**R version**: `r R.version.string`
**Bioconductor version**: `r BiocManager::version()`
**Package version**: `r packageVersion("annotation")`
| 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. |
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:
```{r}
res <- grs[[1]]
head(res, n=3)
```
## 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 ]
# 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. ```{r} dog <- query(orgs, "Canis familiaris")[[1]] dog ``` ```{r} columns(dog) ``` 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. ```{r} keytypes(dog) ``` 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. ```{r} head(keys(dog, keytype="ENTREZID")) ``` 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: ```{r} keys(dog, keytype="SYMBOL", pattern="COX") ``` 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": ```{r} keys(dog, keytype="ENTREZID", pattern="COX", column="SYMBOL") ``` 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. ```{r} select(dog, keys="804478", columns=c("SYMBOL","REFSEQ"), keytype="ENTREZID") ``` When you call it, select will return a data.frame that attempts to fill in matching values for all the columns you requested. However, if you ask select for things that have a many to one relationship to your keys it can result in an expansion of the data object that is returned. For example, watch what happens when we ask for the GO terms for the same entrez gene ID: ```{r} select(dog, keys="804478", columns="GO", keytype="ENTREZID") ``` Because there are several GO terms associated with the gene "804478", you end up with many rows in the data.frame. This can become problematic if you then ask for several columns that have a many to one relationship to the original key. If you were to do that, not only would the result multiply in size, it would also become really hard to use. A better strategy is to be selective when using select. Sometimes you might want to look up matching results in a way that is simpler than the data.frame object that select returns. This is especially true when you only want to look up one kind of value per key. For these cases, we recommend that you look at the mapIds method. Lets look at what happens if request the same basic information as in our recent select call, but instead using the mapIds method: ```{r} mapIds(dog, keys="804478", column="GO", keytype="ENTREZID") ``` 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: ```{r} mapIds(dog, keys="804478", column="GO", keytype="ENTREZID", multiVals="list") ``` 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. ## 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 ]
# 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). ```{r} txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene txdb ``` Just by looking at the TxDb object, we can learn a lot about what data it contains including where the data came from, which build of the UCSC genome it was based on and the last time that the object was updated. One of the most common uses for a TxDb object is to extract various kinds of transcript data out of it. So for example you can extract all the transcripts out of the TxDb as a GRanges object like this: ```{r} txs <- transcripts(txdb) txs ``` Similarly, there are also extractors for exons(), cds(), genes() and promoters(). Which kind of feature you choose to extract just depends on what information you are after. These basic extractors are fine if you only want a flat representation of these data, but many of these features are inherently nested. So instead of extracting a flat GRanges object, you might choose instead to extract a GRangesList object that groups the transcripts by the genes that they are associated with like this: ```{r} txby <- transcriptsBy(txdb, by="gene") txby ``` 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: ```{r} si <- seqinfo(txdb) si ``` And since the seqinfo information is also attached to the GRanges objects produced by the TxDb extractors, you can also call seqinfo on the results of those methods like this: ```{r} 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). ```{r} head(seqlevels(txdb)) seqlevelsStyle(txdb) seqlevelsStyle(txdb) <- "NCBI" head(seqlevels(txdb)) ## then change it back seqlevelsStyle(txdb) <- "UCSC" head(seqlevels(txdb)) ``` 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'. ```{r} head(isActiveSeq(txdb), n=30) ``` But sometimes you might wish to ignore some of them. For example, lets suppose that you wanted to ignore the Y chromosome from our txdb. You could do that like so: ```{r eval=FALSE} isActiveSeq(txdb)["chrY"] <- FALSE head(isActiveSeq(txdb), n=26) ``` ## 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 ]
# 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. ```{r} 