rols 2.10.1
rols is a Bioconductor package and should hence be installed using the dedicated functionality
## try http:// if https:// URLs are not supported
if (!requireNamespace("BiocManager", quietly=TRUE))
install.packages("BiocManager")
BiocManager::install("rols")To get help, either post your question on the Bioconductor support site or open an issue on the rols github page.
The Ontology Lookup Service (OLS) [1, 2] is originally spin-off of the PRoteomics IDEntifications database (PRIDE) service, located at the EBI, and is now developed and maintained by the Samples, Phenotypes and Ontologies team at EMBL-EBI.
The OLS provides a REST interface to hundreds of ontologies from a single location with a unified output format. The rols package make this possible from within R. Do do so, it relies on the httr package to query the REST interface, and access and retrieve data.
There are 223 ontologies available in the OLS, listed in the table below. Their name is to be use to defined which ontology to query.
The rols package is build around a few classes that enable to query the OLS and retrieve, store and manipulate data. Each of these classes are described in more details in their respective manual pages. We start by loading the package.
library("rols")The Ontology and Ontologies classes can store information about
single of multiple ontologies. The latter can be easily subset using
[ and [[, as one would for lists.
ol <- Ontologies()
ol## Object of class 'Ontologies' with 223 entries
## AERO, BCGO ... REX, UBERONhead(olsNamespace(ol))## aero bcgo cco cdao ceph chebi
## "aero" "bcgo" "cco" "cdao" "ceph" "chebi"ol[["go"]]## Ontology: Gene Ontology (go)
## An ontology for describing the function of genes and gene products
## Loaded: 2018-12-31 Updated: 2019-01-04 Version: 2018-12-28
## 49769 terms 60 properties 0 individualsIt is also possible to initialise a single ontology
go <- Ontology("go")
go## Ontology: Gene Ontology (go)
## An ontology for describing the function of genes and gene products
## Loaded: 2018-12-31 Updated: 2019-01-04 Version: 2018-12-28
## 49769 terms 60 properties 0 individualsSingle ontology terms are stored in Term objects. When more terms
need to be manipulated, they are stored as Terms objects. It is easy
to obtain all terms of an ontology of interest, and the resulting
Terms object can be subset using [ and [[, as one would for
lists.
gotrms <- terms(go) ## or terms("go")
gotrms## Object of class 'Terms' with 49770 entries
## From the GO ontology
## GO:0005230, GO:0015276 ... GO:0032957, GO:0032942gotrms[1:10]## Object of class 'Terms' with 10 entries
## From the GO ontology
## GO:0005230, GO:0015276 ... GO:0001819, GO:0044831gotrms[["GO:0090575"]]## A Term from the GO ontology: GO:0090575
## Label: RNA polymerase II transcription factor complex
## A transcription factor complex that acts at a regulatory region of a
## gene transcribed by RNA polymerase II.It is also possible to initialise a single term
trm <- term(go, "GO:0090575")
termId(trm)## [1] "GO:0090575"termLabel(trm)## [1] "RNA polymerase II transcription factor complex"strwrap(termDesc(trm))## [1] "A transcription factor complex that acts at a regulatory region of a"
## [2] "gene transcribed by RNA polymerase II."It is then possible to extract the ancestors, descendants,
parents and children terms. Each of these functions return a
Terms object
parents(trm)## Object of class 'Terms' with 1 entries
## From the GO ontology
## GO:0044798children(trm)## Object of class 'Terms' with 41 entries
## From the GO ontology
## GO:0062071, GO:0008230 ... GO:0034718, GO:0030232Similarly, the partOf and derivesFrom functions return, for an
input term, the terms it is a part of and derived from.
Finally, a single term or terms object can be coerced to a
data.frame using as(x, "data.frame").
