Genomic Annotation Resources
24 April 2018
Contents
- 1 Version Info
- 2 Introduction
- 3 Set Up
- 4 Using AnnotationHub
- 5 OrgDb objects
- 6 TxDb Objects
- 7 Organism.dplyr src_organism Objects
- 8 BSgenome Objects
- 9 biomaRt
- 10 Creating annotation objects
- 11 Important considerations
- 12 sessionInfo()
- 13 Acknowledgments
- 14 References
- Appendix
- A Answers for exercises
1 Version Info
R version: R version 4.3.1 (2023-06-16)
Bioconductor version: 3.18
Package version: 1.26.0
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 70744 records
## # snapshotDate(): 2023-10-19
## # $dataprovider: Ensembl, BroadInstitute, UCSC, ftp://ftp.ncbi.nlm.nih.gov/g...
## # $species: Homo sapiens, Mus musculus, Drosophila melanogaster, Bos taurus,...
## # $rdataclass: GRanges, TwoBitFile, BigWigFile, EnsDb, Rle, OrgDb, SQLiteFil...
## # additional mcols(): taxonomyid, genome, description,
## # coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
## # rdatapath, sourceurl, sourcetype
## # retrieve records with, e.g., 'object[["AH5012"]]'
##
## title
## AH5012 | Chromosome Band
## AH5013 | STS Markers
## AH5014 | FISH Clones
## AH5015 | Recomb Rate
## AH5016 | ENCODE Pilot
## ... ...
## AH114190 | cadd.v1.6.hg38.chr20.rds
## AH114191 | cadd.v1.6.hg38.chr21.rds
## AH114192 | cadd.v1.6.hg38.chr22.rds
## AH114193 | cadd.v1.6.hg38.chrX.rds
## AH114194 | cadd.v1.6.hg38.chrY.rdsAs you can see from the object above, there are a LOT of different resources available. So normally when you get an AnnotationHub object the 1st thing you want to do is to filter it to remove unwanted resources.
Fortunately, the AnnotationHub has several different kinds of metadata that you can use for searching and subsetting. To see the different categories all you need to do is to type the name of your AnnotationHub object and then tab complete from the ‘$’ 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] "ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/"
## [81] "Google DeepMind"
## [82] "UWashington"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 30531 records
## # snapshotDate(): 2023-10-19
## # $dataprovider: Ensembl, BroadInstitute, UCSC, Haemcode, FungiDB, Pazar, Tr...
## # $species: Homo sapiens, Mus musculus, Bos taurus, Pan troglodytes, Danio r...
## # $rdataclass: GRanges, data.frame, DNAStringSet, GRanges
## # additional mcols(): taxonomyid, genome, description,
## # coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
## # rdatapath, sourceurl, sourcetype
## # retrieve records with, e.g., 'object[["AH5012"]]'
##
##
## AH5012 |
## AH5013 |
## AH5014 |
## AH5015 |
## AH5016 |
## ...
## AH111329 |
## AH111330 |
## AH111331 |
## AH111332 |
## AH111333 |
## title
## AH5012 Chromosome Band
## AH5013 STS Markers
## AH5014 FISH Clones
## AH5015 Recomb Rate
## AH5016 ENCODE Pilot
## ... ...
## AH111329 Zonotrichia_albicollis.Zonotrichia_albicollis-1.0.1.109.abinitio...
## AH111330 Zonotrichia_albicollis.Zonotrichia_albicollis-1.0.1.109.gtf
## AH111331 Zosterops_lateralis_melanops.ASM128173v1.109.abinitio.gtf
## AH111332 Zosterops_lateralis_melanops.ASM128173v1.109.gtf
## AH111333 UCSC RepeatMasker annotations (Oct2022) for Human (hg38)Or you can use subsetting to only select for matches on a specific field
grs <- ah[ah$rdataclass == "GRanges",]The subset function is also provided.
orgs <- subset(ah, ah$rdataclass == "OrgDb")
orgs## AnnotationHub with 1970 records
## # snapshotDate(): 2023-10-19
## # $dataprovider: ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/, NCBI
## # $species: Escherichia coli, Coffea arabica, greater Indian_fruit_bat, Zych...
## # $rdataclass: OrgDb
## # additional mcols(): taxonomyid, genome, description,
## # coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
## # rdatapath, sourceurl, sourcetype
## # retrieve records with, e.g., 'object[["AH111589"]]'
##
## title
## AH111589 | TxDb Sqlite file for Coffea arabica
## AH111590 | org.Triticum_aestivum.eg.sqlite
## AH111591 | org.Triticum_aestivum_subsp._aestivum.eg.sqlite
## AH111592 | org.Triticum_vulgare.eg.sqlite
## AH111593 | org.Brassica_napus.eg.sqlite
## ... ...
## AH114088 | org.Mmu.eg.db.sqlite
## AH114089 | org.Ce.eg.db.sqlite
## AH114090 | org.Xl.eg.db.sqlite
## AH114091 | org.Sc.sgd.db.sqlite
## AH114092 | org.Dr.eg.db.sqliteAnd if you really need access to all the metadata you can extract it as a DataFrame using mcols() like so:
meta <- mcols(ah)Also if you are a fan of GUI’s you can use the display method to look at your data in a browser and return selected rows back as a smaller AnnotationHub object like this:
sah <- display(ah)Calling this method will produce a web based interface like the one pictured here:
Once you have the AnnotationHub object pared down to a reasonable size, and are sure about which records you want to retrieve, then you only need to use the ‘[[’ operator to extract them. Using the ‘[[’ operator, you can extract by numeric index (1,2,3) or by AnnotationHub ID. If you choose to use the former, you simply extract the element that you are interested in. So for our chain example, you might just want to 1st one like this:
res <- grs[[1]]## loading from cachehead(res, n=3)## UCSC track 'cytoBand'
## UCSCData object with 3 ranges and 1 metadata column:
## seqnames ranges strand | name
## <Rle> <IRanges> <Rle> | <character>
## [1] chr1 1-2300000 * | p36.33
## [2] chr1 2300001-5400000 * | p36.32
## [3] chr1 5400001-7200000 * | p36.31
## -------
## seqinfo: 93 sequences (1 circular) from hg19 genome4.1 AnnotationHub exercises
Exercise 1: Use the AnnotationHub to extract UCSC data that is from Homo sapiens and also specifically from the hg19 genome. What happens to the hub object as you filter data at each step?
Exercise 2 Now that you have basically narrowed things down to the hg19 annotations from UCSC genome browser, lets get one of these annotations. Find the oreganno track and save it into a local variable.
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5 OrgDb objects
At this point you might be wondering: What is this OrgDb object about? OrgDb objects are one member of a family of annotation objects that all represent hidden data through a shared set of methods. So if you look closely at the dog object created below you can see it contains data for Canis familiaris (taxonomy ID = 9615). You can learn a little more about it by learning about the columns method.
dogquery <- query(orgs, c("Canis familiaris", "9615"))
dogquery## AnnotationHub with 1 record
## # snapshotDate(): 2023-10-19
## # names(): AH114078
## # $dataprovider: ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/
## # $species: Canis familiaris
## # $rdataclass: OrgDb
## # $rdatadateadded: 2023-10-03
## # $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
## # $tags: c("NCBI", "Gene", "Annotation")
## # retrieve record with 'object[["AH114078"]]'ah_id <- dogquery$ah_id
ah_id## [1] "AH114078"dog <- ah[[ah_id]]## loading from cachecolumns(dog)## [1] "ACCNUM" "ALIAS" "ENSEMBL" "ENSEMBLPROT" "ENSEMBLTRANS"
## [6] "ENTREZID" "ENZYME" "EVIDENCE" "EVIDENCEALL" "GENENAME"
## [11] "GENETYPE" "GO" "GOALL" "ONTOLOGY" "ONTOLOGYALL"
## [16] "PATH" "PMID" "REFSEQ" "SYMBOL" "UNIPROT"The columns method gives you a vector of data types that can be retrieved from the object that you call it on. So the above call indicates that there are several different data types that can be retrieved from the tetra object.
A very similar method is the keytypes method, which will list all the data types that can also be used as keys.
keytypes(dog)## [1] "ACCNUM" "ALIAS" "ENSEMBL" "ENSEMBLPROT" "ENSEMBLTRANS"
## [6] "ENTREZID" "ENZYME" "EVIDENCE" "EVIDENCEALL" "GENENAME"
## [11] "GENETYPE" "GO" "GOALL" "ONTOLOGY" "ONTOLOGYALL"
## [16] "PATH" "PMID" "REFSEQ" "SYMBOL" "UNIPROT"In many cases most of the things that are listed as columns will also come back from a keytypes call, but since these two things are not guaranteed to be identical, we maintain two separate methods.
Now that you can see what kinds of things can be used as keys, you can call the keys method to extract out all the keys of a given key type.
head(keys(dog, keytype="ENTREZID"))## [1] "399518" "399530" "399544" "399545" "399653" "403152"This is useful if you need to get all the IDs of a particular kind but the keys method has a few extra arguments that can make it even more flexible. For example, using the keys method you could also extract the gene SYMBOLS that contain “COX” like this:
keys(dog, keytype="SYMBOL", pattern="COX")## [1] "COX5B" "COX7A2L" "COX8A" "COX15" "COX5A" "COX4I1" "COX6A2"
## [8] "COX20" "COX18" "ACOX1" "COX4I2" "ACOX3" "COX10" "COX17"
## [15] "COX11" "ACOXL" "COX7A1" "COX1" "COX2" "COX3" "COX19"
## [22] "COX7B2" "COX14" "ACOX2" "COX16"Or if you really needed an other keytype, you can use the column argument to extract the ENTREZ GENE IDs for those gene SYMBOLS that contain the string “COX”:
keys(dog, keytype="ENTREZID", pattern="COX", column="SYMBOL")## 'select()' returned 1:1 mapping between keys and columns## [1] "474567" "475739" "476040" "477792" "478370" "479623"
## [7] "479780" "480099" "482193" "483322" "485825" "488790"
## [13] "489515" "503668" "609555" "611729" "612614" "804478"
## [19] "804479" "804480" "100685945" "100687434" "100688544" "100855488"
## [25] "119863880"But often, you will really want to extract other data that matches a particular key or set of keys. For that there are two methods which you can use. The more powerful of these is probably select. Here is how you would look up the gene SYMBOL, and REFSEQ id for specific entrez gene ID.
select(dog, keys="804478", columns=c("SYMBOL","REFSEQ"), keytype="ENTREZID")## 'select()' returned 1:1 mapping between keys and columns## ENTREZID SYMBOL REFSEQ
## 1 804478 COX1 NP_008473When you call it, select will return a data.frame that attempts to fill in matching values for all the columns you requested. However, if you ask select for things that have a many to one relationship to your keys it can result in an expansion of the data object that is returned. For example, watch what happens when we ask for the GO terms for the same entrez gene ID:
select(dog, keys="804478", columns="GO", keytype="ENTREZID")## 'select()' returned 1:many mapping between keys and columns## ENTREZID GO EVIDENCE ONTOLOGY
## 1 804478 GO:0004129 IEA MF
## 2 804478 GO: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 MFBecause there are several GO terms associated with the gene “804478”, you end up with many rows in the data.frame. This can become problematic if you then ask for several columns that have a many to one relationship to the original key. If you were to do that, not only would the result multiply in size, it would also become really hard to use. A better strategy is to be selective when using select.