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(): ```{r} supported <- supportedOrganisms() print(supported, n=Inf) ``` Notice how there are multiple entries for a single organism (e.g. three for Homo sapiens). There is only one OrgDb per organism, but different TxDbs can be used. To specify a certain version of a TxDb to use, we can use the src_organism() function to create an src_organism object. ```{r} library(org.Hs.eg.db) library(TxDb.Hsapiens.UCSC.hg38.knownGene) ``` ```{r eval=FALSE} src <- src_organism("TxDb.Hsapiens.UCSC.hg38.knownGene") ``` ```{r echo=FALSE} src <- src_organism("TxDb.Hsapiens.UCSC.hg38.knownGene", dbpath=tempfile()) ``` ```{r} src ``` We can also create one using the src_ucsc() function. This will create an src_organism object using the most recent TxDb version available: ```{r eval=FALSE} src <- src_ucsc("Homo sapiens") ``` ```{r} src ``` The five methods that worked for all of the other Db objects that we have discussed (keytypes(), columns(), keys(), select(), and mapIds()) all work for src_organism objects. Here, we use keytypes() to show which keytypes can be passed to the keytype argument of select(). ```{r} keytypes(src) ``` Use columns() to show which keytypes can be passed to the keytype argument of select(). ```{r} columns(src) ``` 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: ```{r} select(src, keys="4488", columns=c("symbol", "tx_name"), keytype="entrez") ``` Organism.dplyr also supports numerous Genomic Extractor functions allowing users to filter based on information contained in the OrgDb and TxDb objects. To see the filters supported by a src_organism() object, use supportedFIlters(): ```{r} head(supportedFilters(src)) ``` The ranged based accessors such as those in GenomicFeatures will also work. There are also "_tbl" functions (e.g. transcripts_tbl()) that return tbl objects instead of GRanges objects. Complex filter statements can be given as input. Here we declare a GRangesFilter and use two different type-returning accessors to query transcripts that either start with "SNORD" and are within our given GRangesFilter, or have symbol with symbol "ADA": ```{r} gr <- GRangesFilter(GenomicRanges::GRanges("chr1:44000000-55000000")) transcripts(src, filter=~(symbol %startsWith% "SNORD" & gr) | symbol == "ADA") transcripts_tbl(src, filter=~(symbol %startsWith% "SNORD" & gr) | symbol == "ADA") ``` ## 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 ]
# 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. ```{r} head(available.genomes()) ``` 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): ```{r} ls(2) Hsapiens ``` 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. ```{r} seqNms <- seqnames(Hsapiens) head(seqNms) getSeq(Hsapiens, seqNms[1:2]) ``` Whereas if you give the a GRanges object for the 2nd argument, you can instead get a DNAStringSet that corresponds to those ranges. This can be a powerful way to learn what sequence was present from a particular range. For example, here we can extract the range of a specific gene of interest like this. ```{r eval=FALSE} txby <- transcriptsBy(txdb, by="gene") geneOfInterest <- txby[["4488"]] res <- getSeq(Hsapiens, geneOfInterest) res ``` Additionally, the Biostrings[11] package has many useful functions for finding a pattern in a string set etc. You may not have noticed when it happened, but the Biostrings package was loaded when you loaded the BSgenome object, so these functions will already be available for you to explore. ## BSgenome exercises Exercise 10: Use what you have just learned to extract the sequence for the PTEN gene.[ Back to top ]
# biomaRt Another great annotation resource is the biomaRt package[5,6,7]. The biomaRt package exposes a huge family of different online annotation resources called marts. Each mart is another of a set of online web resources that are following a convention that allows them to work with this package. So the first step in using biomaRt is always to load the package and then decide which "mart" you want to use. Once you have made your decision, you will then use the useEnsembl() method to create a mart object in your R session. Here we are looking at the marts available and then choosing to use one of the most popular marts: the "ensembl"" mart. ```{r} head(listMarts()) ensembl <- useEnsembl("ensembl") ensembl ``` Each 'mart' can contain datasets for multiple different things. So the next step is that you need to decide on a dataset. Once you have chosen one, you will need to specify that dataset using the dataset argument when you call the useEnsembl() constructor method. Here we will point to the dataset for humans. ```{r} head(listDatasets(ensembl)) ensembl <- useEnsembl("ensembl",dataset="hsapiens_gene_ensembl") ensembl ``` Next we need to