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: 3.17
Package version: 1.24.1
2 Introduction
Annotation resources make up a significant proportion of the Bioconductor project[1]. And there are also a diverse set of online resources available which are accessed using specific packages. This walkthrough will describe the most popular of these resources and give some high level examples on how to use them.
Bioconductor annotation resources have traditionally been used near the end of an analysis. After the bulk of the data analysis, annotations would be used interpretatively to learn about the most significant results. But increasingly, they are also used as a starting point or even as an intermediate step to help guide a study that is still in progress. In addition to this, what it means for something to be an annotation is also becoming less clear than it once was. It used to be clear that annotations were only those things that had been established after multiple different studies had been performed (such as the primary role of a gene product). But today many large data sets are treated by communities in much the same way that classic annotations once were: as a reference for additional comparisons.
Another change that is underway with annotations in Bioconductor is in the way that they are obtained. In the past annotations existed almost exclusively as separate annotation packages[2,3,4]. Today packages are still an enormous source of annotations. The current release repository contains over eight hundred annotation packages. This table summarizes some of the more important classes of annotation objects that are often accessed using packages:
| Object Type | Example Package Name | Contents |
|---|---|---|
| TxDb |
TxDb.Hsapiens.UCSC.hg19.knownGene
|
Transcriptome ranges for the known gene track of Homo sapiens, e.g., introns, exons, UTR regions. |
| OrgDb |
org.Hs.eg.db
|
Gene-based information for Homo sapiens; useful for mapping between gene IDs, Names, Symbols, GO and KEGG identifiers, etc. |
| BSgenome |
BSgenome.Hsapiens.UCSC.hg19
|
Full genome sequence for Homo sapiens. |
| Organism.dplyr |
src_organism
|
Collection of multiple annotations for a common organism and genome build. |
| AnnotationHub |
AnnotationHub
|
Provides a convenient interface to annotations from many different sources; objects are returned as fully parsed Bioconductor data objects or as the name of a file on disk. |
But in spite of the popularity of annotation packages, annotations are increasingly also being pulled down from web services like biomaRt[5,6,7] or from the AnnotationHub[8]. And both of these represent enormous resources for annotation data.
In part because of the rapidly evolving landscape, it is currently impossible in a single document to cover every possible annotation or even every kind of annotation present in Bioconductor. So here we will instead go over the most popular annotation resources and describe them in a way intended to expose common patterns used for accessing them. The hope is that a user with this information will be able to make educated guesses about how to find and use additional resources that will inevitably be added later. Topics that will be covered will include the following:
3 Set Up
In this chapter we make use of several Bioconductor packages. You can install
them with BiocManager::install():
if (!"BiocManager" %in% rownames(installed.packages()))
install.packages("BiocManager")
BiocManager::install(c("AnnotationHub", "Homo.sapiens",
"Organism.dplyr",
"TxDb.Hsapiens.UCSC.hg19.knownGene",
"TxDb.Hsapiens.UCSC.hg38.knownGene",
"BSgenome.Hsapiens.UCSC.hg19", "biomaRt",
"TxDb.Athaliana.BioMart.plantsmart22"))The usage of the installed packages will be described in detail within the Usage section.
4 Using AnnotationHub
The top of the list for learning about annotation resources is the relatively new AnnotationHub package[8]. The AnnotationHub was created to provide a convenient access point for end users to find a large range of different annotation objects for use with Bioconductor. Resources found in the AnnotationHub are easy to discover and are presented to the user as familiar Bioconductor data objects. Because it is a recent addition, the AnnotationHub allows access to a broad range of annotation like objects, some of which may not have been considered annotations even a few years ago. To get started with the AnnotationHub users only need to load the package and then create a local AnnotationHub object like this:
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 70130 records
## # snapshotDate(): 2023-04-24
## # $dataprovider: Ensembl, BroadInstitute, UCSC, ftp://ftp.ncbi.nlm.nih.gov/g...
## # $species: Homo sapiens, Mus musculus, Drosophila melanogaster, Bos taurus,...
## # $rdataclass: GRanges, TwoBitFile, BigWigFile, EnsDb, Rle, OrgDb, ChainFile...
## # additional mcols(): taxonomyid, genome, description,
## # coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
## # rdatapath, sourceurl, sourcetype
## # retrieve records with, e.g., 'object[["AH5012"]]'
##
## title
## AH5012 | Chromosome Band
## AH5013 | STS Markers
## AH5014 | FISH Clones
## AH5015 | Recomb Rate
## AH5016 | ENCODE Pilot
## ... ...
## AH113536 | org.Alternaria_alternata.eg.sqlite
## AH113537 | org.Alternaria_tenuis.eg.sqlite
## AH113538 | org.Torula_alternata.eg.sqlite
## AH113539 | org.Psilocybe_cubensis.eg.sqlite
## AH113540 | org.Stropharia_cubensis.eg.sqliteAs you can see from the object above, there are a LOT of different resources available. So normally when you get an AnnotationHub object the 1st thing you want to do is to filter it to remove unwanted resources.
Fortunately, the AnnotationHub has several different kinds of metadata that you can use for searching and subsetting. To see the different categories all you need to do is to type the name of your AnnotationHub object and then tab complete from the ‘$’ operator. And to see all possible contents of one of these categories you can pass that value in to unique like this:
unique(ah$dataprovider)## [1] "UCSC"
## [2] "Ensembl"
## [3] "RefNet"
## [4] "Inparanoid8"
## [5] "NHLBI"
## [6] "ChEA"
## [7] "Pazar"
## [8] "NIH Pathway Interaction Database"
## [9] "Haemcode"
## [10] "BroadInstitute"
## [11] "PRIDE"
## [12] "Gencode"
## [13] "CRIBI"
## [14] "Genoscope"
## [15] "MISO, VAST-TOOLS, UCSC"
## [16] "UWashington"
## [17] "Stanford"
## [18] "dbSNP"
## [19] "BioMart"
## [20] "GeneOntology"
## [21] "KEGG"
## [22] "URGI"
## [23] "EMBL-EBI"
## [24] "MicrosporidiaDB"
## [25] "FungiDB"
## [26] "TriTrypDB"
## [27] "ToxoDB"
## [28] "AmoebaDB"
## [29] "PlasmoDB"
## [30] "PiroplasmaDB"
## [31] "CryptoDB"
## [32] "TrichDB"
## [33] "GiardiaDB"
## [34] "The Gene Ontology Consortium"
## [35] "ENCODE Project"
## [36] "SchistoDB"
## [37] "NCBI/UniProt"
## [38] "GENCODE"
## [39] "http://www.pantherdb.org"
## [40] "RMBase v2.0"
## [41] "snoRNAdb"
## [42] "tRNAdb"
## [43] "NCBI"
## [44] "DrugAge, DrugBank, Broad Institute"
## [45] "DrugAge"
## [46] "DrugBank"
## [47] "Broad Institute"
## [48] "HMDB, EMBL-EBI, EPA"
## [49] "STRING"
## [50] "OMA"
## [51] "OrthoDB"
## [52] "PathBank"
## [53] "EBI/EMBL"
## [54] "NCBI,DBCLS"
## [55] "FANTOM5,DLRP,IUPHAR,HPRD,STRING,SWISSPROT,TREMBL,ENSEMBL,CELLPHONEDB,BADERLAB,SINGLECELLSIGNALR,HOMOLOGENE"
## [56] "WikiPathways"
## [57] "VAST-TOOLS"
## [58] "pyGenomeTracks "
## [59] "NA"
## [60] "UoE"
## [61] "TargetScan,miRTarBase,USCS,ENSEMBL"
## [62] "TargetScan"
## [63] "QuickGO"
## [64] "CIS-BP"
## [65] "CTCFBSDB 2.0"
## [66] "HOCOMOCO v11"
## [67] "JASPAR 2022"
## [68] "Jolma 2013"
## [69] "SwissRegulon"
## [70] "ENCODE SCREEN v3"
## [71] "MassBank"
## [72] "excluderanges"
## [73] "ENCODE"
## [74] "GitHub"
## [75] "Stanford.edu"
## [76] "Publication"
## [77] "CHM13"
## [78] "UCSChub"
## [79] "MGI"
## [80] "ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/"One of the most valuable ways in which the data is labeled is according to the kind of R object that will be returned to you.
unique(ah$rdataclass)## [1] "GRanges" "data.frame"
## [3] "Inparanoid8Db" "TwoBitFile"
## [5] "ChainFile" "SQLiteConnection"
## [7] "biopax" "BigWigFile"
## [9] "AAStringSet" "MSnSet"
## [11] "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(): 2023-04-24
## # $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 1969 records
## # snapshotDate(): 2023-04-24
## # $dataprovider: ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/
## # $species: Escherichia coli, greater Indian_fruit_bat, Zychaea mexicana, Zo...
## # $rdataclass: OrgDb
## # additional mcols(): taxonomyid, genome, description,
## # coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
## # rdatapath, sourceurl, sourcetype
## # retrieve records with, e.g., 'object[["AH111566"]]'
##
## title
## AH111566 | org.Ag.eg.db.sqlite
## AH111567 | org.At.tair.db.sqlite
## AH111568 | org.Bt.eg.db.sqlite
## AH111569 | org.Cf.eg.db.sqlite
## AH111570 | org.Gg.eg.db.sqlite
## ... ...
## AH113536 | org.Alternaria_alternata.eg.sqlite
## AH113537 | org.Alternaria_tenuis.eg.sqlite
## AH113538 | org.Torula_alternata.eg.sqlite
## AH113539 | org.Psilocybe_cubensis.eg.sqlite
## AH113540 | org.Stropharia_cubensis.eg.sqliteAnd if you really need access to all the metadata you can extract it as a DataFrame using mcols() like so:
meta <- mcols(ah)Also if you are a fan of GUI’s you can use the display method to look at your data in a browser and return selected rows back as a smaller AnnotationHub object like this:
sah <- display(ah)Calling this method will produce a web based interface like the one pictured here:
Once you have the AnnotationHub object pared down to a reasonable size, and are sure about which records you want to retrieve, then you only need to use the ‘[[’ operator to extract them. Using the ‘[[’ operator, you can extract by numeric index (1,2,3) or by AnnotationHub ID. If you choose to use the former, you simply extract the element that you are interested in. So for our chain example, you might just want to 1st one like this:
res <- grs[[1]]## loading from cachehead(res, n=3)## UCSC track 'cytoBand'
## UCSCData object with 3 ranges and 1 metadata column:
## seqnames ranges strand | name
## <Rle> <IRanges> <Rle> | <character>
## [1] chr1 1-2300000 * | p36.33
## [2] chr1 2300001-5400000 * | p36.32
## [3] chr1 5400001-7200000 * | p36.31
## -------
## seqinfo: 93 sequences (1 circular) from hg19 genome4.1 AnnotationHub exercises
Exercise 1: Use the AnnotationHub to extract UCSC data that is from Homo sapiens and also specifically from the hg19 genome. What happens to the hub object as you filter data at each step?
Exercise 2 Now that you have basically narrowed things down to the hg19 annotations from UCSC genome browser, lets get one of these annotations. Find the oreganno track and save it into a local variable.
[ Back to top ]
5 OrgDb objects
At this point you might be wondering: What is this OrgDb object about? OrgDb objects are one member of a family of annotation objects that all represent hidden data through a shared set of methods. So if you look closely at the dog object created below you can see it contains data for Canis familiaris (taxonomy ID = 9615). You can learn a little more about it by learning about the columns method.
dogquery <- query(orgs, c("Canis familiaris", "9615"))
dogquery## AnnotationHub with 1 record
## # snapshotDate(): 2023-04-24
## # names(): AH111569
## # $dataprovider: ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/
## # $species: Canis familiaris
## # $rdataclass: OrgDb
## # $rdatadateadded: 2023-04-06
## # $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
## # $tags: c("NCBI", "Gene", "Annotation")
## # retrieve record with 'object[["AH111569"]]'ah_id <- dogquery$ah_id
ah_id## [1] "AH111569"dog <- ah[[ah_id]]## downloading 1 resources## retrieving 1 resource## 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:0009060 IEA BP
## 7 804478 GO:0015990 IEA BP
## 8 804478 GO:0020037 IEA MF
## 9 804478 GO:0022904 IEA BP
## 10 804478 GO:0045277 IEA CC
## 11 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:0009060" "GO:0015990" "GO:0020037" "GO:0022904" "GO:0045277"
## [11] "GO:0046872"Now you know how to extract information from an OrgDb object, you might find it helpful to know that there is a whole family of other AnnotationDb derived objects that you can also use with these same five methods (keytypes(), columns(), keys(), select(), and mapIds()). For example there are ChipDb objects, InparanoidDb objects and TxDb objects which contain data about microarray probes, inparanoid homology partners or transcript range information respectively. And there are also more specialized objects like GODb or ReactomeDb objects which offer access to data from GO and reactome. In the next section, we will be looking at one of the more popular classes of these objects: the TxDb object.