Sometimes you might want to look up matching results in a way that is simpler than the data.frame object that select returns. This is especially true when you only want to look up one kind of value per key. For these cases, we recommend that you look at the mapIds method. Lets look at what happens if request the same basic information as in our recent select call, but instead using the mapIds method:
mapIds(dog, keys="804478", column="GO", keytype="ENTREZID")## 'select()' returned 1:many mapping between keys and columns## 804478
## "GO:0004129"As you can see, the mapIds method allows you to simplify the result that is returned. And by default, mapIds only returns the 1st matching element for each key. But what if you really need all those GO terms returned when you call mapIds? Well then you can make use of the mapIds multiVals argument. There are several options for this argument, we have already seen how by default you can return only the ‘first’ element. But you can also return a ‘list’ or ‘CharacterList’ object, or you can ‘filter’ out or return ‘asNA’ any keys that have multiple matches. You can even define your own rule (as a function) and pass that in as an argument to multiVals. Lets look at what happens when you return a list:
mapIds(dog, keys="804478", column="GO", keytype="ENTREZID", multiVals="list")## 'select()' returned 1:many mapping between keys and columns## $`804478`
## [1] "GO:0004129" "GO: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.
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6 TxDb Objects
As mentioned before, TxDb objects can be accessed using the standard set of methods: keytypes(), columns(), keys(), select(), and mapIds(). But because these objects contain information about a transcriptome, they are often used to compare range based information to these important features of the genome[3,4]. As a result they also have specialized accessors for extracting out ranges that correspond to important transcriptome characteristics.
Lets start by loading a TxDb object from an annotation package based on the UCSC ensembl genes track for Drosophila. A common practice when loading these is to shorten the long name to ‘txdb’ (just as a convenience).
txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene
txdb## TxDb object:
## # Db type: TxDb
## # Supporting package: GenomicFeatures
## # Data source: UCSC
## # Genome: hg19
## # Organism: Homo sapiens
## # Taxonomy ID: 9606
## # UCSC Table: knownGene
## # Resource URL: http://genome.ucsc.edu/
## # Type of Gene ID: Entrez Gene ID
## # Full dataset: yes
## # miRBase build ID: GRCh37
## # transcript_nrow: 82960
## # exon_nrow: 289969
## # cds_nrow: 237533
## # Db created by: GenomicFeatures package from Bioconductor
## # Creation time: 2015-10-07 18:11:28 +0000 (Wed, 07 Oct 2015)
## # GenomicFeatures version at creation time: 1.21.30
## # RSQLite version at creation time: 1.0.0
## # DBSCHEMAVERSION: 1.1Just by looking at the TxDb object, we can learn a lot about what data it contains including where the data came from, which build of the UCSC genome it was based on and the last time that the object was updated. One of the most common uses for a TxDb object is to extract various kinds of transcript data out of it. So for example you can extract all the transcripts out of the TxDb as a GRanges object like this:
txs <- transcripts(txdb)
txs## GRanges object with 5506 ranges and 2 metadata columns:
## seqnames ranges strand | tx_id tx_name
## <Rle> <IRanges> <Rle> | <integer> <character>
## [1] chr3 238279-451097 + | 13060 uc003bot.3
## [2] chr3 238279-451097 + | 13061 uc003bou.3
## [3] chr3 239326-290282 + | 13062 uc003bov.2
## [4] chr3 239326-440831 + | 13063 uc003bow.2
## [5] chr3 361366-451097 + | 13064 uc011asi.2
## ... ... ... ... . ... ...
## [5502] chr18 77732867-77748532 - | 65761 uc002lnr.3
## [5503] chr18 77732867-77748532 - | 65762 uc010drf.3
## [5504] chr18 77732867-77793915 - | 65763 uc010drg.3
## [5505] chr18 77915117-78005397 - | 65764 uc002lny.3
## [5506] chr18 77941005-78005397 - | 65765 uc010xfp.2
## -------
## seqinfo: 2 sequences from hg19 genomeSimilarly, there are also extractors for exons(), cds(), genes() and promoters(). Which kind of feature you choose to extract just depends on what information you are after. These basic extractors are fine if you only want a flat representation of these data, but many of these features are inherently nested. So instead of extracting a flat GRanges object, you might choose instead to extract a GRangesList object that groups the transcripts by the genes that they are associated with like this:
txby <- transcriptsBy(txdb, by="gene")
txby## GRangesList object of length 1612:
## $`1000`
## GRanges object with 2 ranges and 2 metadata columns:
## seqnames ranges strand | tx_id tx_name
## <Rle> <IRanges> <Rle> | <integer> <character>
## [1] chr18 25530930-25616539 - | 65378 uc010xbn.1
## [2] chr18 25530930-25757445 - | 65379 uc002kwg.2
## -------
## seqinfo: 2 sequences from hg19 genome
##
## $`100009676`
## GRanges object with 1 range and 2 metadata columns:
## seqnames ranges strand | tx_id tx_name
## <Rle> <IRanges> <Rle> | <integer> <character>
## [1] chr3 101395274-101398057 + | 14200 uc003dvg.3
## -------
## seqinfo: 2 sequences from hg19 genome
##
## $`100101467`
## GRanges object with 3 ranges and 2 metadata columns:
## seqnames ranges strand | tx_id tx_name
## <Rle> <IRanges> <Rle> | <integer> <character>
## [1] chr18 32831023-32870196 - | 65418 uc002kyl.3
## [2] chr18 32831023-32870196 - | 65419 uc002kym.3
## [3] chr18 32843361-32870165 - | 65420 uc002kyn.1
## -------
## seqinfo: 2 sequences from hg19 genome
##
## ...
## <1609 more elements>Just as with the flat extractors, there is a whole family of extractors available depending on what you want to extract and how you want it grouped. They include transcriptsBy(), exonsBy(), cdsBy(), intronsByTranscript(), fiveUTRsByTranscript() and threeUTRsByTranscript().
When dealing with genomic data it is almost inevitable that you will run into problems with the way that different groups have adopted alternate ways of naming chromosomes. This is because almost every major repository has cooked up their own slightly different way of labeling these important features.
To cope with this, the Seqinfo object was invented and is attached to TxDb objects as well as the GenomicRanges extracted from these objects. You can extract it using the seqinfo() method like this:
si <- seqinfo(txdb)
si## Seqinfo object with 2 sequences from hg19 genome:
## seqnames seqlengths isCircular genome
## chr3 198022430 NA hg19
## chr18 78077248 NA hg19And since the seqinfo information is also attached to the GRanges objects produced by the TxDb extractors, you can also call seqinfo on the results of those methods like this:
txby <- transcriptsBy(txdb, by="gene")
si <- seqinfo(txby)The Seqinfo object contains a lot of valuable data about which chromosome features are present, whether they are circular or linear, and how long each one is. It is also something that will be checked against if you try to do an operation like ‘findOverlaps’ to compute overlapping ranges etc. So it’s a valuable way to make sure that the chromosomes and genome are the same for your annotations as the range that you are comparing them to. But sometimes you may have a situation where your annotation object contains data that is comparable to your data object, but where it is simply named with a different naming style. For those cases, there are helpers that you can use to discover what the current name style is for an object. And there is also a setter method to allow you to change the value to something more appropriate. So in the following example, we are going to change the seqlevelStyle from ‘UCSC’ to ‘ensembl’ based naming convention (and then back again).
head(seqlevels(txdb))## [1] "chr3" "chr18"seqlevelsStyle(txdb)## [1] "UCSC"seqlevelsStyle(txdb) <- "NCBI"
head(seqlevels(txdb))## [1] "3" "18"## then change it back
seqlevelsStyle(txdb) <- "UCSC"
head(seqlevels(txdb))## [1] "chr3" "chr18"In addition to being able to change the naming style used for an object with seqinfo data, you can also toggle which of the chromosomes are ‘active’ so that the software will ignore certain chromosomes. By default, all of the chromosomes are set to be ‘active’.
head(isActiveSeq(txdb), n=30)## chr3 chr18
## TRUE TRUEBut sometimes you might wish to ignore some of them. For example, lets suppose that you wanted to ignore the Y chromosome from our txdb. You could do that like so:
isActiveSeq(txdb)["chrY"] <- FALSE
head(isActiveSeq(txdb), n=26)6.1 TxDb exercises
Exercise 5: Use the accessors for the TxDb.Hsapiens.UCSC.hg19.knownGene package to retrieve the gene id, transcript name and transcript chromosome for all the transcripts. Do this using both the select() method and also using the transcripts() method. What is the difference in the output?
Exercise 6: Load the TxDb.Athaliana.BioMart.plantsmart22 package. This package is not from UCSC and it is based on plantsmart. Now use select or one of the range based accessors to look at the gene ids from this TxDb object. How do they compare to what you saw in the TxDb.Hsapiens.UCSC.hg19.knownGene package?
