OrganismDbi: A meta framework for Annotation Packages
October 24, 2023
Package
OrganismDbi 1.44.0
Contents
OrganismDbi is a software package that helps tie together different annotation resources. It is expected that users may have previously made or seen packages like org.Hs.eg.db and TxDb.Hsapiens.UCSC.hg19.knownGene. Packages like these two are very different and contain very different kinds of information, but are still about the same organism: Homo sapiens. The OrganismDbi package allows us to combine resources like these together into a single package resource, which can represent ALL of these resources at the same time. An example of this is the Homo.sapiens package, which combines access to the two resources above along with others.
This is made possible because the packages that are represented by Homo.sapiens are related to each other via foreign keys.
Figure 1: Relationships between Annotation packages
1 Getting started with OrganismDbi
Usage of a package like this has been deliberately kept very simple. The methods
supported are the same ones that work for all the packages based on
AnnotationDb objects. The methods that can be applied to these new packages
are columns, keys, keytypes and select.
So to learn which kinds of data can be retrieved from a package like this we
would simply load the package and then call the columns method.
library(Homo.sapiens)
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"To learn which of those kinds of data can be used as keys to extract data, we
use the keytypes method.
keytypes(Homo.sapiens)## [1] "ACCNUM" "ALIAS" "CDSID" "CDSNAME" "DEFINITION"
## [6] "ENSEMBL" "ENSEMBLPROT" "ENSEMBLTRANS" "ENTREZID" "ENZYME"
## [11] "EVIDENCE" "EVIDENCEALL" "EXONID" "EXONNAME" "GENEID"
## [16] "GENENAME" "GENETYPE" "GO" "GOALL" "GOID"
## [21] "IPI" "MAP" "OMIM" "ONTOLOGY" "ONTOLOGYALL"
## [26] "PATH" "PFAM" "PMID" "PROSITE" "REFSEQ"
## [31] "SYMBOL" "TERM" "TXID" "TXNAME" "UCSCKG"
## [36] "UNIPROT"To extract specific keys, we need to use the keys method, and also provide it
a legitimate keytype:
head(keys(Homo.sapiens, keytype = "ENTREZID"))## [1] "1" "2" "3" "9" "10" "11"And to extract data, we can use the select method. The select method depends
on the values from the previous three methods to specify what it will extract.
Here is an example that will extract, UCSC transcript names, and gene symbols
using Entrez Gene IDs as keys.
k <- head(keys(Homo.sapiens, keytype = "ENTREZID"), n = 3)
select(
Homo.sapiens,
keys = k,
columns = c("TXNAME", "SYMBOL"),
keytype = "ENTREZID"
)## ENTREZID SYMBOL TXNAME
## 1 1 A1BG uc002qsd.4
## 2 1 A1BG uc002qsf.2
## 3 2 A2M uc001qvk.1
## 4 2 A2M uc009zgk.1
## 5 3 A2MP1 uc021qum.1In addition to select, some of the more popular range based methods have also
been updated to work with an AnnotationDb object. So for example you could
extract transcript information like this:
transcripts(Homo.sapiens, columns = c("TXNAME", "SYMBOL"))## GRanges object with 82960 ranges and 2 metadata columns:
## seqnames ranges strand | TXNAME SYMBOL
## <Rle> <IRanges> <Rle> | <CharacterList> <CharacterList>
## [1] chr1 11874-14409 + | uc001aaa.3 DDX11L1
## [2] chr1 11874-14409 + | uc010nxq.1 DDX11L1
## [3] chr1 11874-14409 + | uc010nxr.1 DDX11L1
## [4] chr1 69091-70008 + | uc001aal.1 OR4F5
## [5] chr1 321084-321115 + | uc001aaq.2 <NA>
## ... ... ... ... . ... ...
## [82956] chrUn_gl000237 1-2686 - | uc011mgu.1 <NA>
## [82957] chrUn_gl000241 20433-36875 - | uc011mgv.2 <NA>
## [82958] chrUn_gl000243 11501-11530 + | uc011mgw.1 <NA>
## [82959] chrUn_gl000243 13608-13637 + | uc022brq.1 <NA>
## [82960] chrUn_gl000247 5787-5816 - | uc022brr.1 <NA>
## -------
## seqinfo: 93 sequences (1 circular) from hg19 genomeAnd the GRanges object that would be returned would have the information that
you specified in the columns argument. You could also have used the exons or
cds methods in this way.