5.1 OrgDb exercises
Exercise 3: Look at the help page for the different columns and keytypes values with: help(“SYMBOL”). Now use this information and what we just described to look up the entrez gene and chromosome for the gene symbol “MSX2”.
Exercise 4: In the previous exercise we had to use gene symbols as keys. But in the past this kind of behavior has sometimes been inadvisable because some gene symbols are used as the official symbol for more than one gene. To learn if this is still happening take advantage of the fact that entrez gene ids are uniquely assigned, and extract all of the gene symbols and their associated entrez gene ids from the org.Hs.eg.db package. Then check the symbols for redundancy.
[ Back to top ]
6 TxDb Objects
As mentioned before, TxDb objects can be accessed using the standard set of methods: keytypes(), columns(), keys(), select(), and mapIds(). But because these objects contain information about a transcriptome, they are often used to compare range based information to these important features of the genome[3,4]. As a result they also have specialized accessors for extracting out ranges that correspond to important transcriptome characteristics.
Lets start by loading a TxDb object from an annotation package based on the UCSC ensembl genes track for Drosophila. A common practice when loading these is to shorten the long name to ‘txdb’ (just as a convenience).
txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene
txdb## TxDb object:
## # Db type: TxDb
## # Supporting package: GenomicFeatures
## # Data source: UCSC
## # Genome: hg19
## # Organism: Homo sapiens
## # Taxonomy ID: 9606
## # UCSC Table: knownGene
## # Resource URL: http://genome.ucsc.edu/
## # Type of Gene ID: Entrez Gene ID
## # Full dataset: yes
## # miRBase build ID: GRCh37
## # transcript_nrow: 82960
## # exon_nrow: 289969
## # cds_nrow: 237533
## # Db created by: GenomicFeatures package from Bioconductor
## # Creation time: 2015-10-07 18:11:28 +0000 (Wed, 07 Oct 2015)
## # GenomicFeatures version at creation time: 1.21.30
## # RSQLite version at creation time: 1.0.0
## # DBSCHEMAVERSION: 1.1Just 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?
[ Back to top ]
7 Organism.dplyr src_organism Objects
So what happens if you have data from multiple different Annotation objects. For example, what if you had gene SYMBOLS (found in an OrgDb object) and you wanted to easily match those up with known gene transcript names from a UCSC based TxDb object? There is an ideal tool that can help with this kind of problem and it’s called an src_organism object from the Organism.dplyr package. src_organism objects and their related methods are able to query each of OrgDb and TxDb resources for you and then merge the results back together in way that lets you pretend that you only have one source for all your annotations.
library(Organism.dplyr)src_organism objects can be created for organisms that have both an OrgDb and a TxDb. To see organisms that can have src_organism objects made, use the function supportOrganisms():
supported <- supportedOrganisms()
print(supported, n=Inf)## # A tibble: 21 × 3
## organism OrgDb TxDb
## <chr> <chr> <chr>
## 1 Bos taurus org.Bt.eg.db TxDb.Btaurus.UCSC.bosTau8.refGene
## 2 Caenorhabditis elegans org.Ce.eg.db TxDb.Celegans.UCSC.ce11.refGene
## 3 Caenorhabditis elegans org.Ce.eg.db TxDb.Celegans.UCSC.ce6.ensGene
## 4 Canis familiaris org.Cf.eg.db TxDb.Cfamiliaris.UCSC.canFam3.refGene
## 5 Drosophila melanogaster org.Dm.eg.db TxDb.Dmelanogaster.UCSC.dm3.ensGene
## 6 Drosophila melanogaster org.Dm.eg.db TxDb.Dmelanogaster.UCSC.dm6.ensGene
## 7 Danio rerio org.Dr.eg.db TxDb.Drerio.UCSC.danRer10.refGene
## 8 Gallus gallus org.Gg.eg.db TxDb.Ggallus.UCSC.galGal4.refGene
## 9 Homo sapiens org.Hs.eg.db TxDb.Hsapiens.UCSC.hg18.knownGene
## 10 Homo sapiens org.Hs.eg.db TxDb.Hsapiens.UCSC.hg19.knownGene
## 11 Homo sapiens org.Hs.eg.db TxDb.Hsapiens.UCSC.hg38.knownGene
## 12 Mus musculus org.Mm.eg.db TxDb.Mmusculus.UCSC.mm10.ensGene
## 13 Mus musculus org.Mm.eg.db TxDb.Mmusculus.UCSC.mm10.knownGene
## 14 Mus musculus org.Mm.eg.db TxDb.Mmusculus.UCSC.mm9.knownGene
## 15 Macaca mulatta org.Mmu.eg.db TxDb.Mmulatta.UCSC.rheMac3.refGene
## 16 Macaca mulatta org.Mmu.eg.db TxDb.Mmulatta.UCSC.rheMac8.refGene
## 17 Pan troglodytes org.Pt.eg.db TxDb.Ptroglodytes.UCSC.panTro4.refGene
## 18 Rattus norvegicus org.Rn.eg.db TxDb.Rnorvegicus.UCSC.rn4.ensGene
## 19 Rattus norvegicus org.Rn.eg.db TxDb.Rnorvegicus.UCSC.rn5.refGene
## 20 Rattus norvegicus org.Rn.eg.db TxDb.Rnorvegicus.UCSC.rn6.refGene
## 21 Sus scrofa org.Ss.eg.db TxDb.Sscrofa.UCSC.susScr3.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/Rtmp7clVO4/file10c5985edb3986]
## 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/Rtmp7clVO4/file10c5985edb3986]
## 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 + | 3435 ENST00000581525.1
## [2] chr1 44776490-44776593 + | 3436 ENST00000364043.1
## [3] chr1 44777843-44777912 + | 3439 ENST00000365161.1
## [4] chr1 44778390-44778456 + | 3441 ENST00000384690.1
## [5] chr1 44778390-44778458 + | 3442 ENST00000625943.1
## ... ... ... ... . ... ...
## [62] chr20 44623752-44651678 - | 234236 ENST00000695997.1
## [63] chr20 44623972-44651718 - | 234237 ENST00000696009.1
## [64] chr20 44626323-44651661 - | 234238 ENST00000545776.5
## [65] chr20 44627547-44651720 - | 234239 ENST00000696010.1
## [66] chr20 44636071-44652233 - | 234240 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 + 3435 ENST00000581525.1 SNORD55
## 2 chr1 44776490 44776593 + 3436 ENST00000364043.1 SNORD46
## 3 chr1 44777843 44777912 + 3439 ENST00000365161.1 SNORD38A
## 4 chr1 44778390 44778456 + 3441 ENST00000384690.1 SNORD38B
## 5 chr1 44778390 44778458 + 3442 ENST00000625943.1 SNORD38B
## 6 chr20 44584896 44651702 - 234180 ENST00000696034.1 ADA
## 7 chr20 44618605 44651745 - 234181 ENST00000537820.2 ADA
## 8 chr20 44618618 44651699 - 234182 ENST00000696003.1 ADA
## 9 chr20 44618625 44651699 - 234183 ENST00000696004.1 ADA
## 10 chr20 44619521 44651678 - 234184 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’.
[ Back to top ]
8 BSgenome Objects
Another important annotation resource type is a BSgenome package[10]. There are many BSgenome packages in the repository for you to choose from. And you can learn which organisms are already supported by using the available.genomes() function.
head(available.genomes())## [1] "BSgenome.Alyrata.JGI.v1"
## [2] "BSgenome.Amellifera.BeeBase.assembly4"
## [3] "BSgenome.Amellifera.NCBI.AmelHAv3.1"
## [4] "BSgenome.Amellifera.UCSC.apiMel2"
## [5] "BSgenome.Amellifera.UCSC.apiMel2.masked"
## [6] "BSgenome.Aofficinalis.NCBI.V1"Unlike the other resources that we have discussed here, these packages are meant to contain sequence data for a specific genome build of an organism. You can load one of these packages in the usual way. And each of them normally has an alias for the primary object that is shorter than the full package name (as a convenience):
ls(2)## character(0)Hsapiens## | BSgenome object for Human
## | - organism: Homo sapiens
## | - provider: UCSC
## | - genome: hg19
## | - release date: June 2013
## | - 298 sequence(s):
## | chr1 chr2 chr3
## | chr4 chr5 chr6
## | chr7 chr8 chr9
## | chr10 chr11 chr12
## | chr13 chr14 chr15
## | ... ... ...
## | chr19_gl949749_alt chr19_gl949750_alt chr19_gl949751_alt
## | chr19_gl949752_alt chr19_gl949753_alt chr20_gl383577_alt
## | chr21_gl383578_alt chr21_gl383579_alt chr21_gl383580_alt
## | chr21_gl383581_alt chr22_gl383582_alt chr22_gl383583_alt
## | chr22_kb663609_alt
## |
## | Tips: call 'seqnames()' on the object to get all the sequence names, call
## | 'seqinfo()' to get the full sequence info, use the '$' or '[[' operator to
## | access a given sequence, see '?BSgenome' for more information.The getSeq method is a useful way of extracting data from these packages. This method takes several arguments but the important ones are the 1st two. The 1st argument specifies the BSgenome object to use and the second argument (names) specifies what data you want back out. So for example, if you call it and give a character vector that names the seqnames for the object then you will get the sequences from those chromosomes as a DNAStringSet object.
seqNms <- seqnames(Hsapiens)
head(seqNms)## [1] "chr1" "chr2" "chr3" "chr4" "chr5" "chr6"getSeq(Hsapiens, seqNms[1:2])## DNAStringSet object of length 2:
## width seq names
## [1] 249250621 NNNNNNNNNNNNNNNNNNNNN...NNNNNNNNNNNNNNNNNNNNN chr1
## [2] 243199373 NNNNNNNNNNNNNNNNNNNNN...NNNNNNNNNNNNNNNNNNNNN 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.
[ Back to top ]
9 biomaRt
Another great annotation resource is the biomaRt package[5,6,7]. The biomaRt package exposes a huge family of different online annotation resources called marts. Each mart is another of a set of online web resources that are following a convention that allows them to work with this package. Historically these marts were maintained by various projects around the world, however the majority are now maintained as part of Ensembl and we’ll focus on that resource here. If you wish to access another BioMart instance see the biomaRt vignette Using a BioMart other than Ensembl.
The first step in using biomaRt is always to load the package and then decide which “mart” you want to use. Once you have made your decision, you will then use the useEnsembl() method to create a mart object in your R session. Here we are looking at the marts available and then choosing to use one of the most popular marts: the Ensembl “genes” mart.
listEnsembl()## biomart version
## 1 genes Ensembl Genes 109
## 2 mouse_strains Mouse strains 109
## 3 snps Ensembl Variation 109
## 4 regulation Ensembl Regulation 109ensembl <- 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?
[ Back to top ]
10 Creating annotation objects
By now you are aware that Bioconductor has a lot of annotation resources. But it is still completely impossible to have every annotation resource pre-packaged for every conceivable use. Because of this, almost all annotation objects have special functions that can be called to create those objects (or the packages that load them) from generalized data resources or specific file types. Below is a table with a few of the more popular options.
| If you want this | And you have this | Then you could call this to help |
|---|---|---|
| TxDb | tracks from UCSC | GenomicFeatures::makeTxDbPackageFromUCSC |
| TxDb | data from biomaRt | GenomicFeatures::makeTxDbPackageFromBiomaRt |
| TxDb | gff or gtf file | GenomicFeatures::makeTxDbFromGFF |
| OrgDb | custom data.frames | AnnotationForge::makeOrgPackage |
| OrgDb | valid Taxonomy ID | AnnotationForge::makeOrgPackageFromNCBI |
| ChipDb | org package & data.frame | AnnotationForge::makeChipPackage |
| BSgenome | fasta or twobit sequence files | BSgenome::forgeBSgenomeDataPkg |
In most cases the output for resource creation functions will be an annotation package that you can install.