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7 Organism.dplyr src_organism Objects
So what happens if you have data from multiple different Annotation objects. For example, what if you had gene SYMBOLS (found in an OrgDb object) and you wanted to easily match those up with known gene transcript names from a UCSC based TxDb object? There is an ideal tool that can help with this kind of problem and it’s called an src_organism object from the Organism.dplyr package. src_organism objects and their related methods are able to query each of OrgDb and TxDb resources for you and then merge the results back together in way that lets you pretend that you only have one source for all your annotations.
library(Organism.dplyr)src_organism objects can be created for organisms that have both an OrgDb and a TxDb. To see organisms that can have src_organism objects made, use the function supportOrganisms():
supported <- supportedOrganisms()
print(supported, n=Inf)## # A tibble: 21 × 3
## organism OrgDb TxDb
## <chr> <chr> <chr>
## 1 Bos taurus org.Bt.eg.db TxDb.Btaurus.UCSC.bosTau8.refGene
## 2 Caenorhabditis elegans org.Ce.eg.db TxDb.Celegans.UCSC.ce11.refGene
## 3 Caenorhabditis elegans org.Ce.eg.db TxDb.Celegans.UCSC.ce6.ensGene
## 4 Canis familiaris org.Cf.eg.db TxDb.Cfamiliaris.UCSC.canFam3.refGene
## 5 Drosophila melanogaster org.Dm.eg.db TxDb.Dmelanogaster.UCSC.dm3.ensGene
## 6 Drosophila melanogaster org.Dm.eg.db TxDb.Dmelanogaster.UCSC.dm6.ensGene
## 7 Danio rerio org.Dr.eg.db TxDb.Drerio.UCSC.danRer10.refGene
## 8 Gallus gallus org.Gg.eg.db TxDb.Ggallus.UCSC.galGal4.refGene
## 9 Homo sapiens org.Hs.eg.db TxDb.Hsapiens.UCSC.hg18.knownGene
## 10 Homo sapiens org.Hs.eg.db TxDb.Hsapiens.UCSC.hg19.knownGene
## 11 Homo sapiens org.Hs.eg.db TxDb.Hsapiens.UCSC.hg38.knownGene
## 12 Mus musculus org.Mm.eg.db TxDb.Mmusculus.UCSC.mm10.ensGene
## 13 Mus musculus org.Mm.eg.db TxDb.Mmusculus.UCSC.mm10.knownGene
## 14 Mus musculus org.Mm.eg.db TxDb.Mmusculus.UCSC.mm9.knownGene
## 15 Macaca mulatta org.Mmu.eg.db TxDb.Mmulatta.UCSC.rheMac3.refGene
## 16 Macaca mulatta org.Mmu.eg.db TxDb.Mmulatta.UCSC.rheMac8.refGene
## 17 Pan troglodytes org.Pt.eg.db TxDb.Ptroglodytes.UCSC.panTro4.refGene
## 18 Rattus norvegicus org.Rn.eg.db TxDb.Rnorvegicus.UCSC.rn4.ensGene
## 19 Rattus norvegicus org.Rn.eg.db TxDb.Rnorvegicus.UCSC.rn5.refGene
## 20 Rattus norvegicus org.Rn.eg.db TxDb.Rnorvegicus.UCSC.rn6.refGene
## 21 Sus scrofa org.Ss.eg.db TxDb.Sscrofa.UCSC.susScr3.refGeneNotice how there are multiple entries for a single organism (e.g. three for Homo sapiens). There is only one OrgDb per organism, but different TxDbs can be used. To specify a certain version of a TxDb to use, we can use the src_organism() function to create an src_organism object.
library(org.Hs.eg.db)
library(TxDb.Hsapiens.UCSC.hg38.knownGene)src <- src_organism("TxDb.Hsapiens.UCSC.hg38.knownGene")## creating 'src_organism' database...src## src: sqlite 3.41.2 [/tmp/RtmpqXarNi/filec6b5463de7f59]
## tbls: id, id_accession, id_go, id_go_all, id_omim_pm, id_protein,
## id_transcript, ranges_cds, ranges_exon, ranges_gene, ranges_txWe can also create one using the src_ucsc() function. This will create an src_organism object using the most recent TxDb version available:
src <- src_ucsc("Homo sapiens")src## src: sqlite 3.41.2 [/tmp/RtmpqXarNi/filec6b5463de7f59]
## tbls: id, id_accession, id_go, id_go_all, id_omim_pm, id_protein,
## id_transcript, ranges_cds, ranges_exon, ranges_gene, ranges_txThe five methods that worked for all of the other Db objects that we have discussed (keytypes(), columns(), keys(), select(), and mapIds()) all work for src_organism objects. Here, we use keytypes() to show which keytypes can be passed to the keytype argument of select().
keytypes(src)## [1] "accnum" "alias" "cds_chrom" "cds_end" "cds_id"
## [6] "cds_name" "cds_start" "cds_strand" "ensembl" "ensemblprot"
## [11] "ensembltrans" "entrez" "enzyme" "evidence" "evidenceall"
## [16] "exon_chrom" "exon_end" "exon_id" "exon_name" "exon_rank"
## [21] "exon_start" "exon_strand" "gene_chrom" "gene_end" "gene_start"
## [26] "gene_strand" "genename" "go" "goall" "ipi"
## [31] "map" "omim" "ontology" "ontologyall" "pfam"
## [36] "pmid" "prosite" "refseq" "symbol" "tx_chrom"
## [41] "tx_end" "tx_id" "tx_name" "tx_start" "tx_strand"
## [46] "tx_type" "uniprot"Use columns() to show which keytypes can be passed to the keytype argument of select().
columns(src)## [1] "accnum" "alias" "cds_chrom" "cds_end" "cds_id"
## [6] "cds_name" "cds_start" "cds_strand" "ensembl" "ensemblprot"
## [11] "ensembltrans" "entrez" "enzyme" "evidence" "evidenceall"
## [16] "exon_chrom" "exon_end" "exon_id" "exon_name" "exon_rank"
## [21] "exon_start" "exon_strand" "gene_chrom" "gene_end" "gene_start"
## [26] "gene_strand" "genename" "go" "goall" "ipi"
## [31] "map" "omim" "ontology" "ontologyall" "pfam"
## [36] "pmid" "prosite" "refseq" "symbol" "tx_chrom"
## [41] "tx_end" "tx_id" "tx_name" "tx_start" "tx_strand"
## [46] "tx_type" "uniprot"And that’s it. You can now use these objects in the same way that you use OrgDb or TxDb objects. It works the same as the base objects that it contains:
select(src, keys="4488", columns=c("symbol", "tx_name"), keytype="entrez")## Joining 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 ENST00000507785.2Organism.dplyr also supports numerous Genomic Extractor functions allowing users to filter based on information contained in the OrgDb and TxDb objects. To see the filters supported by a src_organism() object, use supportedFIlters():
head(supportedFilters(src))## filter field
## 1 AccnumFilter accnum
## 2 AliasFilter alias
## 3 CdsChromFilter cds_chrom
## 44 CdsEndFilter cds_end
## 42 CdsIdFilter cds_id
## 4 CdsNameFilter cds_nameThe ranged based accessors such as those in GenomicFeatures will also work. There are also "_tbl" functions (e.g. transcripts_tbl()) that return tbl objects instead of GRanges objects. Complex filter statements can be given as input. Here we declare a GRangesFilter and use two different type-returning accessors to query transcripts that either start with “SNORD” and are within our given GRangesFilter, or have symbol with symbol “ADA”:
gr <- GRangesFilter(GenomicRanges::GRanges("chr1:44000000-55000000"))
transcripts(src, filter=~(symbol %startsWith% "SNORD" & gr) | symbol == "ADA")## 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 genometranscripts_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 rows7.1 Organism.dplyr exercises
Exercise 7: Use the src_organism object to look up the gene symbol, transcript start and chromosome using select(). Then do the same thing using transcripts. You might expect that this call to transcripts will look the same as it did for the TxDb object, but (temporarily) it will not.
Exercise 8: Look at the results from call the columns method on the src_organism object and compare that to what happens when you call columns on the org.Hs.eg.db object and then look at a call to columns on the TxDb.Hsapiens.UCSC.hg19.knownGene object.
Exercise 9: Use the src_organism object with the transcripts method to look up the entrez gene IDs for all gene symbols that contain the letter ‘X’.
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8 BSgenome Objects
Another important annotation resource type is a BSgenome package[10]. There are many BSgenome packages in the repository for you to choose from. And you can learn which organisms are already supported by using the available.genomes() function.
head(available.genomes())## [1] "BSgenome.Alyrata.JGI.v1"
## [2] "BSgenome.Amellifera.BeeBase.assembly4"
## [3] "BSgenome.Amellifera.NCBI.AmelHAv3.1"
## [4] "BSgenome.Amellifera.UCSC.apiMel2"
## [5] "BSgenome.Amellifera.UCSC.apiMel2.masked"
## [6] "BSgenome.Aofficinalis.NCBI.V1"Unlike the other resources that we have discussed here, these packages are meant to contain sequence data for a specific genome build of an organism. You can load one of these packages in the usual way. And each of them normally has an alias for the primary object that is shorter than the full package name (as a convenience):
ls(2)## character(0)Hsapiens## | 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 chr2Whereas if you give the a GRanges object for the 2nd argument, you can instead get a DNAStringSet that corresponds to those ranges. This can be a powerful way to learn what sequence was present from a particular range. For example, here we can extract the range of a specific gene of interest like this.
txby <- transcriptsBy(txdb, by="gene")
geneOfInterest <- txby[["4488"]]
res <- getSeq(Hsapiens, geneOfInterest)
resAdditionally, the Biostrings[11] package has many useful functions for finding a pattern in a string set etc. You may not have noticed when it happened, but the Biostrings package was loaded when you loaded the BSgenome object, so these functions will already be available for you to explore.
8.1 BSgenome exercises
Exercise 10: Use what you have just learned to extract the sequence for the PTEN gene.
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9 biomaRt
Another great annotation resource is the biomaRt package[5,6,7]. The biomaRt package exposes a huge family of different online annotation resources called marts. Each mart is another of a set of online web resources that are following a convention that allows them to work with this package. 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 110
## 2 mouse_strains Mouse strains 110
## 3 snps Ensembl Variation 110
## 4 regulation Ensembl Regulation 110ensembl <- useEnsembl(biomart = "genes")
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 ASM200744v2ensembl <- useEnsembl(biomart="genes", dataset="hsapiens_gene_ensembl")
ensembl## Object of class 'Mart':
## Using the ENSEMBL_MART_ENSEMBL BioMart database
## Using the hsapiens_gene_ensembl datasetNext we need to think about attributes, values and filters. Lets start with attributes. You can get a listing of the different kinds of attributes from biomaRt buy using the listAttributes method:
head(listAttributes(ensembl))## name description page
## 1 ensembl_gene_id Gene stable ID feature_page
## 2 ensembl_gene_id_version Gene stable ID version feature_page
## 3 ensembl_transcript_id Transcript stable ID feature_page
## 4 ensembl_transcript_id_version Transcript stable ID version feature_page
## 5 ensembl_peptide_id Protein stable ID feature_page
## 6 ensembl_peptide_id_version Protein stable ID version feature_pageAnd you can see what the values for a particular attribute are by using the getBM method:
head(getBM(attributes="chromosome_name", mart=ensembl))## chromosome_name
## 1 1
## 2 10
## 3 11
## 4 12
## 5 13
## 6 14Attributes are the things that you can have returned from biomaRt. They are analogous to what you get when you use the columns method with other objects.