think about attributes, values and filters. Lets start with attributes. You can get a listing of the different kinds of attributes from biomaRt buy using the listAttributes method: ```{r} head(listAttributes(ensembl)) ``` And you can see what the values for a particular attribute are by using the getBM method: ```{r} head(getBM(attributes="chromosome_name", mart=ensembl)) ``` Attributes are the things that you can have returned from biomaRt. They are analogous to what you get when you use the columns method with other objects. In the biomaRt package, filters are things that can be used with values to restrict or choose what comes back. The 'values' here are treated as keys that you are passing in and which you would like to know more information about. In contrast, the filter represents the kind of key that you are searching for. So for example, you might choose a filter name of "chromosome_name" to go with specific value of "1". Together these two argument values would request whatever attributes matched things on the 1st chromosome. And just as there here is an accessor for attributes, there is also an accessor for filters: ```{r} head(listFilters(ensembl)) ``` So now you know about attributes, values and filters, you can call the getBM method to put it all together and request specific data from the mart. So for example, the following requests gene symbols and entrez gene IDs that are found on chromosome 1 of flies: ```{r} res <- getBM(attributes=c("hgnc_symbol", "entrezgene_id"), filters = "chromosome_name", values = "1", mart = ensembl) head(res) ``` Of course you may have noticed that a lot of the arguments for getBM are very similar to what you do when you call shsapienselect. So if it's your preference you can now also use the standard select methods with mart objects. ```{r} head(columns(ensembl)) ``` ## 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 ]
# 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. ```{r,eval=FALSE} 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. # 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](http://bioconductor.org/) which contains many current walkthroughs and vignettes. Also visit the [support site](https://support.bioconductor.org/) where you can ask questions and engage in discussions. # sessionInfo() Package versions used in this tutorial: ```{r} sessionInfo() ``` # 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. # References 1. Wolfgang Huber, Vincent J Carey, Robert Gentleman, Simon Anders, Marc Carlson, Benilton S Carvalho, Hector Corrada Bravo, Sean Davis, Laurent Gatto, Thomas Girke, Raphael Gottardo, Florian Hahne, Kasper D Hansen, Rafael A Irizarry, Michael Lawrence, Michael I Love, James MacDonald, Valerie Obenchain, Andrzej K Oleś, Hervé Pagès, Alejandro Reyes, Paul Shannon, Gordon K Smyth, Dan Tenenbaum, Levi Waldron & Martin Morgan (2015) Orchestrating high-throughput genomic analysis with Bioconductor Nature Methods 12:115-121 2. Pages H, Carlson M, Falcon S and Li N. AnnotationDbi: Annotation Database Interface. R package version 1.30.0. 3. M. Carlson, H. Pages, P. Aboyoun, S. Falcon, M. Morgan, D. Sarkar, M. Lawrence GenomicFeatures: Tools for making and manipulating transcript centric annotations version 1.19.38. 4. Lawrence M, Huber W, Pagès H, Aboyoun P, Carlson M, Gentleman R, Morgan M and Carey V (2013). Software for Computing and Annotating Genomic Ranges. PLoS Computational Biology, 9. http://dx.doi.org/10.1371/journal.pcbi.1003118, http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003118 5. Steffen Durinck, Wolfgang Huber biomaRt: Interface to BioMart databases (e.g. Ensembl, COSMIC ,Wormbase and Gramene) version 2.23.5. 6. Durinck S, Spellman P, Birney E and Huber W (2009). Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nature Protocols, 4, pp. 1184-1191. 7. Durinck S, Moreau Y, Kasprzyk A, Davis S, De Moor B, Brazma A and Huber W (2005). BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis. Bioinformatics, 21, pp. 3439-3440. 8. Morgan M, Carlson M, Tenenbaum D and Arora S. AnnotationHub: Client to access AnnotationHub resources. R package version 2.0.1. 9. Carlson M, Pages H, Morgan M and Obenchain V. OrganismDbi: Software to enable the smooth interfacing of different database packages. R package version 1.10.0. 10. Pages H. BSgenome: Infrastructure for Biostrings-based genome data packages. R package version 1.36.0. 11. Pages H, Aboyoun P, Gentleman R and DebRoy S. Biostrings: String objects representing biological sequences, and matching algorithms. R package version 2.36.0. 