And there is unfortunately not enough space to demonstrate how to call each of these functions here. But to do so is actually pretty straightforward and most such functions will be well documented with their associated manual pages and vignettes[3,4,10,12]. As usual, you can see the help page for any function right inside of R.
help("makeTxDbPackageFromUCSC")If you plan to make use of these kinds of functions then you should expect to consult the associated documentation first. These kinds of functions tend to have a lot of arguments and most of them also require that their input data meet some fairly specific criteria. Finally, you should know that even after you have succeeded at creating an annotation package, you will also have to make use of the install.packages() function (with the repos argument=NULL) to install whatever package source directory has just been created.
11 Important considerations
The bioconductor project represents a very large and active codebase from an active and engaged community. Because of this, you should expect that the software described in this walkthrough will change over time and often in dramatic ways. As an example, the getSeq function that is described in this chapter is expected to a big overhaul in the coming months. When this happens the older function will be deprecated for a full release cycle (6 months) and then labeled as defunct for another release cycle before it is removed. This cycle is in place so that active users can be warned about what is happening and where they should look for the appropriate replacement functionality. But obviously, this system cannot warn end users if they have not been vigilant about updating their software to the latest version. So please take the time to always update your software to the latest version.
To stay abreast of new developments users are encouraged to explore the bioconductor website which contains many current walkthroughs and vignettes. Also visit the support site where you can ask questions and engage in discussions.
12 sessionInfo()
Package versions used in this tutorial:
sessionInfo()## R version 4.3.1 (2023-06-16)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.2 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.17-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_1.24.1
## [2] TxDb.Athaliana.BioMart.plantsmart22_3.0.1
## [3] biomaRt_2.56.1
## [4] BSgenome.Hsapiens.UCSC.hg19_1.4.3
## [5] BSgenome_1.68.0
## [6] rtracklayer_1.60.0
## [7] Homo.sapiens_1.3.1
## [8] GO.db_3.17.0
## [9] OrganismDbi_1.42.0
## [10] org.Mm.eg.db_3.17.0
## [11] org.Hs.eg.db_3.17.0
## [12] TxDb.Mmusculus.UCSC.mm10.ensGene_3.4.0
## [13] TxDb.Hsapiens.UCSC.hg38.knownGene_3.17.0
## [14] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
## [15] GenomicFeatures_1.52.1
## [16] AnnotationDbi_1.62.1
## [17] Organism.dplyr_1.28.0
## [18] AnnotationFilter_1.24.0
## [19] dplyr_1.1.2
## [20] AnnotationHub_3.8.0
## [21] BiocFileCache_2.8.0
## [22] dbplyr_2.3.2
## [23] VariantAnnotation_1.46.0
## [24] Rsamtools_2.16.0
## [25] Biostrings_2.68.1
## [26] XVector_0.40.0
## [27] SummarizedExperiment_1.30.2
## [28] Biobase_2.60.0
## [29] GenomicRanges_1.52.0
## [30] GenomeInfoDb_1.36.1
## [31] IRanges_2.34.1
## [32] S4Vectors_0.38.1
## [33] MatrixGenerics_1.12.2
## [34] matrixStats_1.0.0
## [35] BiocGenerics_0.46.0
## [36] BiocStyle_2.28.0
##
## loaded via a namespace (and not attached):
## [1] DBI_1.1.3 bitops_1.0-7
## [3] RBGL_1.76.0 rlang_1.1.1
## [5] magrittr_2.0.3 compiler_4.3.1
## [7] RSQLite_2.3.1 png_0.1-8
## [9] vctrs_0.6.3 stringr_1.5.0
## [11] pkgconfig_2.0.3 crayon_1.5.2
## [13] fastmap_1.1.1 ellipsis_0.3.2
## [15] utf8_1.2.3 promises_1.2.0.1
## [17] rmarkdown_2.22 graph_1.78.0
## [19] purrr_1.0.1 bit_4.0.5
## [21] xfun_0.39 zlibbioc_1.46.0
## [23] cachem_1.0.8 jsonlite_1.8.5
## [25] progress_1.2.2 blob_1.2.4
## [27] later_1.3.1 DelayedArray_0.26.4
## [29] BiocParallel_1.34.2 interactiveDisplayBase_1.38.0
## [31] parallel_4.3.1 prettyunits_1.1.1
## [33] R6_2.5.1 bslib_0.5.0
## [35] stringi_1.7.12 jquerylib_0.1.4
## [37] Rcpp_1.0.10 bookdown_0.34
## [39] knitr_1.43 httpuv_1.6.11
## [41] Matrix_1.5-4.1 tidyselect_1.2.0
## [43] yaml_2.3.7 codetools_0.2-19
## [45] curl_5.0.1 lattice_0.21-8
## [47] tibble_3.2.1 withr_2.5.0
## [49] shiny_1.7.4 KEGGREST_1.40.0
## [51] evaluate_0.21 xml2_1.3.4
## [53] pillar_1.9.0 BiocManager_1.30.21
## [55] filelock_1.0.2 generics_0.1.3
## [57] RCurl_1.98-1.12 BiocVersion_3.17.1
## [59] hms_1.1.3 xtable_1.8-4
## [61] glue_1.6.2 lazyeval_0.2.2
## [63] tools_4.3.1 BiocIO_1.10.0
## [65] GenomicAlignments_1.36.0 XML_3.99-0.14
## [67] grid_4.3.1 GenomeInfoDbData_1.2.10
## [69] restfulr_0.0.15 cli_3.6.1
## [71] rappdirs_0.3.3 fansi_1.0.4
## [73] S4Arrays_1.0.4 sass_0.4.6
## [75] digest_0.6.32 rjson_0.2.21
## [77] memoise_2.0.1 htmltools_0.5.5
## [79] lifecycle_1.0.3 httr_1.4.6
## [81] mime_0.12 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(): 2023-04-24
## # $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(): 2023-04-24
## # 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] 142352length(unique(egr$ENTREZID))## [1] 142352## VS:
length(egr$SYMBOL)## [1] 142352length(unique(egr$SYMBOL))## [1] 142248## So lets trap these symbols that are redundant and look more closely...
redund <- egr$SYMBOL
badSymbols <- redund[duplicated(redund)]
select(org.Hs.eg.db, badSymbols, "ENTREZID", "SYMBOL")## 'select()' returned many:many mapping between keys and columns## SYMBOL ENTREZID
## 1 HBD 3045
## 2 HBD 100187828
## 3 RNR1 4549
## 4 RNR1 6052
## 5 RNR2 4550
## 6 RNR2 6053
## 7 TEC 7006
## 8 TEC 100124696
## 9 MEMO1 7795
## 10 MEMO1 51072
## 11 MMD2 221938
## 12 MMD2 100505381
## 13 DEL1P36 100240737
## 14 DEL1P36 123670537
## 15 DEL11P13 100528024
## 16 DEL11P13 107648861
## 17 TRNAV-CAC 107985614
## 18 TRNAV-CAC 107985615
## 19 TRNAE-UUC 107987368
## 20 TRNAE-UUC 124905580
## 21 TRNAE-UUC 124905583
## 22 TRNAE-UUC 124905584
## 23 TRNAE-UUC 124905586
## 24 TRNAE-UUC 124905908
## 25 TRNAE-UUC 107987368
## 26 TRNAE-UUC 124905580
## 27 TRNAE-UUC 124905583
## 28 TRNAE-UUC 124905584
## 29 TRNAE-UUC 124905586
## 30 TRNAE-UUC 124905908
## 31 TRNAE-UUC 107987368
## 32 TRNAE-UUC 124905580
## 33 TRNAE-UUC 124905583
## 34 TRNAE-UUC 124905584
## 35 TRNAE-UUC 124905586
## 36 TRNAE-UUC 124905908
## 37 TRNAE-UUC 107987368
## 38 TRNAE-UUC 124905580
## 39 TRNAE-UUC 124905583
## 40 TRNAE-UUC 124905584
## 41 TRNAE-UUC 124905586
## 42 TRNAE-UUC 124905908
## 43 TRNAE-UUC 107987368
## 44 TRNAE-UUC 124905580
## 45 TRNAE-UUC 124905583
## 46 TRNAE-UUC 124905584
## 47 TRNAE-UUC 124905586
## 48 TRNAE-UUC 124905908
## 49 TRNAA-AGC 124901561
## 50 TRNAA-AGC 124901562
## 51 TRNAA-AGC 124901563
## 52 TRNAA-AGC 124901564
## 53 TRNAA-AGC 124901565
## 54 TRNAA-AGC 124906586
## 55 TRNAA-AGC 124901561
## 56 TRNAA-AGC 124901562
## 57 TRNAA-AGC 124901563
## 58 TRNAA-AGC 124901564
## 59 TRNAA-AGC 124901565
## 60 TRNAA-AGC 124906586
## 61 TRNAA-AGC 124901561
## 62 TRNAA-AGC 124901562
## 63 TRNAA-AGC 124901563
## 64 TRNAA-AGC 124901564
## 65 TRNAA-AGC 124901565
## 66 TRNAA-AGC 124906586
## 67 TRNAA-AGC 124901561
## 68 TRNAA-AGC 124901562
## 69 TRNAA-AGC 124901563
## 70 TRNAA-AGC 124901564
## 71 TRNAA-AGC 124901565
## 72 TRNAA-AGC 124906586
## 73 TRNAA-AGC 124901561
## 74 TRNAA-AGC 124901562
## 75 TRNAA-AGC 124901563
## 76 TRNAA-AGC 124901564
## 77 TRNAA-AGC 124901565
## 78 TRNAA-AGC 124906586
## 79 TRNAG-CCC 124905578
## 80 TRNAG-CCC 124905581
## 81 TRNAG-CCC 124905588
## 82 TRNAG-CCC 124905578
## 83 TRNAG-CCC 124905581
## 84 TRNAG-CCC 124905588