In the biomaRt package, filters are things that can be used with values to restrict or choose what comes back. The ‘values’ here are treated as keys that you are passing in and which you would like to know more information about. In contrast, the filter represents the kind of key that you are searching for. So for example, you might choose a filter name of “chromosome_name” to go with specific value of “1”. Together these two argument values would request whatever attributes matched things on the 1st chromosome. 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 StartSo now you know about attributes, values and filters, you can call the getBM() method to put it all together and request specific data from the mart. So for example, the following requests gene symbols and 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 84771
## 2 727856
## 3 100287102
## 4 100287596
## 5 102725121
## 6 DDX11L1 NAOf 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?
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10 Creating annotation objects
By now you are aware that Bioconductor has a lot of annotation resources. But it is still completely impossible to have every annotation resource pre-packaged for every conceivable use. Because of this, almost all annotation objects have special functions that can be called to create those objects (or the packages that load them) from generalized data resources or specific file types. Below is a table with a few of the more popular options.
| If you want this | And you have this | Then you could call this to help |
|---|---|---|
| TxDb | tracks from UCSC | GenomicFeatures::makeTxDbPackageFromUCSC |
| TxDb | data from biomaRt | GenomicFeatures::makeTxDbPackageFromBiomaRt |
| TxDb | gff or gtf file | GenomicFeatures::makeTxDbFromGFF |
| OrgDb | custom data.frames | AnnotationForge::makeOrgPackage |
| OrgDb | valid Taxonomy ID | AnnotationForge::makeOrgPackageFromNCBI |
| ChipDb | org package & data.frame | AnnotationForge::makeChipPackage |
| BSgenome | fasta or twobit sequence files | BSgenome::forgeBSgenomeDataPkg |
In most cases the output for resource creation functions will be an annotation package that you can install.
And there is unfortunately not enough space to demonstrate how to call each of these functions here. But to do so is actually pretty straightforward and most such functions will be well documented with their associated manual pages and vignettes[3,4,10,12]. As usual, you can see the help page for any function right inside of R.
help("makeTxDbPackageFromUCSC")If you plan to make use of these kinds of functions then you should expect to consult the associated documentation first. These kinds of functions tend to have a lot of arguments and most of them also require that their input data meet some fairly specific criteria. Finally, you should know that even after you have succeeded at creating an annotation package, you will also have to make use of the install.packages() function (with the repos argument=NULL) to install whatever package source directory has just been created.
11 Important considerations
The bioconductor project represents a very large and active codebase from an active and engaged community. Because of this, you should expect that the software described in this walkthrough will change over time and often in dramatic ways. As an example, the getSeq function that is described in this chapter is expected to a big overhaul in the coming months. When this happens the older function will be deprecated for a full release cycle (6 months) and then labeled as defunct for another release cycle before it is removed. This cycle is in place so that active users can be warned about what is happening and where they should look for the appropriate replacement functionality. But obviously, this system cannot warn end users if they have not been vigilant about updating their software to the latest version. So please take the time to always update your software to the latest version.
To stay abreast of new developments users are encouraged to explore the bioconductor website which contains many current walkthroughs and vignettes. Also visit the support site where you can ask questions and engage in discussions.
12 sessionInfo()
Package versions used in this tutorial:
sessionInfo()## R version 4.3.1 (2023-06-16)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.3 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.18-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_1.26.0
## [2] TxDb.Athaliana.BioMart.plantsmart22_3.0.1
## [3] biomaRt_2.58.0
## [4] BSgenome.Hsapiens.UCSC.hg19_1.4.3
## [5] BSgenome_1.70.0
## [6] rtracklayer_1.62.0
## [7] BiocIO_1.12.0
## [8] Homo.sapiens_1.3.1
## [9] GO.db_3.18.0
## [10] OrganismDbi_1.44.0
## [11] org.Mm.eg.db_3.18.0
## [12] org.Hs.eg.db_3.18.0
## [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_1.54.0
## [17] AnnotationDbi_1.64.0
## [18] Organism.dplyr_1.30.0
## [19] AnnotationFilter_1.26.0
## [20] dplyr_1.1.3
## [21] AnnotationHub_3.10.0
## [22] BiocFileCache_2.10.0
## [23] dbplyr_2.3.4
## [24] VariantAnnotation_1.48.0
## [25] Rsamtools_2.18.0
## [26] Biostrings_2.70.0
## [27] XVector_0.42.0
## [28] SummarizedExperiment_1.32.0
## [29] Biobase_2.62.0
## [30] GenomicRanges_1.54.0
## [31] GenomeInfoDb_1.38.0
## [32] IRanges_2.36.0
## [33] S4Vectors_0.40.0
## [34] MatrixGenerics_1.14.0
## [35] matrixStats_1.0.0
## [36] BiocGenerics_0.48.0
## [37] BiocStyle_2.30.0
##
## loaded via a namespace (and not attached):
## [1] DBI_1.1.3 bitops_1.0-7
## [3] RBGL_1.78.0 rlang_1.1.1
## [5] magrittr_2.0.3 compiler_4.3.1
## [7] RSQLite_2.3.1 png_0.1-8
## [9] vctrs_0.6.4 stringr_1.5.0
## [11] pkgconfig_2.0.3 crayon_1.5.2
## [13] fastmap_1.1.1 ellipsis_0.3.2
## [15] utf8_1.2.4 promises_1.2.1
## [17] rmarkdown_2.25 graph_1.80.0
## [19] purrr_1.0.2 bit_4.0.5
## [21] xfun_0.40 zlibbioc_1.48.0
## [23] cachem_1.0.8 jsonlite_1.8.7
## [25] progress_1.2.2 blob_1.2.4
## [27] later_1.3.1 DelayedArray_0.28.0
## [29] BiocParallel_1.36.0 interactiveDisplayBase_1.40.0
## [31] parallel_4.3.1 prettyunits_1.2.0
## [33] R6_2.5.1 bslib_0.5.1
## [35] stringi_1.7.12 jquerylib_0.1.4
## [37] Rcpp_1.0.11 bookdown_0.36
## [39] knitr_1.44 httpuv_1.6.12
## [41] Matrix_1.6-1.1 tidyselect_1.2.0
## [43] abind_1.4-5 yaml_2.3.7
## [45] codetools_0.2-19 curl_5.1.0
## [47] lattice_0.22-5 tibble_3.2.1
## [49] withr_2.5.1 shiny_1.7.5.1
## [51] KEGGREST_1.42.0 evaluate_0.22
## [53] xml2_1.3.5 pillar_1.9.0
## [55] BiocManager_1.30.22 filelock_1.0.2
## [57] generics_0.1.3 RCurl_1.98-1.12
## [59] BiocVersion_3.18.0 hms_1.1.3
## [61] xtable_1.8-4 glue_1.6.2
## [63] lazyeval_0.2.2 tools_4.3.1
## [65] GenomicAlignments_1.38.0 XML_3.99-0.14
## [67] grid_4.3.1 GenomeInfoDbData_1.2.11
## [69] restfulr_0.0.15 cli_3.6.1
## [71] rappdirs_0.3.3 fansi_1.0.5
## [73] S4Arrays_1.2.0 sass_0.4.7
## [75] digest_0.6.33 SparseArray_1.2.0
## [77] rjson_0.2.21 memoise_2.0.1
## [79] htmltools_0.5.6.1 lifecycle_1.0.3
## [81] httr_1.4.7 mime_0.12
## [83] bit64_4.0.513 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
Wolfgang Huber, Vincent J Carey, Robert Gentleman, Simon Anders, Marc Carlson, Benilton S Carvalho, Hector Corrada Bravo, Sean Davis, Laurent Gatto, Thomas Girke, Raphael Gottardo, Florian Hahne, Kasper D Hansen, Rafael A Irizarry, Michael Lawrence, Michael I Love, James MacDonald, Valerie Obenchain, Andrzej K Oleś, Hervé Pagès, Alejandro Reyes, Paul Shannon, Gordon K Smyth, Dan Tenenbaum, Levi Waldron & Martin Morgan (2015) Orchestrating high-throughput genomic analysis with Bioconductor Nature Methods 12:115-121
Pages H, Carlson M, Falcon S and Li N. AnnotationDbi: Annotation Database Interface. R package version 1.30.0.
M. Carlson, H. Pages, P. Aboyoun, S. Falcon, M. Morgan, D. Sarkar, M. Lawrence GenomicFeatures: Tools for making and manipulating transcript centric annotations version 1.19.38.
Lawrence M, Huber W, Pagès H, Aboyoun P, Carlson M, Gentleman R, Morgan M and Carey V (2013). Software for Computing and Annotating Genomic Ranges. PLoS Computational Biology, 9. http://dx.doi.org/10.1371/journal.pcbi.1003118, http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003118
Steffen Durinck, Wolfgang Huber biomaRt: Interface to BioMart databases (e.g. Ensembl, COSMIC ,Wormbase and Gramene) version 2.23.5.
Durinck S, Spellman P, Birney E and Huber W (2009). Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nature Protocols, 4, pp. 1184-1191.
Durinck S, Moreau Y, Kasprzyk A, Davis S, De Moor B, Brazma A and Huber W (2005). BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis. Bioinformatics, 21, pp. 3439-3440.
Morgan M, Carlson M, Tenenbaum D and Arora S. AnnotationHub: Client to access AnnotationHub resources. R package version 2.0.1.
Carlson M, Pages H, Morgan M and Obenchain V. OrganismDbi: Software to enable the smooth interfacing of different database packages. R package version 1.10.0.
Pages H. BSgenome: Infrastructure for Biostrings-based genome data packages. R package version 1.36.0.
Pages H, Aboyoun P, Gentleman R and DebRoy S. Biostrings: String objects representing biological sequences, and matching algorithms. R package version 2.36.0.
Carlson M, and Pages H. AnnotationForge: Code for Building Annotation Database Packages. R package version 1.10.0.