12. Carlson M, and Pages H. AnnotationForge: Code for Building Annotation Database Packages. R package version 1.10.0. # Answers for exercises ## Exercise 1: The 1st thing you need to do is look for thing from UCSC ```{r} ahs <- query(ah, "UCSC") ``` Then you can look for Genome values that match 'hg19' and a species that matches 'Homo sapiens'. ```{r} ahs <- subset(ahs, ahs$genome=='hg19') length(ahs) ahs <- subset(ahs, ahs$species=='Homo sapiens') length(ahs) ``` You might notice that the last two filtering steps are redundant (IOW doing the 1st of them is the same as doing both of them.) If this were not the case, we might suspect that there was a problem with the metadata. ## 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." ```{r} ahs <- query(ah, 'oreganno') ahs ahs[1] oreg <- ahs[['AH5087']] oreg ``` ## Exercise 3: ```{r} keys <- "MSX2" columns <- c("ENTREZID", "CHR") select(org.Hs.eg.db, keys, columns, keytype="SYMBOL") ``` ## Exercise 4: ```{r} ## 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") length(egr$ENTREZID) length(unique(egr$ENTREZID)) ## VS: length(egr$SYMBOL) length(unique(egr$SYMBOL)) ## 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") ``` ## Exercise 5: So to retrieve this information using select you need to do it like this: ```{r} res1 <- select(TxDb.Hsapiens.UCSC.hg19.knownGene, keys(TxDb.Hsapiens.UCSC.hg19.knownGene, keytype="TXID"), columns=c("GENEID","TXNAME","TXCHROM"), keytype="TXID") head(res1) ``` And to do it using transcripts you do it like this: ```{r} res2 <- transcripts(TxDb.Hsapiens.UCSC.hg19.knownGene, columns = c("gene_id","tx_name")) head(res2) ``` Notice that in the 2nd case we don't have to ask for the chromosome, as transcripts() returns a GRanges object, so the chromosome will automatically be returned as part of the object. ## Exercise 6: ```{r} 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. ## Exercise 7: ```{r eval=FALSE} 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: ```{r} res2 <- transcripts(Homo.sapiens, columns="SYMBOL") head(res2) ``` ## Exercise 8: ```{r} columns(Homo.sapiens) columns(org.Hs.eg.db) columns(TxDb.Hsapiens.UCSC.hg19.knownGene) ## You might also want to look at this: transcripts(Homo.sapiens, columns=c("SYMBOL","CHRLOC")) ``` The key difference is that the TXSTART refers to the start of a transcript and originates in the TxDb object from the TxDb.Hsapiens.UCSC.hg19.knownGene package, while the CHRLOC refers to the same thing but originates in the OrgDb object from the org.Hs.eg.db package. The point of origin is significant because the TxDb object represents a transcriptome from UCSC and the OrgDb is primarily gene centric data that originates at NCBI. The upshot is that CHRLOC will not have as many regions represented as TXSTART, since there has to be an official gene for there to even be a record. The CHRLOC data is also locked in for org.Hs.eg.db as data for hg19, whereas you can swap in a different TxDb object to match the genome you are using to make it hg18 etc. For these reasons, we strongly recommend using TXSTART instead of CHRLOC. Howeverm CHRLOC still remains in the org packages for historical reasons. ## Exercise 9: To find the keys that match, make use of the pattern and column arguments. ```{r} xk = head(keys(Homo.sapiens, keytype="ENTREZID", pattern="X", column="SYMBOL")) xk ``` select verifies the results ```{r} select(Homo.sapiens, xk, "SYMBOL", "ENTREZID") ``` ## Exercise 10: ```{r eval=FALSE} ## Get the transcript ranges grouped by gene txby <- transcriptsBy(Homo.sapiens, by="gene") ## look up the entrez ID for the gene symbol 'PTEN' select(Homo.sapiens, keys='PTEN', columns='ENTREZID', keytype='SYMBOL') ## subset that genes transcripts geneOfInterest <- txby[["5728"]] ## extract the sequence res <- getSeq(Hsapiens, geneOfInterest) res ``` ## Exercise 11: ```{r} ensembl <- useEnsembl("ensembl",dataset="hsapiens_gene_ensembl") ids=c("1") getBM(attributes=c('go_id', 'entrezgene_id'), filters = 'entrezgene_id', values = ids, mart = ensembl) ``` ## Exercise 12: ```{r} ids=c("1") select(org.Hs.eg.db, keys=ids, columns="GO", keytype="ENTREZID") ``` When this exercise was written, there was a different number of GO terms returned from biomaRt than from org.Hs.eg.db. This may not always be true in the future though as both of these resources are updated. It is expected however that this web service, (which is updated continuously) will fall in and out of sync with the org.Hs.eg.db package (which is updated twice a year). This is an important difference as each approach has different advantages and disadvantages. The advantage to updating continuously is that you always have the very latest annotations which are frequently different for something like GO terms. The advantage to using a package is that the results are frozen to a release of Bioconductor. And this can help you to get the same answers that you get today (reproducibility), a few years from now.[ Back to top ]