## 85 TRNAN-GUU 124905579
## 86 TRNAN-GUU 124905582
## 87 TRNAN-GUU 124905585
## 88 TRNAN-GUU 124905587
## 89 TRNAN-GUU 124905579
## 90 TRNAN-GUU 124905582
## 91 TRNAN-GUU 124905585
## 92 TRNAN-GUU 124905587
## 93 TRNAN-GUU 124905579
## 94 TRNAN-GUU 124905582
## 95 TRNAN-GUU 124905585
## 96 TRNAN-GUU 124905587
## 97 TRNAG-GCC 124905847
## 98 TRNAG-GCC 124905849
## 99 TRNAG-GCC 124905851
## 100 TRNAG-GCC 124905853
## 101 TRNAG-GCC 124905907
## 102 TRNAG-GCC 124905910
## 103 TRNAG-GCC 124905912
## 104 TRNAG-GCC 124905914
## 105 TRNAG-GCC 124905916
## 106 TRNAG-GCC 124905918
## 107 TRNAG-GCC 124905921
## 108 TRNAG-GCC 124905923
## 109 TRNAG-GCC 124905925
## 110 TRNAG-GCC 124905927
## 111 TRNAG-GCC 124905929
## 112 TRNAG-GCC 124905931
## 113 TRNAG-GCC 124905933
## 114 TRNAG-GCC 124905847
## 115 TRNAG-GCC 124905849
## 116 TRNAG-GCC 124905851
## 117 TRNAG-GCC 124905853
## 118 TRNAG-GCC 124905907
## 119 TRNAG-GCC 124905910
## 120 TRNAG-GCC 124905912
## 121 TRNAG-GCC 124905914
## 122 TRNAG-GCC 124905916
## 123 TRNAG-GCC 124905918
## 124 TRNAG-GCC 124905921
## 125 TRNAG-GCC 124905923
## 126 TRNAG-GCC 124905925
## 127 TRNAG-GCC 124905927
## 128 TRNAG-GCC 124905929
## 129 TRNAG-GCC 124905931
## 130 TRNAG-GCC 124905933
## 131 TRNAG-GCC 124905847
## 132 TRNAG-GCC 124905849
## 133 TRNAG-GCC 124905851
## 134 TRNAG-GCC 124905853
## 135 TRNAG-GCC 124905907
## 136 TRNAG-GCC 124905910
## 137 TRNAG-GCC 124905912
## 138 TRNAG-GCC 124905914
## 139 TRNAG-GCC 124905916
## 140 TRNAG-GCC 124905918
## 141 TRNAG-GCC 124905921
## 142 TRNAG-GCC 124905923
## 143 TRNAG-GCC 124905925
## 144 TRNAG-GCC 124905927
## 145 TRNAG-GCC 124905929
## 146 TRNAG-GCC 124905931
## 147 TRNAG-GCC 124905933
## 148 TRNAG-GCC 124905847
## 149 TRNAG-GCC 124905849
## 150 TRNAG-GCC 124905851
## 151 TRNAG-GCC 124905853
## 152 TRNAG-GCC 124905907
## 153 TRNAG-GCC 124905910
## 154 TRNAG-GCC 124905912
## 155 TRNAG-GCC 124905914
## 156 TRNAG-GCC 124905916
## 157 TRNAG-GCC 124905918
## 158 TRNAG-GCC 124905921
## 159 TRNAG-GCC 124905923
## 160 TRNAG-GCC 124905925
## 161 TRNAG-GCC 124905927
## 162 TRNAG-GCC 124905929
## 163 TRNAG-GCC 124905931
## 164 TRNAG-GCC 124905933
## 165 TRNAG-GCC 124905847
## 166 TRNAG-GCC 124905849
## 167 TRNAG-GCC 124905851
## 168 TRNAG-GCC 124905853
## 169 TRNAG-GCC 124905907
## 170 TRNAG-GCC 124905910
## 171 TRNAG-GCC 124905912
## 172 TRNAG-GCC 124905914
## 173 TRNAG-GCC 124905916
## 174 TRNAG-GCC 124905918
## 175 TRNAG-GCC 124905921
## 176 TRNAG-GCC 124905923
## 177 TRNAG-GCC 124905925
## 178 TRNAG-GCC 124905927
## 179 TRNAG-GCC 124905929
## 180 TRNAG-GCC 124905931
## 181 TRNAG-GCC 124905933
## 182 TRNAG-GCC 124905847
## 183 TRNAG-GCC 124905849
## 184 TRNAG-GCC 124905851
## 185 TRNAG-GCC 124905853
## 186 TRNAG-GCC 124905907
## 187 TRNAG-GCC 124905910
## 188 TRNAG-GCC 124905912
## 189 TRNAG-GCC 124905914
## 190 TRNAG-GCC 124905916
## 191 TRNAG-GCC 124905918
## 192 TRNAG-GCC 124905921
## 193 TRNAG-GCC 124905923
## 194 TRNAG-GCC 124905925
## 195 TRNAG-GCC 124905927
## 196 TRNAG-GCC 124905929
## 197 TRNAG-GCC 124905931
## 198 TRNAG-GCC 124905933
## 199 TRNAG-GCC 124905847
## 200 TRNAG-GCC 124905849
## 201 TRNAG-GCC 124905851
## 202 TRNAG-GCC 124905853
## 203 TRNAG-GCC 124905907
## 204 TRNAG-GCC 124905910
## 205 TRNAG-GCC 124905912
## 206 TRNAG-GCC 124905914
## 207 TRNAG-GCC 124905916
## 208 TRNAG-GCC 124905918
## 209 TRNAG-GCC 124905921
## 210 TRNAG-GCC 124905923
## 211 TRNAG-GCC 124905925
## 212 TRNAG-GCC 124905927
## 213 TRNAG-GCC 124905929
## 214 TRNAG-GCC 124905931
## 215 TRNAG-GCC 124905933
## 216 TRNAG-GCC 124905847
## 217 TRNAG-GCC 124905849
## 218 TRNAG-GCC 124905851
## 219 TRNAG-GCC 124905853
## 220 TRNAG-GCC 124905907
## 221 TRNAG-GCC 124905910
## 222 TRNAG-GCC 124905912
## 223 TRNAG-GCC 124905914
## 224 TRNAG-GCC 124905916
## 225 TRNAG-GCC 124905918
## 226 TRNAG-GCC 124905921
## 227 TRNAG-GCC 124905923
## 228 TRNAG-GCC 124905925
## 229 TRNAG-GCC 124905927
## 230 TRNAG-GCC 124905929
## 231 TRNAG-GCC 124905931
## 232 TRNAG-GCC 124905933
## 233 TRNAG-GCC 124905847
## 234 TRNAG-GCC 124905849
## 235 TRNAG-GCC 124905851
## 236 TRNAG-GCC 124905853
## 237 TRNAG-GCC 124905907
## 238 TRNAG-GCC 124905910
## 239 TRNAG-GCC 124905912
## 240 TRNAG-GCC 124905914
## 241 TRNAG-GCC 124905916
## 242 TRNAG-GCC 124905918
## 243 TRNAG-GCC 124905921
## 244 TRNAG-GCC 124905923
## 245 TRNAG-GCC 124905925
## 246 TRNAG-GCC 124905927
## 247 TRNAG-GCC 124905929
## 248 TRNAG-GCC 124905931
## 249 TRNAG-GCC 124905933
## 250 TRNAG-GCC 124905847
## 251 TRNAG-GCC 124905849
## 252 TRNAG-GCC 124905851
## 253 TRNAG-GCC 124905853
## 254 TRNAG-GCC 124905907
## 255 TRNAG-GCC 124905910
## 256 TRNAG-GCC 124905912
## 257 TRNAG-GCC 124905914
## 258 TRNAG-GCC 124905916
## 259 TRNAG-GCC 124905918
## 260 TRNAG-GCC 124905921
## 261 TRNAG-GCC 124905923
## 262 TRNAG-GCC 124905925
## 263 TRNAG-GCC 124905927
## 264 TRNAG-GCC 124905929
## 265 TRNAG-GCC 124905931
## 266 TRNAG-GCC 124905933
## 267 TRNAG-GCC 124905847
## 268 TRNAG-GCC 124905849
## 269 TRNAG-GCC 124905851
## 270 TRNAG-GCC 124905853
## 271 TRNAG-GCC 124905907
## 272 TRNAG-GCC 124905910
## 273 TRNAG-GCC 124905912
## 274 TRNAG-GCC 124905914
## 275 TRNAG-GCC 124905916
## 276 TRNAG-GCC 124905918
## 277 TRNAG-GCC 124905921
## 278 TRNAG-GCC 124905923
## 279 TRNAG-GCC 124905925
## 280 TRNAG-GCC 124905927
## 281 TRNAG-GCC 124905929
## 282 TRNAG-GCC 124905931
## 283 TRNAG-GCC 124905933
## 284 TRNAG-GCC 124905847
## 285 TRNAG-GCC 124905849
## 286 TRNAG-GCC 124905851
## 287 TRNAG-GCC 124905853
## 288 TRNAG-GCC 124905907
## 289 TRNAG-GCC 124905910
## 290 TRNAG-GCC 124905912
## 291 TRNAG-GCC 124905914
## 292 TRNAG-GCC 124905916
## 293 TRNAG-GCC 124905918
## 294 TRNAG-GCC 124905921
## 295 TRNAG-GCC 124905923
## 296 TRNAG-GCC 124905925
## 297 TRNAG-GCC 124905927
## 298 TRNAG-GCC 124905929
## 299 TRNAG-GCC 124905931
## 300 TRNAG-GCC 124905933
## 301 TRNAG-GCC 124905847
## 302 TRNAG-GCC 124905849
## 303 TRNAG-GCC 124905851
## 304 TRNAG-GCC 124905853
## 305 TRNAG-GCC 124905907
## 306 TRNAG-GCC 124905910
## 307 TRNAG-GCC 124905912
## 308 TRNAG-GCC 124905914
## 309 TRNAG-GCC 124905916
## 310 TRNAG-GCC 124905918
## 311 TRNAG-GCC 124905921
## 312 TRNAG-GCC 124905923
## 313 TRNAG-GCC 124905925
## 314 TRNAG-GCC 124905927
## 315 TRNAG-GCC 124905929
## 316 TRNAG-GCC 124905931
## 317 TRNAG-GCC 124905933
## 318 TRNAG-GCC 124905847
## 319 TRNAG-GCC 124905849
## 320 TRNAG-GCC 124905851
## 321 TRNAG-GCC 124905853
## 322 TRNAG-GCC 124905907
## 323 TRNAG-GCC 124905910
## 324 TRNAG-GCC 124905912
## 325 TRNAG-GCC 124905914
## 326 TRNAG-GCC 124905916
## 327 TRNAG-GCC 124905918
## 328 TRNAG-GCC 124905921
## 329 TRNAG-GCC 124905923
## 330 TRNAG-GCC 124905925
## 331 TRNAG-GCC 124905927
## 332 TRNAG-GCC 124905929
## 333 TRNAG-GCC 124905931
## 334 TRNAG-GCC 124905933
## 335 TRNAG-GCC 124905847
## 336 TRNAG-GCC 124905849
## 337 TRNAG-GCC 124905851
## 338 TRNAG-GCC 124905853
## 339 TRNAG-GCC 124905907
## 340 TRNAG-GCC 124905910
## 341 TRNAG-GCC 124905912
## 342 TRNAG-GCC 124905914
## 343 TRNAG-GCC 124905916
## 344 TRNAG-GCC 124905918
## 345 TRNAG-GCC 124905921
## 346 TRNAG-GCC 124905923
## 347 TRNAG-GCC 124905925
## 348 TRNAG-GCC 124905927
## 349 TRNAG-GCC 124905929
## 350 TRNAG-GCC 124905931
## 351 TRNAG-GCC 124905933
## 352 TRNAG-GCC 124905847
## 353 TRNAG-GCC 124905849
## 354 TRNAG-GCC 124905851
## 355 TRNAG-GCC 124905853
## 356 TRNAG-GCC 124905907
## 357 TRNAG-GCC 124905910
## 358 TRNAG-GCC 124905912
## 359 TRNAG-GCC 124905914
## 360 TRNAG-GCC 124905916
## 361 TRNAG-GCC 124905918
## 362 TRNAG-GCC 124905921
## 363 TRNAG-GCC 124905923
## 364 TRNAG-GCC 124905925