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$genome=='hg19')
length(ahs)## [1] 5908ahs <- subset(ahs, ahs$species=='Homo sapiens')
length(ahs)## [1] 5908You 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(): 2023-10-19
## # $dataprovider: Pazar, UCSC
## # $species: Saccharomyces cerevisiae, Homo sapiens, NA
## # $rdataclass: GRanges
## # additional mcols(): taxonomyid, genome, description,
## # coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
## # rdatapath, sourceurl, sourcetype
## # retrieve records with, e.g., 'object[["AH5087"]]'
##
## title
## AH5087 | ORegAnno
## AH5213 | ORegAnno
## AH7053 | ORegAnno
## AH7061 | ORegAnno
## AH22286 | pazar_ORegAnno_20120522.csv
## AH22287 | pazar_ORegAnno_ENCODEprom_20120522.csv
## AH22288 | pazar_ORegAnno_Erythroid_20120522.csv
## AH22289 | pazar_ORegAnno_STAT1_ChIP_20120522.csv
## AH22290 | pazar_ORegAnno_STAT1_lit_20120522.csvahs[1]## AnnotationHub with 1 record
## # snapshotDate(): 2023-10-19
## # names(): AH5087
## # $dataprovider: UCSC
## # $species: Homo sapiens
## # $rdataclass: GRanges
## # $rdatadateadded: 2013-03-26
## # $title: ORegAnno
## # $description: GRanges object from UCSC track 'ORegAnno'
## # $taxonomyid: 9606
## # $genome: hg19
## # $sourcetype: UCSC track
## # $sourceurl: rtracklayer://hgdownload.cse.ucsc.edu/goldenpath/hg19/database...
## # $sourcesize: NA
## # $tags: c("oreganno", "UCSC", "track", "Gene", "Transcript",
## # "Annotation")
## # retrieve record with 'object[["AH5087"]]'oreg <- ahs[['AH5087']]## loading from cacheoreg## GRanges object with 23118 ranges and 2 metadata columns:
## seqnames ranges strand | name score
## <Rle> <IRanges> <Rle> | <character> <numeric>
## [1] chr1 873499-873849 + | OREG0012989 0
## [2] chr1 886764-887214 + | OREG0012990 0
## [3] chr1 886938-886958 + | OREG0007909 0
## [4] chr1 919400-919950 + | OREG0012991 0
## [5] chr1 919695-919715 + | OREG0007910 0
## ... ... ... ... . ... ...
## [23114] chr7_gl000195_random 1-851 + | OREG0026736 0
## [23115] chr7_gl000195_random 103427-103447 + | OREG0012963 0
## [23116] chr7_gl000195_random 121139-121159 + | OREG0012964 0
## [23117] chr17_gl000204_random 58370-58955 + | OREG0026769 0
## [23118] chr17_gl000205_random 117492-118442 + | OREG0026772 0
## -------
## seqinfo: 93 sequences (1 circular) from hg19 genomeA.3 Exercise 3:
keys <- "MSX2"
columns <- c("ENTREZID", "CHR")
select(org.Hs.eg.db, keys, columns, keytype="SYMBOL")## Warning in .deprecatedColsMessage(): Accessing gene location information via 'CHR','CHRLOC','CHRLOCEND' is
## deprecated. Please use a range based accessor like genes(), or select()
## with columns values like TXCHROM and TXSTART on a TxDb or OrganismDb
## object instead.## 'select()' returned 1:1 mapping between keys and columns## SYMBOL ENTREZID CHR
## 1 MSX2 4488 5A.4 Exercise 4:
## 1st get all the gene symbols
orgSymbols <- keys(org.Hs.eg.db, keytype="SYMBOL")
## and then use that to get all gene symbols matched to all entrez gene IDs
egr <- select(org.Hs.eg.db, keys=orgSymbols, "ENTREZID", "SYMBOL")## 'select()' returned 1:many mapping between keys and columnslength(egr$ENTREZID)## [1] 191179length(unique(egr$ENTREZID))## [1] 191179## VS:
length(egr$SYMBOL)## [1] 191179length(unique(egr$SYMBOL))## [1] 191076## So lets trap these symbols that are redundant and look more closely...
redund <- egr$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
## 16 TRNAV-CAC 107985615
## 17 TRNAE-UUC 107987368
## 18 TRNAE-UUC 124905580
## 19 TRNAE-UUC 124905583
## 20 TRNAE-UUC 124905584
## 21 TRNAE-UUC 124905586
## 22 TRNAE-UUC 124905908
## 23 TRNAE-UUC 107987368
## 24 TRNAE-UUC 124905580
## 25 TRNAE-UUC 124905583
## 26 TRNAE-UUC 124905584
## 27 TRNAE-UUC 124905586
## 28 TRNAE-UUC 124905908
## 29 TRNAE-UUC 107987368
## 30 TRNAE-UUC 124905580
## 31 TRNAE-UUC 124905583
## 32 TRNAE-UUC 124905584
## 33 TRNAE-UUC 124905586
## 34 TRNAE-UUC 124905908
## 35 TRNAE-UUC 107987368
## 36 TRNAE-UUC 124905580
## 37 TRNAE-UUC 124905583
## 38 TRNAE-UUC 124905584
## 39 TRNAE-UUC 124905586
## 40 TRNAE-UUC 124905908
## 41 TRNAE-UUC 107987368
## 42 TRNAE-UUC 124905580
## 43 TRNAE-UUC 124905583
## 44 TRNAE-UUC 124905584
## 45 TRNAE-UUC 124905586
## 46 TRNAE-UUC 124905908
## 47 TRNAA-AGC 124901561
## 48 TRNAA-AGC 124901562
## 49 TRNAA-AGC 124901563
## 50 TRNAA-AGC 124901564
## 51 TRNAA-AGC 124901565
## 52 TRNAA-AGC 124906586
## 53 TRNAA-AGC 124901561
## 54 TRNAA-AGC 124901562
## 55 TRNAA-AGC 124901563
## 56 TRNAA-AGC 124901564
## 57 TRNAA-AGC 124901565
## 58 TRNAA-AGC 124906586
## 59 TRNAA-AGC 124901561
## 60 TRNAA-AGC 124901562
## 61 TRNAA-AGC 124901563
## 62 TRNAA-AGC 124901564
## 63 TRNAA-AGC 124901565
## 64 TRNAA-AGC 124906586
## 65 TRNAA-AGC 124901561
## 66 TRNAA-AGC 124901562
## 67 TRNAA-AGC 124901563
## 68 TRNAA-AGC 124901564
## 69 TRNAA-AGC 124901565
## 70 TRNAA-AGC 124906586
## 71 TRNAA-AGC 124901561
## 72 TRNAA-AGC 124901562
## 73 TRNAA-AGC 124901563
## 74 TRNAA-AGC 124901564
## 75 TRNAA-AGC 124901565
## 76 TRNAA-AGC 124906586
## 77 TRNAG-CCC 124905578
## 78 TRNAG-CCC 124905581
## 79 TRNAG-CCC 124905588
## 80 TRNAG-CCC 124905578
## 81 TRNAG-CCC 124905581
## 82 TRNAG-CCC 124905588
## 83 TRNAN-GUU 124905579
## 84 TRNAN-GUU 124905582
## 85 TRNAN-GUU 124905585
## 86 TRNAN-GUU 124905587
## 87 TRNAN-GUU 124905579
## 88 TRNAN-GUU 124905582
## 89 TRNAN-GUU 124905585
## 90 TRNAN-GUU 124905587
## 91 TRNAN-GUU 124905579
## 92 TRNAN-GUU 124905582
## 93 TRNAN-GUU 124905585
## 94 TRNAN-GUU 124905587
## 95 TRNAG-GCC 124905847
## 96 TRNAG-GCC 124905849
## 97 TRNAG-GCC 124905851
## 98 TRNAG-GCC 124905853
## 99 TRNAG-GCC 124905907
## 100 TRNAG-GCC 124905910
## 101 TRNAG-GCC 124905912
## 102 TRNAG-GCC 124905914
## 103 TRNAG-GCC 124905916
## 104 TRNAG-GCC 124905918
## 105 TRNAG-GCC 124905921
## 106 TRNAG-GCC 124905923
## 107 TRNAG-GCC 124905925
## 108 TRNAG-GCC 124905927
## 109 TRNAG-GCC 124905929
## 110 TRNAG-GCC 124905931
## 111 TRNAG-GCC 124905933
## 112 TRNAG-GCC 124905847
## 113 TRNAG-GCC 124905849
## 114 TRNAG-GCC 124905851
## 115 TRNAG-GCC 124905853
## 116 TRNAG-GCC 124905907
## 117 TRNAG-GCC 124905910
## 118 TRNAG-GCC 124905912
## 119 TRNAG-GCC 124905914
## 120 TRNAG-GCC 124905916
## 121 TRNAG-GCC 124905918
## 122 TRNAG-GCC 124905921
## 123 TRNAG-GCC 124905923
## 124 TRNAG-GCC 124905925
## 125 TRNAG-GCC 124905927
## 126 TRNAG-GCC 124905929
## 127 TRNAG-GCC 124905931
## 128 TRNAG-GCC 124905933
## 129 TRNAG-GCC 124905847
## 130 TRNAG-GCC 124905849
## 131 TRNAG-GCC 124905851
## 132 TRNAG-GCC 124905853
## 133 TRNAG-GCC 124905907
## 134 TRNAG-GCC 124905910
## 135 TRNAG-GCC 124905912
## 136 TRNAG-GCC 124905914
## 137 TRNAG-GCC 124905916
## 138 TRNAG-GCC 124905918
## 139 TRNAG-GCC 124905921
## 140 TRNAG-GCC 124905923
## 141 TRNAG-GCC 124905925
## 142 TRNAG-GCC 124905927
## 143 TRNAG-GCC 124905929
## 144 TRNAG-GCC 124905931
## 145 TRNAG-GCC 124905933
## 146 TRNAG-GCC 124905847
## 147 TRNAG-GCC 124905849
## 148 TRNAG-GCC 124905851
## 149 TRNAG-GCC 124905853
## 150 TRNAG-GCC 124905907
## 151 TRNAG-GCC 124905910
## 152 TRNAG-GCC 124905912
## 153 TRNAG-GCC 124905914
## 154 TRNAG-GCC 124905916
## 155 TRNAG-GCC 124905918
## 156 TRNAG-GCC 124905921
## 157 TRNAG-GCC 124905923
## 158 TRNAG-GCC 124905925
## 159 TRNAG-GCC 124905927
## 160 TRNAG-GCC 124905929
## 161 TRNAG-GCC 124905931
## 162 TRNAG-GCC 124905933
## 163 TRNAG-GCC 124905847
## 164 TRNAG-GCC 124905849
## 165 