## 1448 TRNAG-UCC 124905886
## 1449 TRNAG-UCC 124905889
## 1450 TRNAG-UCC 124905892
## 1451 TRNAG-UCC 124905895
## 1452 TRNAG-UCC 124905898
## 1453 TRNAG-UCC 124905901
## 1454 TRNAG-UCC 124905904

A.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 columns
head(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    chr1

And 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 genome

Notice 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.

A.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.

A.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 columns
head(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 genome

A.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 genome

The 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.

A.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 columns
xk
## [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 ALOX5AP

A.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)
res

A.11 Exercise 11:

ensembl <- useEnsembl(biomart = "ensembl", dataset="hsapiens_gene_ensembl")
## Ensembl site unresponsive, trying useast mirror
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:0005576             1
## 3  GO:0005615             1
## 4  GO:0005886             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             1

A.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:0005886      IBA       CC
## 7         1 GO:0008150       ND       BP
## 8         1 GO:0031093      TAS       CC
## 9         1 GO:0034774      TAS       CC
## 10        1 GO:0062023      HDA       CC
## 11        1 GO:0070062      HDA       CC
## 12        1 GO:0072562      HDA       CC
## 13        1 GO:1904813      TAS       CC

When 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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