## 365 TRNAG-GCC 124905927
## 366 TRNAG-GCC 124905929
## 367 TRNAG-GCC 124905931
## 368 TRNAG-GCC 124905933
## 369 TRNAL-CAG 124905848
## 370 TRNAL-CAG 124905850
## 371 TRNAL-CAG 124905852
## 372 TRNAL-CAG 124905906
## 373 TRNAL-CAG 124905909
## 374 TRNAL-CAG 124905911
## 375 TRNAL-CAG 124905913
## 376 TRNAL-CAG 124905915
## 377 TRNAL-CAG 124905917
## 378 TRNAL-CAG 124905920
## 379 TRNAL-CAG 124905922
## 380 TRNAL-CAG 124905924
## 381 TRNAL-CAG 124905926
## 382 TRNAL-CAG 124905928
## 383 TRNAL-CAG 124905930
## 384 TRNAL-CAG 124905932
## 385 TRNAL-CAG 124905934
## 386 TRNAL-CAG 124905848
## 387 TRNAL-CAG 124905850
## 388 TRNAL-CAG 124905852
## 389 TRNAL-CAG 124905906
## 390 TRNAL-CAG 124905909
## 391 TRNAL-CAG 124905911
## 392 TRNAL-CAG 124905913
## 393 TRNAL-CAG 124905915
## 394 TRNAL-CAG 124905917
## 395 TRNAL-CAG 124905920
## 396 TRNAL-CAG 124905922
## 397 TRNAL-CAG 124905924
## 398 TRNAL-CAG 124905926
## 399 TRNAL-CAG 124905928
## 400 TRNAL-CAG 124905930
## 401 TRNAL-CAG 124905932
## 402 TRNAL-CAG 124905934
## 403 TRNAL-CAG 124905848
## 404 TRNAL-CAG 124905850
## 405 TRNAL-CAG 124905852
## 406 TRNAL-CAG 124905906
## 407 TRNAL-CAG 124905909
## 408 TRNAL-CAG 124905911
## 409 TRNAL-CAG 124905913
## 410 TRNAL-CAG 124905915
## 411 TRNAL-CAG 124905917
## 412 TRNAL-CAG 124905920
## 413 TRNAL-CAG 124905922
## 414 TRNAL-CAG 124905924
## 415 TRNAL-CAG 124905926
## 416 TRNAL-CAG 124905928
## 417 TRNAL-CAG 124905930
## 418 TRNAL-CAG 124905932
## 419 TRNAL-CAG 124905934
## 420 TRNAL-CAG 124905848
## 421 TRNAL-CAG 124905850
## 422 TRNAL-CAG 124905852
## 423 TRNAL-CAG 124905906
## 424 TRNAL-CAG 124905909
## 425 TRNAL-CAG 124905911
## 426 TRNAL-CAG 124905913
## 427 TRNAL-CAG 124905915
## 428 TRNAL-CAG 124905917
## 429 TRNAL-CAG 124905920
## 430 TRNAL-CAG 124905922
## 431 TRNAL-CAG 124905924
## 432 TRNAL-CAG 124905926
## 433 TRNAL-CAG 124905928
## 434 TRNAL-CAG 124905930
## 435 TRNAL-CAG 124905932
## 436 TRNAL-CAG 124905934
## 437 TRNAL-CAG 124905848
## 438 TRNAL-CAG 124905850
## 439 TRNAL-CAG 124905852
## 440 TRNAL-CAG 124905906
## 441 TRNAL-CAG 124905909
## 442 TRNAL-CAG 124905911
## 443 TRNAL-CAG 124905913
## 444 TRNAL-CAG 124905915
## 445 TRNAL-CAG 124905917
## 446 TRNAL-CAG 124905920
## 447 TRNAL-CAG 124905922
## 448 TRNAL-CAG 124905924
## 449 TRNAL-CAG 124905926
## 450 TRNAL-CAG 124905928
## 451 TRNAL-CAG 124905930
## 452 TRNAL-CAG 124905932
## 453 TRNAL-CAG 124905934
## 454 TRNAL-CAG 124905848
## 455 TRNAL-CAG 124905850
## 456 TRNAL-CAG 124905852
## 457 TRNAL-CAG 124905906
## 458 TRNAL-CAG 124905909
## 459 TRNAL-CAG 124905911
## 460 TRNAL-CAG 124905913
## 461 TRNAL-CAG 124905915
## 462 TRNAL-CAG 124905917
## 463 TRNAL-CAG 124905920
## 464 TRNAL-CAG 124905922
## 465 TRNAL-CAG 124905924
## 466 TRNAL-CAG 124905926
## 467 TRNAL-CAG 124905928
## 468 TRNAL-CAG 124905930
## 469 TRNAL-CAG 124905932
## 470 TRNAL-CAG 124905934
## 471 TRNAL-CAG 124905848
## 472 TRNAL-CAG 124905850
## 473 TRNAL-CAG 124905852
## 474 TRNAL-CAG 124905906
## 475 TRNAL-CAG 124905909
## 476 TRNAL-CAG 124905911
## 477 TRNAL-CAG 124905913
## 478 TRNAL-CAG 124905915
## 479 TRNAL-CAG 124905917
## 480 TRNAL-CAG 124905920
## 481 TRNAL-CAG 124905922
## 482 TRNAL-CAG 124905924
## 483 TRNAL-CAG 124905926
## 484 TRNAL-CAG 124905928
## 485 TRNAL-CAG 124905930
## 486 TRNAL-CAG 124905932
## 487 TRNAL-CAG 124905934
## 488 TRNAL-CAG 124905848
## 489 TRNAL-CAG 124905850
## 490 TRNAL-CAG 124905852
## 491 TRNAL-CAG 124905906
## 492 TRNAL-CAG 124905909
## 493 TRNAL-CAG 124905911
## 494 TRNAL-CAG 124905913
## 495 TRNAL-CAG 124905915
## 496 TRNAL-CAG 124905917
## 497 TRNAL-CAG 124905920
## 498 TRNAL-CAG 124905922
## 499 TRNAL-CAG 124905924
## 500 TRNAL-CAG 124905926
## 501 TRNAL-CAG 124905928
## 502 TRNAL-CAG 124905930
## 503 TRNAL-CAG 124905932
## 504 TRNAL-CAG 124905934
## 505 TRNAL-CAG 124905848
## 506 TRNAL-CAG 124905850
## 507 TRNAL-CAG 124905852
## 508 TRNAL-CAG 124905906
## 509 TRNAL-CAG 124905909
## 510 TRNAL-CAG 124905911
## 511 TRNAL-CAG 124905913
## 512 TRNAL-CAG 124905915
## 513 TRNAL-CAG 124905917
## 514 TRNAL-CAG 124905920
## 515 TRNAL-CAG 124905922
## 516 TRNAL-CAG 124905924
## 517 TRNAL-CAG 124905926
## 518 TRNAL-CAG 124905928
## 519 TRNAL-CAG 124905930
## 520 TRNAL-CAG 124905932
## 521 TRNAL-CAG 124905934
## 522 TRNAL-CAG 124905848
## 523 TRNAL-CAG 124905850
## 524 TRNAL-CAG 124905852
## 525 TRNAL-CAG 124905906
## 526 TRNAL-CAG 124905909
## 527 TRNAL-CAG 124905911
## 528 TRNAL-CAG 124905913
## 529 TRNAL-CAG 124905915
## 530 TRNAL-CAG 124905917
## 531 TRNAL-CAG 124905920
## 532 TRNAL-CAG 124905922
## 533 TRNAL-CAG 124905924
## 534 TRNAL-CAG 124905926
## 535 TRNAL-CAG 124905928
## 536 TRNAL-CAG 124905930
## 537 TRNAL-CAG 124905932
## 538 TRNAL-CAG 124905934
## 539 TRNAL-CAG 124905848
## 540 TRNAL-CAG 124905850
## 541 TRNAL-CAG 124905852
## 542 TRNAL-CAG 124905906
## 543 TRNAL-CAG 124905909
## 544 TRNAL-CAG 124905911
## 545 TRNAL-CAG 124905913
## 546 TRNAL-CAG 124905915
## 547 TRNAL-CAG 124905917
## 548 TRNAL-CAG 124905920
## 549 TRNAL-CAG 124905922
## 550 TRNAL-CAG 124905924
## 551 TRNAL-CAG 124905926
## 552 TRNAL-CAG 124905928
## 553 TRNAL-CAG 124905930
## 554 TRNAL-CAG 124905932
## 555 TRNAL-CAG 124905934
## 556 TRNAL-CAG 124905848
## 557 TRNAL-CAG 124905850
## 558 TRNAL-CAG 124905852
## 559 TRNAL-CAG 124905906
## 560 TRNAL-CAG 124905909
## 561 TRNAL-CAG 124905911
## 562 TRNAL-CAG 124905913
## 563 TRNAL-CAG 124905915
## 564 TRNAL-CAG 124905917
## 565 TRNAL-CAG 124905920
## 566 TRNAL-CAG 124905922
## 567 TRNAL-CAG 124905924
## 568 TRNAL-CAG 124905926
## 569 TRNAL-CAG 124905928
## 570 TRNAL-CAG 124905930
## 571 TRNAL-CAG 124905932
## 572 TRNAL-CAG 124905934
## 573 TRNAL-CAG 124905848
## 574 TRNAL-CAG 124905850
## 575 TRNAL-CAG 124905852
## 576 TRNAL-CAG 124905906
## 577 TRNAL-CAG 124905909
## 578 TRNAL-CAG 124905911
## 579 TRNAL-CAG 124905913
## 580 TRNAL-CAG 124905915
## 581 TRNAL-CAG 124905917
## 582 TRNAL-CAG 124905920
## 583 TRNAL-CAG 124905922
## 584 TRNAL-CAG 124905924
## 585 TRNAL-CAG 124905926
## 586 TRNAL-CAG 124905928
## 587 TRNAL-CAG 124905930
## 588 TRNAL-CAG 124905932
## 589 TRNAL-CAG 124905934
## 590 TRNAL-CAG 124905848
## 591 TRNAL-CAG 124905850
## 592 TRNAL-CAG 124905852
## 593 TRNAL-CAG 124905906
## 594 TRNAL-CAG 124905909
## 595 TRNAL-CAG 124905911
## 596 TRNAL-CAG 124905913
## 597 TRNAL-CAG 124905915
## 598 TRNAL-CAG 124905917
## 599 TRNAL-CAG 124905920
## 600 TRNAL-CAG 124905922
## 601 TRNAL-CAG 124905924
## 602 TRNAL-CAG 124905926
## 603 TRNAL-CAG 124905928
## 604 TRNAL-CAG 124905930
## 605 TRNAL-CAG 124905932
## 606 TRNAL-CAG 124905934
## 607 TRNAL-CAG 124905848
## 608 TRNAL-CAG 124905850
## 609 TRNAL-CAG 124905852
## 610 TRNAL-CAG 124905906
## 611 TRNAL-CAG 124905909
## 612 TRNAL-CAG 124905911
## 613 TRNAL-CAG 124905913
## 614 TRNAL-CAG 124905915
## 615 TRNAL-CAG 124905917
## 616 TRNAL-CAG 124905920
## 617 TRNAL-CAG 124905922
## 618 TRNAL-CAG 124905924
## 619 TRNAL-CAG 124905926
## 620 TRNAL-CAG 124905928
## 621 TRNAL-CAG 124905930
## 622 TRNAL-CAG 124905932
## 623 TRNAL-CAG 124905934
## 624 TRNAL-CAG 124905848
## 625 TRNAL-CAG 124905850
## 626 TRNAL-CAG 124905852
## 627 TRNAL-CAG 124905906
## 628 TRNAL-CAG 124905909
## 629 TRNAL-CAG 124905911
## 630 TRNAL-CAG 124905913
## 631 TRNAL-CAG 124905915
## 632 TRNAL-CAG 124905917
## 633 TRNAL-CAG 124905920
## 634 TRNAL-CAG 124905922
## 635 TRNAL-CAG 124905924
## 636 TRNAL-CAG 124905926
## 637 TRNAL-CAG 124905928
## 638 TRNAL-CAG 124905930
## 639 TRNAL-CAG 124905932
## 640 TRNAL-CAG 124905934
## 641 TRNAD-GUC 124905854
## 642 TRNAD-GUC 124905857
## 643 TRNAD-GUC 124905860
## 644 