Properties (relationships) of single or multiple terms or complete
ontologies can be queries with the properties method, as briefly
illustrated below.
trm <- term("uberon", "UBERON:0002107")
trm## A Term from the UBERON ontology: UBERON:0002107
## Label: liver
## An exocrine gland which secretes bile and functions in metabolism of
## protein and carbohydrate and fat, synthesizes substances involved in
## the clotting of the blood, synthesizes vitamin A, detoxifies
## poisonous substances, stores glycogen, and breaks down worn-out
## erythrocytes[GO].p <- properties(trm)
p## Object of class 'Properties' with 112 entries
## From the UBERON ontology
## digestive system gland, abdomen element ... liver lobule, hepatobiliary systemp[[1]]## A Property from the UBERON ontology: UBERON:0006925
## Label: digestive system glandtermLabel(p[[1]])## [1] "digestive system gland"A researcher might be interested in the trans-Golgi network. Searching
the OLS is assured by the OlsSearch and olsSearch
classes/functions. The first step is to defined the search query with
OlsSearch, as shown below. This creates an search object of class
OlsSearch that stores the query and its parameters. In records the
number of requested results (default is 20) and the total number of
possible results (there are 15318 results across all
ontologies, in this case). At this stage, the results have not yet
been downloaded, as shown by the 0 responses.
OlsSearch(q = "trans-golgi network")## Object of class 'OlsSearch':
## query: trans-golgi network
## requested: 20 (out of 15318)
## response(s): 015318 results are probably too many to be
relevant. Below we show how to perform an exact search by setting
exact = TRUE, and limiting the search the the GO ontology by
specifying ontology = "GO", or doing both.
OlsSearch(q = "trans-golgi network", exact = TRUE)## Object of class 'OlsSearch':
## query: trans-golgi network
## requested: 20 (out of 8)
## response(s): 0OlsSearch(q = "trans-golgi network", ontology = "GO")## Object of class 'OlsSearch':
## ontolgy: GO
## query: trans-golgi network
## requested: 20 (out of 756)
## response(s): 0OlsSearch(q = "trans-golgi network", ontology = "GO", exact = TRUE)## Object of class 'OlsSearch':
## ontolgy: GO
## query: trans-golgi network
## requested: 20 (out of 1)
## response(s): 0One case set the rows argument to set the number of desired results.
OlsSearch(q = "trans-golgi network", ontology = "GO", rows = 200)## Object of class 'OlsSearch':
## ontolgy: GO
## query: trans-golgi network
## requested: 200 (out of 756)
## response(s): 0Alternatively, one can call the allRows function to request all results.
(tgnq <- OlsSearch(q = "trans-golgi network", ontology = "GO"))## Object of class 'OlsSearch':
## ontolgy: GO
## query: trans-golgi network
## requested: 20 (out of 756)
## response(s): 0(tgnq <- allRows(tgnq))## Object of class 'OlsSearch':
## ontolgy: GO
## query: trans-golgi network
## requested: 756 (out of 756)
## response(s): 0Let’s proceed with the exact search and retrieve the results. Even if
we request the default 20 results, only the 8 relevant
result will be retrieved. The olsSearch function updates the
previously created object (called qry below) by adding the results
to it.
qry <- OlsSearch(q = "trans-golgi network", exact = TRUE)
(qry <- olsSearch(qry))## Object of class 'OlsSearch':
## query: trans-golgi network
## requested: 20 (out of 8)
## response(s): 8We can now transform this search result object into a fully fledged
Terms object or a data.frame.
(qtrms <- as(qry, "Terms"))## Object of class 'Terms' with 8 entries
## From 8 ontologies
## NCIT:C33802, OMIT:0020822 ... GO:0005802, GO:0005802str(qdrf <- as(qry, "data.frame"))## 'data.frame': 8 obs. of 10 variables:
## $ id : chr "ncit:class:http://purl.obolibrary.org/obo/NCIT_C33802" "omit:class:http://purl.obolibrary.org/obo/OMIT_0020822" "go:class:http://purl.obolibrary.org/obo/GO_0005802" "cco:http://purl.obolibrary.org/obo/GO_0005802" ...