TRNAG-GCC 124905851
## 166 TRNAG-GCC 124905853
## 167 TRNAG-GCC 124905907
## 168 TRNAG-GCC 124905910
## 169 TRNAG-GCC 124905912
## 170 TRNAG-GCC 124905914
## 171 TRNAG-GCC 124905916
## 172 TRNAG-GCC 124905918
## 173 TRNAG-GCC 124905921
## 174 TRNAG-GCC 124905923
## 175 TRNAG-GCC 124905925
## 176 TRNAG-GCC 124905927
## 177 TRNAG-GCC 124905929
## 178 TRNAG-GCC 124905931
## 179 TRNAG-GCC 124905933
## 180 TRNAG-GCC 124905847
## 181 TRNAG-GCC 124905849
## 182 TRNAG-GCC 124905851
## 183 TRNAG-GCC 124905853
## 184 TRNAG-GCC 124905907
## 185 TRNAG-GCC 124905910
## 186 TRNAG-GCC 124905912
## 187 TRNAG-GCC 124905914
## 188 TRNAG-GCC 124905916
## 189 TRNAG-GCC 124905918
## 190 TRNAG-GCC 124905921
## 191 TRNAG-GCC 124905923
## 192 TRNAG-GCC 124905925
## 193 TRNAG-GCC 124905927
## 194 TRNAG-GCC 124905929
## 195 TRNAG-GCC 124905931
## 196 TRNAG-GCC 124905933
## 197 TRNAG-GCC 124905847
## 198 TRNAG-GCC 124905849
## 199 TRNAG-GCC 124905851
## 200 TRNAG-GCC 124905853
## 201 TRNAG-GCC 124905907
## 202 TRNAG-GCC 124905910
## 203 TRNAG-GCC 124905912
## 204 TRNAG-GCC 124905914
## 205 TRNAG-GCC 124905916
## 206 TRNAG-GCC 124905918
## 207 TRNAG-GCC 124905921
## 208 TRNAG-GCC 124905923
## 209 TRNAG-GCC 124905925
## 210 TRNAG-GCC 124905927
## 211 TRNAG-GCC 124905929
## 212 TRNAG-GCC 124905931
## 213 TRNAG-GCC 124905933
## 214 TRNAG-GCC 124905847
## 215 TRNAG-GCC 124905849
## 216 TRNAG-GCC 124905851
## 217 TRNAG-GCC 124905853
## 218 TRNAG-GCC 124905907
## 219 TRNAG-GCC 124905910
## 220 TRNAG-GCC 124905912
## 221 TRNAG-GCC 124905914
## 222 TRNAG-GCC 124905916
## 223 TRNAG-GCC 124905918
## 224 TRNAG-GCC 124905921
## 225 TRNAG-GCC 124905923
## 226 TRNAG-GCC 124905925
## 227 TRNAG-GCC 124905927
## 228 TRNAG-GCC 124905929
## 229 TRNAG-GCC 124905931
## 230 TRNAG-GCC 124905933
## 231 TRNAG-GCC 124905847
## 232 TRNAG-GCC 124905849
## 233 TRNAG-GCC 124905851
## 234 TRNAG-GCC 124905853
## 235 TRNAG-GCC 124905907
## 236 TRNAG-GCC 124905910
## 237 TRNAG-GCC 124905912
## 238 TRNAG-GCC 124905914
## 239 TRNAG-GCC 124905916
## 240 TRNAG-GCC 124905918
## 241 TRNAG-GCC 124905921
## 242 TRNAG-GCC 124905923
## 243 TRNAG-GCC 124905925
## 244 TRNAG-GCC 124905927
## 245 TRNAG-GCC 124905929
## 246 TRNAG-GCC 124905931
## 247 TRNAG-GCC 124905933
## 248 TRNAG-GCC 124905847
## 249 TRNAG-GCC 124905849
## 250 TRNAG-GCC 124905851
## 251 TRNAG-GCC 124905853
## 252 TRNAG-GCC 124905907
## 253 TRNAG-GCC 124905910
## 254 TRNAG-GCC 124905912
## 255 TRNAG-GCC 124905914
## 256 TRNAG-GCC 124905916
## 257 TRNAG-GCC 124905918
## 258 TRNAG-GCC 124905921
## 259 TRNAG-GCC 124905923
## 260 TRNAG-GCC 124905925
## 261 TRNAG-GCC 124905927
## 262 TRNAG-GCC 124905929
## 263 TRNAG-GCC 124905931
## 264 TRNAG-GCC 124905933
## 265 TRNAG-GCC 124905847
## 266 TRNAG-GCC 124905849
## 267 TRNAG-GCC 124905851
## 268 TRNAG-GCC 124905853
## 269 TRNAG-GCC 124905907
## 270 TRNAG-GCC 124905910
## 271 TRNAG-GCC 124905912
## 272 TRNAG-GCC 124905914
## 273 TRNAG-GCC 124905916
## 274 TRNAG-GCC 124905918
## 275 TRNAG-GCC 124905921
## 276 TRNAG-GCC 124905923
## 277 TRNAG-GCC 124905925
## 278 TRNAG-GCC 124905927
## 279 TRNAG-GCC 124905929
## 280 TRNAG-GCC 124905931
## 281 TRNAG-GCC 124905933
## 282 TRNAG-GCC 124905847
## 283 TRNAG-GCC 124905849
## 284 TRNAG-GCC 124905851
## 285 TRNAG-GCC 124905853
## 286 TRNAG-GCC 124905907
## 287 TRNAG-GCC 124905910
## 288 TRNAG-GCC 124905912
## 289 TRNAG-GCC 124905914
## 290 TRNAG-GCC 124905916
## 291 TRNAG-GCC 124905918
## 292 TRNAG-GCC 124905921
## 293 TRNAG-GCC 124905923
## 294 TRNAG-GCC 124905925
## 295 TRNAG-GCC 124905927
## 296 TRNAG-GCC 124905929
## 297 TRNAG-GCC 124905931
## 298 TRNAG-GCC 124905933
## 299 TRNAG-GCC 124905847
## 300 TRNAG-GCC 124905849
## 301 TRNAG-GCC 124905851
## 302 TRNAG-GCC 124905853
## 303 TRNAG-GCC 124905907
## 304 TRNAG-GCC 124905910
## 305 TRNAG-GCC 124905912
## 306 TRNAG-GCC 124905914
## 307 TRNAG-GCC 124905916
## 308 TRNAG-GCC 124905918
## 309 TRNAG-GCC 124905921
## 310 TRNAG-GCC 124905923
## 311 TRNAG-GCC 124905925
## 312 TRNAG-GCC 124905927
## 313 TRNAG-GCC 124905929
## 314 TRNAG-GCC 124905931
## 315 TRNAG-GCC 124905933
## 316 TRNAG-GCC 124905847
## 317 TRNAG-GCC 124905849
## 318 TRNAG-GCC 124905851
## 319 TRNAG-GCC 124905853
## 320 TRNAG-GCC 124905907
## 321 TRNAG-GCC 124905910
## 322 TRNAG-GCC 124905912
## 323 TRNAG-GCC 124905914
## 324 TRNAG-GCC 124905916
## 325 TRNAG-GCC 124905918
## 326 TRNAG-GCC 124905921
## 327 TRNAG-GCC 124905923
## 328 TRNAG-GCC 124905925
## 329 TRNAG-GCC 124905927
## 330 TRNAG-GCC 124905929
## 331 TRNAG-GCC 124905931
## 332 TRNAG-GCC 124905933
## 333 TRNAG-GCC 124905847
## 334 TRNAG-GCC 124905849
## 335 TRNAG-GCC 124905851
## 336 TRNAG-GCC 124905853
## 337 TRNAG-GCC 124905907
## 338 TRNAG-GCC 124905910
## 339 TRNAG-GCC 124905912
## 340 TRNAG-GCC 124905914
## 341 TRNAG-GCC 124905916
## 342 TRNAG-GCC 124905918
## 343 TRNAG-GCC 124905921
## 344 TRNAG-GCC 124905923
## 345 TRNAG-GCC 124905925
## 346 TRNAG-GCC 124905927
## 347 TRNAG-GCC 124905929
## 348 TRNAG-GCC 124905931
## 349 TRNAG-GCC 124905933
## 350 TRNAG-GCC 124905847
## 351 TRNAG-GCC 124905849
## 352 TRNAG-GCC 124905851
## 353 TRNAG-GCC 124905853
## 354 TRNAG-GCC 124905907
## 355 TRNAG-GCC 124905910
## 356 TRNAG-GCC 124905912
## 357 TRNAG-GCC 124905914
## 358 TRNAG-GCC 124905916
## 359 TRNAG-GCC 124905918
## 360 TRNAG-GCC 124905921
## 361 TRNAG-GCC 124905923
## 362 TRNAG-GCC 124905925
## 363 TRNAG-GCC 124905927
## 364 TRNAG-GCC 124905929
## 365 TRNAG-GCC 124905931
## 366 TRNAG-GCC 124905933
## 367 TRNAL-CAG 124905848
## 368 TRNAL-CAG 124905850
## 369 TRNAL-CAG 124905852
## 370 TRNAL-CAG 124905906
## 371 TRNAL-CAG 124905909
## 372 TRNAL-CAG 124905911
## 373 TRNAL-CAG 124905913
## 374 TRNAL-CAG 124905915
## 375 TRNAL-CAG 124905917
## 376 TRNAL-CAG 124905920
## 377 TRNAL-CAG 124905922
## 378 TRNAL-CAG 124905924
## 379 TRNAL-CAG 124905926
## 380 TRNAL-CAG 124905928
## 381 TRNAL-CAG 124905930
## 382 TRNAL-CAG 124905932
## 383 TRNAL-CAG 124905934
## 384 TRNAL-CAG 124905848
## 385 TRNAL-CAG 124905850
## 386 TRNAL-CAG 124905852
## 387 TRNAL-CAG 124905906
## 388 TRNAL-CAG 124905909
## 389 TRNAL-CAG 124905911
## 390 TRNAL-CAG 124905913
## 391 TRNAL-CAG 124905915
## 392 TRNAL-CAG 124905917
## 393 TRNAL-CAG 124905920
## 394 TRNAL-CAG 124905922
## 395 TRNAL-CAG 124905924
## 396 TRNAL-CAG 124905926
## 397 TRNAL-CAG 124905928
## 398 TRNAL-CAG 124905930
## 399 TRNAL-CAG 124905932
## 400 TRNAL-CAG 124905934
## 401 TRNAL-CAG 124905848
## 402 TRNAL-CAG 124905850
## 403 TRNAL-CAG 124905852
## 404 TRNAL-CAG 124905906
## 405 TRNAL-CAG 124905909
## 406 TRNAL-CAG 124905911
## 407 TRNAL-CAG 124905913
## 408 TRNAL-CAG 124905915
## 409 TRNAL-CAG 124905917
## 410 TRNAL-CAG 124905920
## 411 TRNAL-CAG 124905922
## 412 TRNAL-CAG 124905924
## 413 TRNAL-CAG 124905926
## 414 TRNAL-CAG 124905928
## 415 TRNAL-CAG 124905930
## 416 TRNAL-CAG 124905932
## 417 TRNAL-CAG 124905934
## 418 TRNAL-CAG 124905848
## 419 TRNAL-CAG 124905850
## 420 TRNAL-CAG 124905852
## 421 TRNAL-CAG 124905906
## 422 TRNAL-CAG 124905909
## 423 TRNAL-CAG 124905911
## 424 TRNAL-CAG 124905913
## 425 TRNAL-CAG 124905915
## 426 TRNAL-CAG 124905917
## 427 TRNAL-CAG 124905920
## 428 TRNAL-CAG 124905922
## 429 TRNAL-CAG 124905924
## 430 TRNAL-CAG 124905926
## 431 TRNAL-CAG 124905928
## 432 TRNAL-CAG 124905930
## 433 TRNAL-CAG 124905932
## 434 TRNAL-CAG 124905934
## 435 TRNAL-CAG 124905848
## 436 TRNAL-CAG 124905850
## 437 TRNAL-CAG 