TRNAD-GUC 124905863
## 645 TRNAD-GUC 124905866
## 646 TRNAD-GUC 124905869
## 647 TRNAD-GUC 124905872
## 648 TRNAD-GUC 124905875
## 649 TRNAD-GUC 124905878
## 650 TRNAD-GUC 124905881
## 651 TRNAD-GUC 124905884
## 652 TRNAD-GUC 124905887
## 653 TRNAD-GUC 124905890
## 654 TRNAD-GUC 124905893
## 655 TRNAD-GUC 124905896
## 656 TRNAD-GUC 124905899
## 657 TRNAD-GUC 124905902
## 658 TRNAD-GUC 124905854
## 659 TRNAD-GUC 124905857
## 660 TRNAD-GUC 124905860
## 661 TRNAD-GUC 124905863
## 662 TRNAD-GUC 124905866
## 663 TRNAD-GUC 124905869
## 664 TRNAD-GUC 124905872
## 665 TRNAD-GUC 124905875
## 666 TRNAD-GUC 124905878
## 667 TRNAD-GUC 124905881
## 668 TRNAD-GUC 124905884
## 669 TRNAD-GUC 124905887
## 670 TRNAD-GUC 124905890
## 671 TRNAD-GUC 124905893
## 672 TRNAD-GUC 124905896
## 673 TRNAD-GUC 124905899
## 674 TRNAD-GUC 124905902
## 675 TRNAD-GUC 124905854
## 676 TRNAD-GUC 124905857
## 677 TRNAD-GUC 124905860
## 678 TRNAD-GUC 124905863
## 679 TRNAD-GUC 124905866
## 680 TRNAD-GUC 124905869
## 681 TRNAD-GUC 124905872
## 682 TRNAD-GUC 124905875
## 683 TRNAD-GUC 124905878
## 684 TRNAD-GUC 124905881
## 685 TRNAD-GUC 124905884
## 686 TRNAD-GUC 124905887
## 687 TRNAD-GUC 124905890
## 688 TRNAD-GUC 124905893
## 689 TRNAD-GUC 124905896
## 690 TRNAD-GUC 124905899
## 691 TRNAD-GUC 124905902
## 692 TRNAD-GUC 124905854
## 693 TRNAD-GUC 124905857
## 694 TRNAD-GUC 124905860
## 695 TRNAD-GUC 124905863
## 696 TRNAD-GUC 124905866
## 697 TRNAD-GUC 124905869
## 698 TRNAD-GUC 124905872
## 699 TRNAD-GUC 124905875
## 700 TRNAD-GUC 124905878
## 701 TRNAD-GUC 124905881
## 702 TRNAD-GUC 124905884
## 703 TRNAD-GUC 124905887
## 704 TRNAD-GUC 124905890
## 705 TRNAD-GUC 124905893
## 706 TRNAD-GUC 124905896
## 707 TRNAD-GUC 124905899
## 708 TRNAD-GUC 124905902
## 709 TRNAD-GUC 124905854
## 710 TRNAD-GUC 124905857
## 711 TRNAD-GUC 124905860
## 712 TRNAD-GUC 124905863
## 713 TRNAD-GUC 124905866
## 714 TRNAD-GUC 124905869
## 715 TRNAD-GUC 124905872
## 716 TRNAD-GUC 124905875
## 717 TRNAD-GUC 124905878
## 718 TRNAD-GUC 124905881
## 719 TRNAD-GUC 124905884
## 720 TRNAD-GUC 124905887
## 721 TRNAD-GUC 124905890
## 722 TRNAD-GUC 124905893
## 723 TRNAD-GUC 124905896
## 724 TRNAD-GUC 124905899
## 725 TRNAD-GUC 124905902
## 726 TRNAD-GUC 124905854
## 727 TRNAD-GUC 124905857
## 728 TRNAD-GUC 124905860
## 729 TRNAD-GUC 124905863
## 730 TRNAD-GUC 124905866
## 731 TRNAD-GUC 124905869
## 732 TRNAD-GUC 124905872
## 733 TRNAD-GUC 124905875
## 734 TRNAD-GUC 124905878
## 735 TRNAD-GUC 124905881
## 736 TRNAD-GUC 124905884
## 737 TRNAD-GUC 124905887
## 738 TRNAD-GUC 124905890
## 739 TRNAD-GUC 124905893
## 740 TRNAD-GUC 124905896
## 741 TRNAD-GUC 124905899
## 742 TRNAD-GUC 124905902
## 743 TRNAD-GUC 124905854
## 744 TRNAD-GUC 124905857
## 745 TRNAD-GUC 124905860
## 746 TRNAD-GUC 124905863
## 747 TRNAD-GUC 124905866
## 748 TRNAD-GUC 124905869
## 749 TRNAD-GUC 124905872
## 750 TRNAD-GUC 124905875
## 751 TRNAD-GUC 124905878
## 752 TRNAD-GUC 124905881
## 753 TRNAD-GUC 124905884
## 754 TRNAD-GUC 124905887
## 755 TRNAD-GUC 124905890
## 756 TRNAD-GUC 124905893
## 757 TRNAD-GUC 124905896
## 758 TRNAD-GUC 124905899
## 759 TRNAD-GUC 124905902
## 760 TRNAD-GUC 124905854
## 761 TRNAD-GUC 124905857
## 762 TRNAD-GUC 124905860
## 763 TRNAD-GUC 124905863
## 764 TRNAD-GUC 124905866
## 765 TRNAD-GUC 124905869
## 766 TRNAD-GUC 124905872
## 767 TRNAD-GUC 124905875
## 768 TRNAD-GUC 124905878
## 769 TRNAD-GUC 124905881
## 770 TRNAD-GUC 124905884
## 771 TRNAD-GUC 124905887
## 772 TRNAD-GUC 124905890
## 773 TRNAD-GUC 124905893
## 774 TRNAD-GUC 124905896
## 775 TRNAD-GUC 124905899
## 776 TRNAD-GUC 124905902
## 777 TRNAD-GUC 124905854
## 778 TRNAD-GUC 124905857
## 779 TRNAD-GUC 124905860
## 780 TRNAD-GUC 124905863
## 781 TRNAD-GUC 124905866
## 782 TRNAD-GUC 124905869
## 783 TRNAD-GUC 124905872
## 784 TRNAD-GUC 124905875
## 785 TRNAD-GUC 124905878
## 786 TRNAD-GUC 124905881
## 787 TRNAD-GUC 124905884
## 788 TRNAD-GUC 124905887
## 789 TRNAD-GUC 124905890
## 790 TRNAD-GUC 124905893
## 791 TRNAD-GUC 124905896
## 792 TRNAD-GUC 124905899
## 793 TRNAD-GUC 124905902
## 794 TRNAD-GUC 124905854
## 795 TRNAD-GUC 124905857
## 796 TRNAD-GUC 124905860
## 797 TRNAD-GUC 124905863
## 798 TRNAD-GUC 124905866
## 799 TRNAD-GUC 124905869
## 800 TRNAD-GUC 124905872
## 801 TRNAD-GUC 124905875
## 802 TRNAD-GUC 124905878
## 803 TRNAD-GUC 124905881
## 804 TRNAD-GUC 124905884
## 805 TRNAD-GUC 124905887
## 806 TRNAD-GUC 124905890
## 807 TRNAD-GUC 124905893
## 808 TRNAD-GUC 124905896
## 809 TRNAD-GUC 124905899
## 810 TRNAD-GUC 124905902
## 811 TRNAD-GUC 124905854
## 812 TRNAD-GUC 124905857
## 813 TRNAD-GUC 124905860
## 814 TRNAD-GUC 124905863
## 815 TRNAD-GUC 124905866
## 816 TRNAD-GUC 124905869
## 817 TRNAD-GUC 124905872
## 818 TRNAD-GUC 124905875
## 819 TRNAD-GUC 124905878
## 820 TRNAD-GUC 124905881
## 821 TRNAD-GUC 124905884
## 822 TRNAD-GUC 124905887
## 823 TRNAD-GUC 124905890
## 824 TRNAD-GUC 124905893
## 825 TRNAD-GUC 124905896
## 826 TRNAD-GUC 124905899
## 827 TRNAD-GUC 124905902
## 828 TRNAD-GUC 124905854
## 829 TRNAD-GUC 124905857
## 830 TRNAD-GUC 124905860
## 831 TRNAD-GUC 124905863
## 832 TRNAD-GUC 124905866
## 833 TRNAD-GUC 124905869
## 834 TRNAD-GUC 124905872
## 835 TRNAD-GUC 124905875
## 836 TRNAD-GUC 124905878
## 837 TRNAD-GUC 124905881
## 838 TRNAD-GUC 124905884
## 839 TRNAD-GUC 124905887
## 840 TRNAD-GUC 124905890
## 841 TRNAD-GUC 124905893
## 842 TRNAD-GUC 124905896
## 843 TRNAD-GUC 124905899
## 844 TRNAD-GUC 124905902
## 845 TRNAD-GUC 124905854
## 846 TRNAD-GUC 124905857
## 847 TRNAD-GUC 124905860
## 848 TRNAD-GUC 124905863
## 849 TRNAD-GUC 124905866
## 850 TRNAD-GUC 124905869
## 851 TRNAD-GUC 124905872
## 852 TRNAD-GUC 124905875
## 853 TRNAD-GUC 124905878
## 854 TRNAD-GUC 124905881
## 855 TRNAD-GUC 124905884
## 856 TRNAD-GUC 124905887
## 857 TRNAD-GUC 124905890
## 858 TRNAD-GUC 124905893
## 859 TRNAD-GUC 124905896
## 860 TRNAD-GUC 124905899
## 861 TRNAD-GUC 124905902
## 862 TRNAD-GUC 124905854
## 863 TRNAD-GUC 124905857
## 864 TRNAD-GUC 124905860
## 865 TRNAD-GUC 124905863
## 866 TRNAD-GUC 124905866
## 867 TRNAD-GUC 124905869
## 868 TRNAD-GUC 124905872
## 869 TRNAD-GUC 124905875
## 870 TRNAD-GUC 124905878
## 871 TRNAD-GUC 124905881
## 872 TRNAD-GUC 124905884
## 873 TRNAD-GUC 124905887
## 874 TRNAD-GUC 124905890
## 875 TRNAD-GUC 124905893
## 876 TRNAD-GUC 124905896
## 877 TRNAD-GUC 124905899
## 878 TRNAD-GUC 124905902
## 879 TRNAD-GUC 124905854
## 880 TRNAD-GUC 124905857
## 881 TRNAD-GUC 124905860
## 882 TRNAD-GUC 124905863
## 883 TRNAD-GUC 124905866
## 884 TRNAD-GUC 124905869
## 885 TRNAD-GUC 124905872
## 886 TRNAD-GUC 124905875
## 887 TRNAD-GUC 124905878
## 888 TRNAD-GUC 124905881
## 889 TRNAD-GUC 124905884
## 890 TRNAD-GUC 124905887
## 891 TRNAD-GUC 124905890
## 892 TRNAD-GUC 124905893
## 893 TRNAD-GUC 124905896
## 894 TRNAD-GUC 124905899
## 895 TRNAD-GUC 124905902
## 896 TRNAD-GUC 124905854
## 897 TRNAD-GUC 124905857
## 898 TRNAD-GUC 124905860
## 899 TRNAD-GUC 124905863
## 900 TRNAD-GUC 124905866
## 901 TRNAD-GUC 124905869
## 902 TRNAD-GUC 124905872
## 903 TRNAD-GUC 124905875
## 904 TRNAD-GUC 124905878
## 905 TRNAD-GUC 124905881
## 906 TRNAD-GUC 124905884
## 907 TRNAD-GUC 124905887
## 908 TRNAD-GUC 124905890
## 909 TRNAD-GUC 124905893
## 910 TRNAD-GUC 124905896
## 911 TRNAD-GUC 124905899
## 912 TRNAD-GUC 124905902
## 913 TRNAE-CUC 124905855
## 914 TRNAE-CUC 124905858
## 915 TRNAE-CUC 124905861
## 916 TRNAE-CUC 124905864
## 917 TRNAE-CUC 124905867
## 918 TRNAE-CUC 124905870
## 919 TRNAE-CUC 124905873
## 920 TRNAE-CUC 124905876
## 921 TRNAE-CUC 124905879
## 922 TRNAE-CUC 124905882
## 923 TRNAE-CUC 