The transcriptsBy,exonsBy and cdsBy methods are also supported. For
example:
transcriptsBy(Homo.sapiens,
by = "gene",
columns = c("TXNAME", "SYMBOL"))## GRangesList object of length 23459:
## $`1`
## GRanges object with 2 ranges and 3 metadata columns:
## seqnames ranges strand | tx_name TXNAME
## <Rle> <IRanges> <Rle> | <character> <CharacterList>
## [1] chr19 58858172-58864865 - | uc002qsd.4 uc002qsd.4
## [2] chr19 58859832-58874214 - | uc002qsf.2 uc002qsf.2
## SYMBOL
## <CharacterList>
## [1] A1BG
## [2] A1BG
## -------
## seqinfo: 93 sequences (1 circular) from hg19 genome
##
## $`10`
## GRanges object with 1 range and 3 metadata columns:
## seqnames ranges strand | tx_name TXNAME
## <Rle> <IRanges> <Rle> | <character> <CharacterList>
## [1] chr8 18248755-18258723 + | uc003wyw.1 uc003wyw.1
## SYMBOL
## <CharacterList>
## [1] NAT2
## -------
## seqinfo: 93 sequences (1 circular) from hg19 genome
##
## $`100`
## GRanges object with 1 range and 3 metadata columns:
## seqnames ranges strand | tx_name TXNAME
## <Rle> <IRanges> <Rle> | <character> <CharacterList>
## [1] chr20 43248163-43280376 - | uc002xmj.3 uc002xmj.3
## SYMBOL
## <CharacterList>
## [1] ADA
## -------
## seqinfo: 93 sequences (1 circular) from hg19 genome
##
## ...
## <23456 more elements>2 Making your own OrganismDbi packages
So in the preceding section you can see that using an OrganismDbi
package behaves very similarly to how you might use a TxDb or an OrgDb
package. The same methods are defined, and they behave similarly except that
they now have access to much more data than before. But before you make your own
OrganismDbi package you need to understand that there are few
logical limitations for what can be included in this kind of package.
The 1st limitation is that all the annotation resources in question must have implemented the four methods described in the preceding section (
columns,keys,keytypesandselect).The 2nd limitation is that you cannot have more than one example of each field that can be retrieved from each type of package that is included. So basically, all values returned by
columnsmust be unique across ALL of the supporting packages.The 3rd limitation is that you cannot have more than one example of each object type represented. So you cannot have two org packages since that would introduce two
OrgDbobjects.And the 4th limitation is that you cannot have cycles in the graph. What this means is that there will be a graph that represents the relationships between the different object types in your package, and this graph must not present more than one pathway between any two nodes/objects. This limitation means that you can choose one foreign key relationship to connect any two packages in your graph.
With these limitations in mind, lets set up an example. Lets show how we could make Homo.sapiens, such that it allowed access to org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene and GO.db.
The 1st thing that we need to do is set up a list that expresses the way that
these different packages relate to each other. To do this, we make a list that
contains short two element long character vectors. Each character vector
represents one relationship between a pair of packages. The names of the vectors
are the package names and the values are the foreign keys. Please note that the
foreign key values in these vectors are the same strings that are returned by
the columns method for the individual packages. Here is an example that shows
how GO.db, org.Hs.eg.db and
TxDb.Hsapiens.UCSC.hg19.knownGene all relate to each
other.
gd <- list(
join1 = c(GO.db = "GOID", org.Hs.eg.db = "GO"),
join2 = c(
org.Hs.eg.db = "ENTREZID",
TxDb.Hsapiens.UCSC.hg19.knownGene = "GENEID"
)
)So this data.frame indicates both which packages are connected to each other,
and also what these connections are using for foreign keys. Once this is
finished, we just have to call the makeOrganismPackage function
to finish the task.
destination <- tempfile()
dir.create(destination)
makeOrganismPackage(
pkgname = "Homo.sapiens",
graphData = gd,
organism = "Homo sapiens",
version = "1.0.0",
maintainer = "Package Maintainer<maintainer@somewhere.org>",
author = "Some Body",
destDir = destination,
license = "Artistic-2.0"
)makeOrganismPackage will then generate a lightweight package
that you can install. This package will not contain all the data that it refers
to, but will instead depend on the packages that were referred to in the
data.frame. Because the end result will be a package that treats all the data
mapped together as a single source, the user is encouraged to take extra care to
ensure that the different packages used are from the same build etc.