124905852
## 438 TRNAL-CAG 124905906
## 439 TRNAL-CAG 124905909
## 440 TRNAL-CAG 124905911
## 441 TRNAL-CAG 124905913
## 442 TRNAL-CAG 124905915
## 443 TRNAL-CAG 124905917
## 444 TRNAL-CAG 124905920
## 445 TRNAL-CAG 124905922
## 446 TRNAL-CAG 124905924
## 447 TRNAL-CAG 124905926
## 448 TRNAL-CAG 124905928
## 449 TRNAL-CAG 124905930
## 450 TRNAL-CAG 124905932
## 451 TRNAL-CAG 124905934
## 452 TRNAL-CAG 124905848
## 453 TRNAL-CAG 124905850
## 454 TRNAL-CAG 124905852
## 455 TRNAL-CAG 124905906
## 456 TRNAL-CAG 124905909
## 457 TRNAL-CAG 124905911
## 458 TRNAL-CAG 124905913
## 459 TRNAL-CAG 124905915
## 460 TRNAL-CAG 124905917
## 461 TRNAL-CAG 124905920
## 462 TRNAL-CAG 124905922
## 463 TRNAL-CAG 124905924
## 464 TRNAL-CAG 124905926
## 465 TRNAL-CAG 124905928
## 466 TRNAL-CAG 124905930
## 467 TRNAL-CAG 124905932
## 468 TRNAL-CAG 124905934
## 469 TRNAL-CAG 124905848
## 470 TRNAL-CAG 124905850
## 471 TRNAL-CAG 124905852
## 472 TRNAL-CAG 124905906
## 473 TRNAL-CAG 124905909
## 474 TRNAL-CAG 124905911
## 475 TRNAL-CAG 124905913
## 476 TRNAL-CAG 124905915
## 477 TRNAL-CAG 124905917
## 478 TRNAL-CAG 124905920
## 479 TRNAL-CAG 124905922
## 480 TRNAL-CAG 124905924
## 481 TRNAL-CAG 124905926
## 482 TRNAL-CAG 124905928
## 483 TRNAL-CAG 124905930
## 484 TRNAL-CAG 124905932
## 485 TRNAL-CAG 124905934
## 486 TRNAL-CAG 124905848
## 487 TRNAL-CAG 124905850
## 488 TRNAL-CAG 124905852
## 489 TRNAL-CAG 124905906
## 490 TRNAL-CAG 124905909
## 491 TRNAL-CAG 124905911
## 492 TRNAL-CAG 124905913
## 493 TRNAL-CAG 124905915
## 494 TRNAL-CAG 124905917
## 495 TRNAL-CAG 124905920
## 496 TRNAL-CAG 124905922
## 497 TRNAL-CAG 124905924
## 498 TRNAL-CAG 124905926
## 499 TRNAL-CAG 124905928
## 500 TRNAL-CAG 124905930
## 501 TRNAL-CAG 124905932
## 502 TRNAL-CAG 124905934
## 503 TRNAL-CAG 124905848
## 504 TRNAL-CAG 124905850
## 505 TRNAL-CAG 124905852
## 506 TRNAL-CAG 124905906
## 507 TRNAL-CAG 124905909
## 508 TRNAL-CAG 124905911
## 509 TRNAL-CAG 124905913
## 510 TRNAL-CAG 124905915
## 511 TRNAL-CAG 124905917
## 512 TRNAL-CAG 124905920
## 513 TRNAL-CAG 124905922
## 514 TRNAL-CAG 124905924
## 515 TRNAL-CAG 124905926
## 516 TRNAL-CAG 124905928
## 517 TRNAL-CAG 124905930
## 518 TRNAL-CAG 124905932
## 519 TRNAL-CAG 124905934
## 520 TRNAL-CAG 124905848
## 521 TRNAL-CAG 124905850
## 522 TRNAL-CAG 124905852
## 523 TRNAL-CAG 124905906
## 524 TRNAL-CAG 124905909
## 525 TRNAL-CAG 124905911
## 526 TRNAL-CAG 124905913
## 527 TRNAL-CAG 124905915
## 528 TRNAL-CAG 124905917
## 529 TRNAL-CAG 124905920
## 530 TRNAL-CAG 124905922
## 531 TRNAL-CAG 124905924
## 532 TRNAL-CAG 124905926
## 533 TRNAL-CAG 124905928
## 534 TRNAL-CAG 124905930
## 535 TRNAL-CAG 124905932
## 536 TRNAL-CAG 124905934
## 537 TRNAL-CAG 124905848
## 538 TRNAL-CAG 124905850
## 539 TRNAL-CAG 124905852
## 540 TRNAL-CAG 124905906
## 541 TRNAL-CAG 124905909
## 542 TRNAL-CAG 124905911
## 543 TRNAL-CAG 124905913
## 544 TRNAL-CAG 124905915
## 545 TRNAL-CAG 124905917
## 546 TRNAL-CAG 124905920
## 547 TRNAL-CAG 124905922
## 548 TRNAL-CAG 124905924
## 549 TRNAL-CAG 124905926
## 550 TRNAL-CAG 124905928
## 551 TRNAL-CAG 124905930
## 552 TRNAL-CAG 124905932
## 553 TRNAL-CAG 124905934
## 554 TRNAL-CAG 124905848
## 555 TRNAL-CAG 124905850
## 556 TRNAL-CAG 124905852
## 557 TRNAL-CAG 124905906
## 558 TRNAL-CAG 124905909
## 559 TRNAL-CAG 124905911
## 560 TRNAL-CAG 124905913
## 561 TRNAL-CAG 124905915
## 562 TRNAL-CAG 124905917
## 563 TRNAL-CAG 124905920
## 564 TRNAL-CAG 124905922
## 565 TRNAL-CAG 124905924
## 566 TRNAL-CAG 124905926
## 567 TRNAL-CAG 124905928
## 568 TRNAL-CAG 124905930
## 569 TRNAL-CAG 124905932
## 570 TRNAL-CAG 124905934
## 571 TRNAL-CAG 124905848
## 572 TRNAL-CAG 124905850
## 573 TRNAL-CAG 124905852
## 574 TRNAL-CAG 124905906
## 575 TRNAL-CAG 124905909
## 576 TRNAL-CAG 124905911
## 577 TRNAL-CAG 124905913
## 578 TRNAL-CAG 124905915
## 579 TRNAL-CAG 124905917
## 580 TRNAL-CAG 124905920
## 581 TRNAL-CAG 124905922
## 582 TRNAL-CAG 124905924
## 583 TRNAL-CAG 124905926
## 584 TRNAL-CAG 124905928
## 585 TRNAL-CAG 124905930
## 586 TRNAL-CAG 124905932
## 587 TRNAL-CAG 124905934
## 588 TRNAL-CAG 124905848
## 589 TRNAL-CAG 124905850
## 590 TRNAL-CAG 124905852
## 591 TRNAL-CAG 124905906
## 592 TRNAL-CAG 124905909
## 593 TRNAL-CAG 124905911
## 594 TRNAL-CAG 124905913
## 595 TRNAL-CAG 124905915
## 596 TRNAL-CAG 124905917
## 597 TRNAL-CAG 124905920
## 598 TRNAL-CAG 124905922
## 599 TRNAL-CAG 124905924
## 600 TRNAL-CAG 124905926
## 601 TRNAL-CAG 124905928
## 602 TRNAL-CAG 124905930
## 603 TRNAL-CAG 124905932
## 604 TRNAL-CAG 124905934
## 605 TRNAL-CAG 124905848
## 606 TRNAL-CAG 124905850
## 607 TRNAL-CAG 124905852
## 608 TRNAL-CAG 124905906
## 609 TRNAL-CAG 124905909
## 610 TRNAL-CAG 124905911
## 611 TRNAL-CAG 124905913
## 612 TRNAL-CAG 124905915
## 613 TRNAL-CAG 124905917
## 614 TRNAL-CAG 124905920
## 615 TRNAL-CAG 124905922
## 616 TRNAL-CAG 124905924
## 617 TRNAL-CAG 124905926
## 618 TRNAL-CAG 124905928
## 619 TRNAL-CAG 124905930
## 620 TRNAL-CAG 124905932
## 621 TRNAL-CAG 124905934
## 622 TRNAL-CAG 124905848
## 623 TRNAL-CAG 124905850
## 624 TRNAL-CAG 124905852
## 625 TRNAL-CAG 124905906
## 626 TRNAL-CAG 124905909
## 627 TRNAL-CAG 124905911
## 628 TRNAL-CAG 124905913
## 629 TRNAL-CAG 124905915
## 630 TRNAL-CAG 124905917
## 631 TRNAL-CAG 124905920
## 632 TRNAL-CAG 124905922
## 633 TRNAL-CAG 124905924
## 634 TRNAL-CAG 124905926
## 635 TRNAL-CAG 124905928
## 636 TRNAL-CAG 124905930
## 637 TRNAL-CAG 124905932
## 638 TRNAL-CAG 124905934
## 639 TRNAD-GUC 124905854
## 640 TRNAD-GUC 124905857
## 641 TRNAD-GUC 124905860
## 642 TRNAD-GUC 124905863
## 643 TRNAD-GUC 124905866
## 644 TRNAD-GUC 124905869
## 645 TRNAD-GUC 124905872
## 646 TRNAD-GUC 124905875
## 647 TRNAD-GUC 124905878
## 648 TRNAD-GUC 124905881
## 649 TRNAD-GUC 124905884
## 650 TRNAD-GUC 124905887
## 651 TRNAD-GUC 124905890
## 652 TRNAD-GUC 124905893
## 653 TRNAD-GUC 124905896
## 654 TRNAD-GUC 124905899
## 655 TRNAD-GUC 124905902
## 656 TRNAD-GUC 124905854
## 657 TRNAD-GUC 124905857
## 658 TRNAD-GUC 124905860
## 659 TRNAD-GUC 124905863
## 660 TRNAD-GUC 124905866
## 661 TRNAD-GUC 124905869
## 662 TRNAD-GUC 124905872
## 663 TRNAD-GUC 124905875
## 664 TRNAD-GUC 124905878
## 665 TRNAD-GUC 124905881
## 666 TRNAD-GUC 124905884
## 667 TRNAD-GUC 124905887
## 668 TRNAD-GUC 124905890
## 669 TRNAD-GUC 124905893
## 670 TRNAD-GUC 124905896
## 671 TRNAD-GUC 124905899
## 672 TRNAD-GUC 124905902
## 673 TRNAD-GUC 124905854
## 674 TRNAD-GUC 124905857
## 675 TRNAD-GUC 124905860
## 676 TRNAD-GUC 124905863
## 677 TRNAD-GUC 124905866
## 678 TRNAD-GUC 124905869
## 679 TRNAD-GUC 124905872
## 680 TRNAD-GUC 124905875
## 681 TRNAD-GUC 124905878
## 682 TRNAD-GUC 124905881
## 683 TRNAD-GUC 124905884
## 684 TRNAD-GUC 124905887
## 685 TRNAD-GUC 124905890
## 686 TRNAD-GUC 124905893
## 687 TRNAD-GUC 124905896
## 688 TRNAD-GUC 124905899
## 689 TRNAD-GUC 124905902
## 690 TRNAD-GUC 124905854
## 691 TRNAD-GUC 124905857
## 692 TRNAD-GUC 124905860
## 693 TRNAD-GUC 124905863
## 694 TRNAD-GUC 124905866
## 695 TRNAD-GUC 124905869
## 696 TRNAD-GUC 124905872
## 697 TRNAD-GUC 124905875
## 698 TRNAD-GUC 124905878
## 699 TRNAD-GUC 124905881
## 700 TRNAD-GUC 124905884
## 701 TRNAD-GUC 124905887
## 702 TRNAD-GUC 124905890
## 703 TRNAD-GUC 124905893
## 704 TRNAD-GUC 124905896
## 705 TRNAD-GUC 124905899
## 706 TRNAD-GUC 124905902
## 707 TRNAD-GUC 124905854
## 708 TRNAD-GUC 124905857
## 709 TRNAD-GUC 124905860