124905885
## 924 TRNAE-CUC 124905888
## 925 TRNAE-CUC 124905891
## 926 TRNAE-CUC 124905894
## 927 TRNAE-CUC 124905897
## 928 TRNAE-CUC 124905900
## 929 TRNAE-CUC 124905903
## 930 TRNAE-CUC 124905855
## 931 TRNAE-CUC 124905858
## 932 TRNAE-CUC 124905861
## 933 TRNAE-CUC 124905864
## 934 TRNAE-CUC 124905867
## 935 TRNAE-CUC 124905870
## 936 TRNAE-CUC 124905873
## 937 TRNAE-CUC 124905876
## 938 TRNAE-CUC 124905879
## 939 TRNAE-CUC 124905882
## 940 TRNAE-CUC 124905885
## 941 TRNAE-CUC 124905888
## 942 TRNAE-CUC 124905891
## 943 TRNAE-CUC 124905894
## 944 TRNAE-CUC 124905897
## 945 TRNAE-CUC 124905900
## 946 TRNAE-CUC 124905903
## 947 TRNAE-CUC 124905855
## 948 TRNAE-CUC 124905858
## 949 TRNAE-CUC 124905861
## 950 TRNAE-CUC 124905864
## 951 TRNAE-CUC 124905867
## 952 TRNAE-CUC 124905870
## 953 TRNAE-CUC 124905873
## 954 TRNAE-CUC 124905876
## 955 TRNAE-CUC 124905879
## 956 TRNAE-CUC 124905882
## 957 TRNAE-CUC 124905885
## 958 TRNAE-CUC 124905888
## 959 TRNAE-CUC 124905891
## 960 TRNAE-CUC 124905894
## 961 TRNAE-CUC 124905897
## 962 TRNAE-CUC 124905900
## 963 TRNAE-CUC 124905903
## 964 TRNAE-CUC 124905855
## 965 TRNAE-CUC 124905858
## 966 TRNAE-CUC 124905861
## 967 TRNAE-CUC 124905864
## 968 TRNAE-CUC 124905867
## 969 TRNAE-CUC 124905870
## 970 TRNAE-CUC 124905873
## 971 TRNAE-CUC 124905876
## 972 TRNAE-CUC 124905879
## 973 TRNAE-CUC 124905882
## 974 TRNAE-CUC 124905885
## 975 TRNAE-CUC 124905888
## 976 TRNAE-CUC 124905891
## 977 TRNAE-CUC 124905894
## 978 TRNAE-CUC 124905897
## 979 TRNAE-CUC 124905900
## 980 TRNAE-CUC 124905903
## 981 TRNAE-CUC 124905855
## 982 TRNAE-CUC 124905858
## 983 TRNAE-CUC 124905861
## 984 TRNAE-CUC 124905864
## 985 TRNAE-CUC 124905867
## 986 TRNAE-CUC 124905870
## 987 TRNAE-CUC 124905873
## 988 TRNAE-CUC 124905876
## 989 TRNAE-CUC 124905879
## 990 TRNAE-CUC 124905882
## 991 TRNAE-CUC 124905885
## 992 TRNAE-CUC 124905888
## 993 TRNAE-CUC 124905891
## 994 TRNAE-CUC 124905894
## 995 TRNAE-CUC 124905897
## 996 TRNAE-CUC 124905900
## 997 TRNAE-CUC 124905903
## 998 TRNAE-CUC 124905855
## 999 TRNAE-CUC 124905858
## 1000 TRNAE-CUC 124905861
## 1001 TRNAE-CUC 124905864
## 1002 TRNAE-CUC 124905867
## 1003 TRNAE-CUC 124905870
## 1004 TRNAE-CUC 124905873
## 1005 TRNAE-CUC 124905876
## 1006 TRNAE-CUC 124905879
## 1007 TRNAE-CUC 124905882
## 1008 TRNAE-CUC 124905885
## 1009 TRNAE-CUC 124905888
## 1010 TRNAE-CUC 124905891
## 1011 TRNAE-CUC 124905894
## 1012 TRNAE-CUC 124905897
## 1013 TRNAE-CUC 124905900
## 1014 TRNAE-CUC 124905903
## 1015 TRNAE-CUC 124905855
## 1016 TRNAE-CUC 124905858
## 1017 TRNAE-CUC 124905861
## 1018 TRNAE-CUC 124905864
## 1019 TRNAE-CUC 124905867
## 1020 TRNAE-CUC 124905870
## 1021 TRNAE-CUC 124905873
## 1022 TRNAE-CUC 124905876
## 1023 TRNAE-CUC 124905879
## 1024 TRNAE-CUC 124905882
## 1025 TRNAE-CUC 124905885
## 1026 TRNAE-CUC 124905888
## 1027 TRNAE-CUC 124905891
## 1028 TRNAE-CUC 124905894
## 1029 TRNAE-CUC 124905897
## 1030 TRNAE-CUC 124905900
## 1031 TRNAE-CUC 124905903
## 1032 TRNAE-CUC 124905855
## 1033 TRNAE-CUC 124905858
## 1034 TRNAE-CUC 124905861
## 1035 TRNAE-CUC 124905864
## 1036 TRNAE-CUC 124905867
## 1037 TRNAE-CUC 124905870
## 1038 TRNAE-CUC 124905873
## 1039 TRNAE-CUC 124905876
## 1040 TRNAE-CUC 124905879
## 1041 TRNAE-CUC 124905882
## 1042 TRNAE-CUC 124905885
## 1043 TRNAE-CUC 124905888
## 1044 TRNAE-CUC 124905891
## 1045 TRNAE-CUC 124905894
## 1046 TRNAE-CUC 124905897
## 1047 TRNAE-CUC 124905900
## 1048 TRNAE-CUC 124905903
## 1049 TRNAE-CUC 124905855
## 1050 TRNAE-CUC 124905858
## 1051 TRNAE-CUC 124905861
## 1052 TRNAE-CUC 124905864
## 1053 TRNAE-CUC 124905867
## 1054 TRNAE-CUC 124905870
## 1055 TRNAE-CUC 124905873
## 1056 TRNAE-CUC 124905876
## 1057 TRNAE-CUC 124905879
## 1058 TRNAE-CUC 124905882
## 1059 TRNAE-CUC 124905885
## 1060 TRNAE-CUC 124905888
## 1061 TRNAE-CUC 124905891
## 1062 TRNAE-CUC 124905894
## 1063 TRNAE-CUC 124905897
## 1064 TRNAE-CUC 124905900
## 1065 TRNAE-CUC 124905903
## 1066 TRNAE-CUC 124905855
## 1067 TRNAE-CUC 124905858
## 1068 TRNAE-CUC 124905861
## 1069 TRNAE-CUC 124905864
## 1070 TRNAE-CUC 124905867
## 1071 TRNAE-CUC 124905870
## 1072 TRNAE-CUC 124905873
## 1073 TRNAE-CUC 124905876
## 1074 TRNAE-CUC 124905879
## 1075 TRNAE-CUC 124905882
## 1076 TRNAE-CUC 124905885
## 1077 TRNAE-CUC 124905888
## 1078 TRNAE-CUC 124905891
## 1079 TRNAE-CUC 124905894
## 1080 TRNAE-CUC 124905897
## 1081 TRNAE-CUC 124905900
## 1082 TRNAE-CUC 124905903
## 1083 TRNAE-CUC 124905855
## 1084 TRNAE-CUC 124905858
## 1085 TRNAE-CUC 124905861
## 1086 TRNAE-CUC 124905864
## 1087 TRNAE-CUC 124905867
## 1088 TRNAE-CUC 124905870
## 1089 TRNAE-CUC 124905873
## 1090 TRNAE-CUC 124905876
## 1091 TRNAE-CUC 124905879
## 1092 TRNAE-CUC 124905882
## 1093 TRNAE-CUC 124905885
## 1094 TRNAE-CUC 124905888
## 1095 TRNAE-CUC 124905891
## 1096 TRNAE-CUC 124905894
## 1097 TRNAE-CUC 124905897
## 1098 TRNAE-CUC 124905900
## 1099 TRNAE-CUC 124905903
## 1100 TRNAE-CUC 124905855
## 1101 TRNAE-CUC 124905858
## 1102 TRNAE-CUC 124905861
## 1103 TRNAE-CUC 124905864
## 1104 TRNAE-CUC 124905867
## 1105 TRNAE-CUC 124905870
## 1106 TRNAE-CUC 124905873
## 1107 TRNAE-CUC 124905876
## 1108 TRNAE-CUC 124905879
## 1109 TRNAE-CUC 124905882
## 1110 TRNAE-CUC 124905885
## 1111 TRNAE-CUC 124905888
## 1112 TRNAE-CUC 124905891
## 1113 TRNAE-CUC 124905894
## 1114 TRNAE-CUC 124905897
## 1115 TRNAE-CUC 124905900
## 1116 TRNAE-CUC 124905903
## 1117 TRNAE-CUC 124905855
## 1118 TRNAE-CUC 124905858
## 1119 TRNAE-CUC 124905861
## 1120 TRNAE-CUC 124905864
## 1121 TRNAE-CUC 124905867
## 1122 TRNAE-CUC 124905870
## 1123 TRNAE-CUC 124905873
## 1124 TRNAE-CUC 124905876
## 1125 TRNAE-CUC 124905879
## 1126 TRNAE-CUC 124905882
## 1127 TRNAE-CUC 124905885
## 1128 TRNAE-CUC 124905888
## 1129 TRNAE-CUC 124905891
## 1130 TRNAE-CUC 124905894
## 1131 TRNAE-CUC 124905897
## 1132 TRNAE-CUC 124905900
## 1133 TRNAE-CUC 124905903
## 1134 TRNAE-CUC 124905855
## 1135 TRNAE-CUC 124905858
## 1136 TRNAE-CUC 124905861
## 1137 TRNAE-CUC 124905864
## 1138 TRNAE-CUC 124905867
## 1139 TRNAE-CUC 124905870
## 1140 TRNAE-CUC 124905873
## 1141 TRNAE-CUC 124905876
## 1142 TRNAE-CUC 124905879
## 1143 TRNAE-CUC 124905882
## 1144 TRNAE-CUC 124905885
## 1145 TRNAE-CUC 124905888
## 1146 TRNAE-CUC 124905891
## 1147 TRNAE-CUC 124905894
## 1148 TRNAE-CUC 124905897
## 1149 TRNAE-CUC 124905900
## 1150 TRNAE-CUC 124905903
## 1151 TRNAE-CUC 124905855
## 1152 TRNAE-CUC 124905858
## 1153 TRNAE-CUC 124905861
## 1154 TRNAE-CUC 124905864
## 1155 TRNAE-CUC 124905867
## 1156 TRNAE-CUC 124905870
## 1157 TRNAE-CUC 124905873
## 1158 TRNAE-CUC 124905876
## 1159 TRNAE-CUC 124905879
## 1160 TRNAE-CUC 124905882
## 1161 TRNAE-CUC 124905885
## 1162 TRNAE-CUC 124905888
## 1163 TRNAE-CUC 124905891
## 1164 TRNAE-CUC 124905894
## 1165 TRNAE-CUC 124905897
## 1166 TRNAE-CUC 124905900
## 1167 TRNAE-CUC 124905903
## 1168 TRNAE-CUC 124905855
## 1169 TRNAE-CUC 124905858
## 1170 TRNAE-CUC 124905861
## 1171 TRNAE-CUC 124905864
## 1172 TRNAE-CUC 124905867
## 1173 TRNAE-CUC 124905870
## 1174 TRNAE-CUC 124905873
## 1175 TRNAE-CUC 124905876
## 1176 TRNAE-CUC 124905879
## 1177 TRNAE-CUC 124905882
## 1178 TRNAE-CUC 124905885
## 1179 TRNAE-CUC 124905888
## 1180 TRNAE-CUC 124905891
## 1181 TRNAE-CUC 124905894
## 1182 TRNAE-CUC 124905897
## 1183 TRNAE-CUC 124905900
## 1184 TRNAE-CUC 124905903
## 1185 TRNAG-UCC 124905856
## 1186 TRNAG-UCC 124905859
## 1187 TRNAG-UCC 124905862
## 1188 TRNAG-UCC 124905865
## 1189 TRNAG-UCC 124905868
## 1190 TRNAG-UCC 124905871
## 1191 TRNAG-UCC 124905874
## 1192 TRNAG-UCC 124905877
## 1193 TRNAG-UCC 124905880
## 1194 TRNAG-UCC 124905883
## 1195 TRNAG-UCC 