## 710 TRNAD-GUC 124905863
## 711 TRNAD-GUC 124905866
## 712 TRNAD-GUC 124905869
## 713 TRNAD-GUC 124905872
## 714 TRNAD-GUC 124905875
## 715 TRNAD-GUC 124905878
## 716 TRNAD-GUC 124905881
## 717 TRNAD-GUC 124905884
## 718 TRNAD-GUC 124905887
## 719 TRNAD-GUC 124905890
## 720 TRNAD-GUC 124905893
## 721 TRNAD-GUC 124905896
## 722 TRNAD-GUC 124905899
## 723 TRNAD-GUC 124905902
## 724 TRNAD-GUC 124905854
## 725 TRNAD-GUC 124905857
## 726 TRNAD-GUC 124905860
## 727 TRNAD-GUC 124905863
## 728 TRNAD-GUC 124905866
## 729 TRNAD-GUC 124905869
## 730 TRNAD-GUC 124905872
## 731 TRNAD-GUC 124905875
## 732 TRNAD-GUC 124905878
## 733 TRNAD-GUC 124905881
## 734 TRNAD-GUC 124905884
## 735 TRNAD-GUC 124905887
## 736 TRNAD-GUC 124905890
## 737 TRNAD-GUC 124905893
## 738 TRNAD-GUC 124905896
## 739 TRNAD-GUC 124905899
## 740 TRNAD-GUC 124905902
## 741 TRNAD-GUC 124905854
## 742 TRNAD-GUC 124905857
## 743 TRNAD-GUC 124905860
## 744 TRNAD-GUC 124905863
## 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 124905904A.5 Exercise 5:
So to retrieve this information using select you need to do it like this:
res1 <- select(TxDb.Hsapiens.UCSC.hg19.knownGene,
keys(TxDb.Hsapiens.UCSC.hg19.knownGene, keytype="TXID"),
columns=c("GENEID","TXNAME","TXCHROM"), keytype="TXID")## 'select()' returned 1:1 mapping between keys and columnshead(res1)## TXID GENEID TXNAME TXCHROM
## 1 1 100287102 uc001aaa.3 chr1
## 2 2 100287102 uc010nxq.1 chr1
## 3 3 100287102 uc010nxr.1 chr1
## 4 4 79501 uc001aal.1 chr1
## 5 5 <NA> uc001aaq.2 chr1
## 6 6 <NA> uc001aar.2 chr1And to do it using transcripts you do it like this:
res2 <- transcripts(TxDb.Hsapiens.UCSC.hg19.knownGene,
columns = c("gene_id","tx_name"))
head(res2)## GRanges object with 6 ranges and 2 metadata columns:
## seqnames ranges strand | gene_id tx_name
## <Rle> <IRanges> <Rle> | <CharacterList> <character>
## [1] chr3 238279-451097 + | 10752 uc003bot.3
## [2] chr3 238279-451097 + | 10752 uc003bou.3
## [3] chr3 239326-290282 + | 10752 uc003bov.2
## [4] chr3 239326-440831 + | 10752 uc003bow.2
## [5] chr3 361366-451097 + | 10752 uc011asi.2
## [6] chr3 577914-887698 + | uc003boy.1
## -------
## seqinfo: 2 sequences from hg19 genomeNotice that in the 2nd case we don’t have to ask for the chromosome, as transcripts() returns a GRanges object, so the chromosome will automatically be returned as part of the object.
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 columnshead(res2)## GRanges object with 6 ranges and 1 metadata column:
## seqnames ranges strand | SYMBOL
## <Rle> <IRanges> <Rle> | <CharacterList>
## [1] chr3 238279-451097 + | CHL1
## [2] chr3 238279-451097 + | CHL1
## [3] chr3 239326-290282 + | CHL1
## [4] chr3 239326-440831 + | CHL1
## [5] chr3 361366-451097 + | CHL1
## [6] chr3 577914-887698 + | <NA>
## -------
## seqinfo: 2 sequences from hg19 genomeA.8 Exercise 8:
columns(Homo.sapiens)## [1] "ACCNUM" "ALIAS" "CDSCHROM" "CDSEND" "CDSID"
## [6] "CDSNAME" "CDSSTART" "CDSSTRAND" "DEFINITION" "ENSEMBL"
## [11] "ENSEMBLPROT" "ENSEMBLTRANS" "ENTREZID" "ENZYME" "EVIDENCE"
## [16] "EVIDENCEALL" "EXONCHROM" "EXONEND" "EXONID" "EXONNAME"
## [21] "EXONRANK" "EXONSTART" "EXONSTRAND" "GENEID" "GENENAME"
## [26] "GENETYPE" "GO" "GOALL" "GOID" "IPI"
## [31] "MAP" "OMIM" "ONTOLOGY" "ONTOLOGYALL" "PATH"
## [36] "PFAM" "PMID" "PROSITE" "REFSEQ" "SYMBOL"
## [41] "TERM" "TXCHROM" "TXEND" "TXID" "TXNAME"
## [46] "TXSTART" "TXSTRAND" "TXTYPE" "UCSCKG" "UNIPROT"columns(org.Hs.eg.db)## [1] "ACCNUM" "ALIAS" "ENSEMBL" "ENSEMBLPROT" "ENSEMBLTRANS"
## [6] "ENTREZID" "ENZYME" "EVIDENCE" "EVIDENCEALL" "GENENAME"
## [11] "GENETYPE" "GO" "GOALL" "IPI" "MAP"
## [16] "OMIM" "ONTOLOGY" "ONTOLOGYALL" "PATH" "PFAM"
## [21] "PMID" "PROSITE" "REFSEQ" "SYMBOL" "UCSCKG"
## [26] "UNIPROT"columns(TxDb.Hsapiens.UCSC.hg19.knownGene)## [1] "CDSCHROM" "CDSEND" "CDSID" "CDSNAME" "CDSSTART"
## [6] "CDSSTRAND" "EXONCHROM" "EXONEND" "EXONID" "EXONNAME"
## [11] "EXONRANK" "EXONSTART" "EXONSTRAND" "GENEID" "TXCHROM"
## [16] "TXEND" "TXID" "TXNAME" "TXSTART" "TXSTRAND"
## [21] "TXTYPE"## You might also want to look at this:
transcripts(Homo.sapiens, columns=c("SYMBOL","CHRLOC"))## 'select()' returned 1:1 mapping between keys and columns## GRanges object with 5506 ranges and 1 metadata column:
## seqnames ranges strand | SYMBOL
## <Rle> <IRanges> <Rle> | <CharacterList>
## [1] chr3 238279-451097 + | CHL1
## [2] chr3 238279-451097 + | CHL1
## [3] chr3 239326-290282 + | CHL1
## [4] chr3 239326-440831 + | CHL1
## [5] chr3 361366-451097 + | CHL1
## ... ... ... ... . ...
## [5502] chr18 77732867-77748532 - | TXNL4A
## [5503] chr18 77732867-77748532 - | TXNL4A
## [5504] chr18 77732867-77793915 - | TXNL4A
## [5505] chr18 77915117-78005397 - | PARD6G
## [5506] chr18 77941005-78005397 - | PARD6G
## -------
## seqinfo: 2 sequences from hg19 genomeThe key difference is that the TXSTART refers to the start of a transcript and originates in the TxDb object from the TxDb.Hsapiens.UCSC.hg19.knownGene package, while the CHRLOC refers to the same thing but originates in the OrgDb object from the org.Hs.eg.db package. The point of origin is significant because the TxDb object represents a transcriptome from UCSC and the OrgDb is primarily gene centric data that originates at NCBI. The upshot is that CHRLOC will not have as many regions represented as TXSTART, since there has to be an official gene for there to even be a record. The CHRLOC data is also locked in for org.Hs.eg.db as data for hg19, whereas you can swap in a different TxDb object to match the genome you are using to make it hg18 etc. For these reasons, we strongly recommend using TXSTART instead of CHRLOC. Howeverm CHRLOC still remains in the org packages for historical reasons.
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 columnsxk## [1] "51" "179" "189" "239" "240" "241"select verifies the results
select(Homo.sapiens, xk, "SYMBOL", "ENTREZID")## 'select()' returned 1:1 mapping between keys and columns## ENTREZID SYMBOL
## 1 51 ACOX1
## 2 179 AGMX2
## 3 189 AGXT
## 4 239 ALOX12
## 5 240 ALOX5
## 6 241 ALOX5APA.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)
resA.11 Exercise 11:
ensembl <- useEnsembl(biomart = "ensembl", dataset="hsapiens_gene_ensembl")
ids <- c("1")
getBM(attributes=c('go_id', 'entrezgene_id'),
filters = 'entrezgene_id',
values = ids,
mart = ensembl)## go_id entrezgene_id
## 1 1
## 2 GO: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 1A.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 CCWhen this exercise was written, there was a different number of GO terms returned from biomaRt than from org.Hs.eg.db. This may not always be true in the future though as both of these resources are updated. It is expected however that this web service, (which is updated continuously) will fall in and out of sync with the org.Hs.eg.db package (which is updated twice a year). This is an important difference as each approach has different advantages and disadvantages. The advantage to updating continuously is that you always have the very latest annotations which are frequently different for something like GO terms. The advantage to using a package is that the results are frozen to a release of Bioconductor. And this can help you to get the same answers that you get today (reproducibility), a few years from now.
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