124905886
## 1196 TRNAG-UCC 124905889
## 1197 TRNAG-UCC 124905892
## 1198 TRNAG-UCC 124905895
## 1199 TRNAG-UCC 124905898
## 1200 TRNAG-UCC 124905901
## 1201 TRNAG-UCC 124905904
## 1202 TRNAG-UCC 124905856
## 1203 TRNAG-UCC 124905859
## 1204 TRNAG-UCC 124905862
## 1205 TRNAG-UCC 124905865
## 1206 TRNAG-UCC 124905868
## 1207 TRNAG-UCC 124905871
## 1208 TRNAG-UCC 124905874
## 1209 TRNAG-UCC 124905877
## 1210 TRNAG-UCC 124905880
## 1211 TRNAG-UCC 124905883
## 1212 TRNAG-UCC 124905886
## 1213 TRNAG-UCC 124905889
## 1214 TRNAG-UCC 124905892
## 1215 TRNAG-UCC 124905895
## 1216 TRNAG-UCC 124905898
## 1217 TRNAG-UCC 124905901
## 1218 TRNAG-UCC 124905904
## 1219 TRNAG-UCC 124905856
## 1220 TRNAG-UCC 124905859
## 1221 TRNAG-UCC 124905862
## 1222 TRNAG-UCC 124905865
## 1223 TRNAG-UCC 124905868
## 1224 TRNAG-UCC 124905871
## 1225 TRNAG-UCC 124905874
## 1226 TRNAG-UCC 124905877
## 1227 TRNAG-UCC 124905880
## 1228 TRNAG-UCC 124905883
## 1229 TRNAG-UCC 124905886
## 1230 TRNAG-UCC 124905889
## 1231 TRNAG-UCC 124905892
## 1232 TRNAG-UCC 124905895
## 1233 TRNAG-UCC 124905898
## 1234 TRNAG-UCC 124905901
## 1235 TRNAG-UCC 124905904
## 1236 TRNAG-UCC 124905856
## 1237 TRNAG-UCC 124905859
## 1238 TRNAG-UCC 124905862
## 1239 TRNAG-UCC 124905865
## 1240 TRNAG-UCC 124905868
## 1241 TRNAG-UCC 124905871
## 1242 TRNAG-UCC 124905874
## 1243 TRNAG-UCC 124905877
## 1244 TRNAG-UCC 124905880
## 1245 TRNAG-UCC 124905883
## 1246 TRNAG-UCC 124905886
## 1247 TRNAG-UCC 124905889
## 1248 TRNAG-UCC 124905892
## 1249 TRNAG-UCC 124905895
## 1250 TRNAG-UCC 124905898
## 1251 TRNAG-UCC 124905901
## 1252 TRNAG-UCC 124905904
## 1253 TRNAG-UCC 124905856
## 1254 TRNAG-UCC 124905859
## 1255 TRNAG-UCC 124905862
## 1256 TRNAG-UCC 124905865
## 1257 TRNAG-UCC 124905868
## 1258 TRNAG-UCC 124905871
## 1259 TRNAG-UCC 124905874
## 1260 TRNAG-UCC 124905877
## 1261 TRNAG-UCC 124905880
## 1262 TRNAG-UCC 124905883
## 1263 TRNAG-UCC 124905886
## 1264 TRNAG-UCC 124905889
## 1265 TRNAG-UCC 124905892
## 1266 TRNAG-UCC 124905895
## 1267 TRNAG-UCC 124905898
## 1268 TRNAG-UCC 124905901
## 1269 TRNAG-UCC 124905904
## 1270 TRNAG-UCC 124905856
## 1271 TRNAG-UCC 124905859
## 1272 TRNAG-UCC 124905862
## 1273 TRNAG-UCC 124905865
## 1274 TRNAG-UCC 124905868
## 1275 TRNAG-UCC 124905871
## 1276 TRNAG-UCC 124905874
## 1277 TRNAG-UCC 124905877
## 1278 TRNAG-UCC 124905880
## 1279 TRNAG-UCC 124905883
## 1280 TRNAG-UCC 124905886
## 1281 TRNAG-UCC 124905889
## 1282 TRNAG-UCC 124905892
## 1283 TRNAG-UCC 124905895
## 1284 TRNAG-UCC 124905898
## 1285 TRNAG-UCC 124905901
## 1286 TRNAG-UCC 124905904
## 1287 TRNAG-UCC 124905856
## 1288 TRNAG-UCC 124905859
## 1289 TRNAG-UCC 124905862
## 1290 TRNAG-UCC 124905865
## 1291 TRNAG-UCC 124905868
## 1292 TRNAG-UCC 124905871
## 1293 TRNAG-UCC 124905874
## 1294 TRNAG-UCC 124905877
## 1295 TRNAG-UCC 124905880
## 1296 TRNAG-UCC 124905883
## 1297 TRNAG-UCC 124905886
## 1298 TRNAG-UCC 124905889
## 1299 TRNAG-UCC 124905892
## 1300 TRNAG-UCC 124905895
## 1301 TRNAG-UCC 124905898
## 1302 TRNAG-UCC 124905901
## 1303 TRNAG-UCC 124905904
## 1304 TRNAG-UCC 124905856
## 1305 TRNAG-UCC 124905859
## 1306 TRNAG-UCC 124905862
## 1307 TRNAG-UCC 124905865
## 1308 TRNAG-UCC 124905868
## 1309 TRNAG-UCC 124905871
## 1310 TRNAG-UCC 124905874
## 1311 TRNAG-UCC 124905877
## 1312 TRNAG-UCC 124905880
## 1313 TRNAG-UCC 124905883
## 1314 TRNAG-UCC 124905886
## 1315 TRNAG-UCC 124905889
## 1316 TRNAG-UCC 124905892
## 1317 TRNAG-UCC 124905895
## 1318 TRNAG-UCC 124905898
## 1319 TRNAG-UCC 124905901
## 1320 TRNAG-UCC 124905904
## 1321 TRNAG-UCC 124905856
## 1322 TRNAG-UCC 124905859
## 1323 TRNAG-UCC 124905862
## 1324 TRNAG-UCC 124905865
## 1325 TRNAG-UCC 124905868
## 1326 TRNAG-UCC 124905871
## 1327 TRNAG-UCC 124905874
## 1328 TRNAG-UCC 124905877
## 1329 TRNAG-UCC 124905880
## 1330 TRNAG-UCC 124905883
## 1331 TRNAG-UCC 124905886
## 1332 TRNAG-UCC 124905889
## 1333 TRNAG-UCC 124905892
## 1334 TRNAG-UCC 124905895
## 1335 TRNAG-UCC 124905898
## 1336 TRNAG-UCC 124905901
## 1337 TRNAG-UCC 124905904
## 1338 TRNAG-UCC 124905856
## 1339 TRNAG-UCC 124905859
## 1340 TRNAG-UCC 124905862
## 1341 TRNAG-UCC 124905865
## 1342 TRNAG-UCC 124905868
## 1343 TRNAG-UCC 124905871
## 1344 TRNAG-UCC 124905874
## 1345 TRNAG-UCC 124905877
## 1346 TRNAG-UCC 124905880
## 1347 TRNAG-UCC 124905883
## 1348 TRNAG-UCC 124905886
## 1349 TRNAG-UCC 124905889
## 1350 TRNAG-UCC 124905892
## 1351 TRNAG-UCC 124905895
## 1352 TRNAG-UCC 124905898
## 1353 TRNAG-UCC 124905901
## 1354 TRNAG-UCC 124905904
## 1355 TRNAG-UCC 124905856
## 1356 TRNAG-UCC 124905859
## 1357 TRNAG-UCC 124905862
## 1358 TRNAG-UCC 124905865
## 1359 TRNAG-UCC 124905868
## 1360 TRNAG-UCC 124905871
## 1361 TRNAG-UCC 124905874
## 1362 TRNAG-UCC 124905877
## 1363 TRNAG-UCC 124905880
## 1364 TRNAG-UCC 124905883
## 1365 TRNAG-UCC 124905886
## 1366 TRNAG-UCC 124905889
## 1367 TRNAG-UCC 124905892
## 1368 TRNAG-UCC 124905895
## 1369 TRNAG-UCC 124905898
## 1370 TRNAG-UCC 124905901
## 1371 TRNAG-UCC 124905904
## 1372 TRNAG-UCC 124905856
## 1373 TRNAG-UCC 124905859
## 1374 TRNAG-UCC 124905862
## 1375 TRNAG-UCC 124905865
## 1376 TRNAG-UCC 124905868
## 1377 TRNAG-UCC 124905871
## 1378 TRNAG-UCC 124905874
## 1379 TRNAG-UCC 124905877
## 1380 TRNAG-UCC 124905880
## 1381 TRNAG-UCC 124905883
## 1382 TRNAG-UCC 124905886
## 1383 TRNAG-UCC 124905889
## 1384 TRNAG-UCC 124905892
## 1385 TRNAG-UCC 124905895
## 1386 TRNAG-UCC 124905898
## 1387 TRNAG-UCC 124905901
## 1388 TRNAG-UCC 124905904
## 1389 TRNAG-UCC 124905856
## 1390 TRNAG-UCC 124905859
## 1391 TRNAG-UCC 124905862
## 1392 TRNAG-UCC 124905865
## 1393 TRNAG-UCC 124905868
## 1394 TRNAG-UCC 124905871
## 1395 TRNAG-UCC 124905874
## 1396 TRNAG-UCC 124905877
## 1397 TRNAG-UCC 124905880
## 1398 TRNAG-UCC 124905883
## 1399 TRNAG-UCC 124905886
## 1400 TRNAG-UCC 124905889
## 1401 TRNAG-UCC 124905892
## 1402 TRNAG-UCC 124905895
## 1403 TRNAG-UCC 124905898
## 1404 TRNAG-UCC 124905901
## 1405 TRNAG-UCC 124905904
## 1406 TRNAG-UCC 124905856
## 1407 TRNAG-UCC 124905859
## 1408 TRNAG-UCC 124905862
## 1409 TRNAG-UCC 124905865
## 1410 TRNAG-UCC 124905868
## 1411 TRNAG-UCC 124905871
## 1412 TRNAG-UCC 124905874
## 1413 TRNAG-UCC 124905877
## 1414 TRNAG-UCC 124905880
## 1415 TRNAG-UCC 124905883
## 1416 TRNAG-UCC 124905886
## 1417 TRNAG-UCC 124905889
## 1418 TRNAG-UCC 124905892
## 1419 TRNAG-UCC 124905895
## 1420 TRNAG-UCC 124905898
## 1421 TRNAG-UCC 124905901
## 1422 TRNAG-UCC 124905904
## 1423 TRNAG-UCC 124905856
## 1424 TRNAG-UCC 124905859
## 1425 TRNAG-UCC 124905862
## 1426 TRNAG-UCC 124905865
## 1427 TRNAG-UCC 124905868
## 1428 TRNAG-UCC 124905871
## 1429 TRNAG-UCC 124905874
## 1430 TRNAG-UCC 124905877
## 1431 TRNAG-UCC 124905880
## 1432 TRNAG-UCC 124905883
## 1433 TRNAG-UCC 124905886
## 1434 TRNAG-UCC 124905889
## 1435 TRNAG-UCC 124905892
## 1436 TRNAG-UCC 124905895
## 1437 TRNAG-UCC 124905898
## 1438 TRNAG-UCC 124905901
## 1439 TRNAG-UCC 124905904
## 1440 TRNAG-UCC 124905856
## 1441 TRNAG-UCC 124905859
## 1442 TRNAG-UCC 124905862
## 1443 TRNAG-UCC 124905865
## 1444 TRNAG-UCC 124905868
## 1445 TRNAG-UCC 124905871
## 1446 TRNAG-UCC 124905874
## 1447 TRNAG-UCC 124905877
## 1448 TRNAG-UCC 124905880
## 1449 TRNAG-UCC 124905883
## 1450 TRNAG-UCC 124905886
## 1451 TRNAG-UCC 124905889
## 1452 TRNAG-UCC 124905892
## 1453 TRNAG-UCC 124905895
## 1454 TRNAG-UCC 124905898
## 1455 TRNAG-UCC 124905901
## 1456 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:0070062 1
## 5 GO:0003674 1
## 6 GO:0008150 1
## 7 GO:0072562 1
## 8 GO:0062023 1
## 9 GO:0034774 1
## 10 GO:1904813 1
## 11 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.
[ Back to top ]