## $ iri : chr "http://purl.obolibrary.org/obo/NCIT_C33802" "http://purl.obolibrary.org/obo/OMIT_0020822" "http://purl.obolibrary.org/obo/GO_0005802" "http://purl.obolibrary.org/obo/GO_0005802" ...
## $ short_form : chr "NCIT_C33802" "OMIT_0020822" "GO_0005802" "GO_0005802" ...
## $ obo_id : chr "NCIT:C33802" "OMIT:0020822" "GO:0005802" "GO:0005802" ...
## $ label : chr "Trans-Golgi Network" "trans-Golgi Network" "trans-Golgi network" "trans-Golgi network" ...
## $ description :List of 8
## ..$ : chr "A network of membrane components where vesicles bud off the Golgi apparatus to bring proteins, membranes and ot"| __truncated__
## ..$ : NULL
## ..$ : chr "The network of interconnected tubular and cisternal structures located within the Golgi apparatus on the side d"| __truncated__
## ..$ : chr "The network of interconnected tubular and cisternal structures located within the Golgi apparatus on the side d"| __truncated__
## ..$ : chr "The network of interconnected tubular and cisternal structures located within the Golgi apparatus on the side d"| __truncated__
## ..$ : chr "The network of interconnected tubular and cisternal structures located within the Golgi apparatus on the side d"| __truncated__
## ..$ : chr "The network of interconnected tubular and cisternal structures located within the Golgi apparatus on the side d"| __truncated__
## ..$ : chr "The network of interconnected tubular and cisternal structures located within the Golgi apparatus on the side d"| __truncated__
## $ ontology_name : chr "ncit" "omit" "go" "cco" ...
## $ ontology_prefix : chr "NCIT" "OMIT" "GO" "CCO" ...
## $ type : chr "class" "class" "class" "class" ...
## $ is_defining_ontology: logi TRUE TRUE TRUE FALSE FALSE FALSE ...In this case, we can see that we actually retrieve the same term used across different ontologies. In such cases, it might be useful to keep only non-redundant term instances. Here, this would have been equivalent to searching the ncit, omit, go ontology
qtrms <- unique(qtrms)
termOntology(qtrms)## NCIT:C33802 OMIT:0020822 GO:0005802
## "ncit" "omit" "go"termNamespace(qtrms)## $`NCIT:C33802`
## NULL
##
## $`OMIT:0020822`
## NULL
##
## $`GO:0005802`
## [1] "cellular_component"Below, we execute the same query using the GO.db package.
library("GO.db")
GOTERM[["GO:0005802"]]## GOID: GO:0005802
## Term: trans-Golgi network
## Ontology: CC
## Definition: The network of interconnected tubular and cisternal
## structures located within the Golgi apparatus on the side distal
## to the endoplasmic reticulum, from which secretory vesicles
## emerge. The trans-Golgi network is important in the later stages
## of protein secretion where it is thought to play a key role in
## the sorting and targeting of secreted proteins to the correct
## destination.
## Synonym: Golgi trans face
## Synonym: Golgi trans-face
## Synonym: late Golgi
## Synonym: maturing face
## Synonym: TGN
## Synonym: trans face
## Synonym: trans Golgi networkIt is possible to observe different results with rols and GO.db, as a result of the different ways they access the data. rols or biomaRt perform direct online queries, while GO.db and other annotation packages use database snapshot that are updated every release.
Both approaches have advantages. While online queries allow to obtain
the latest up-to-date information, such approaches rely on network
availability and quality. If reproducibility is a major issue, the
version of the database to be queried can easily be controlled with
off-line approaches. In the case of rols, although the
load date of a specific ontology can be queried with olsVersion, it
is not possible to query a specific version of an ontology.
rols 2.0 has substantially changed. While the table
below shows some correspondence between the old and new interface,
this is not always the case. The new interface relies on the
Ontology/Ontologies, Term/Terms and OlsSearch classes, that
need to be instantiated and can then be queried, as described above.
| version < 1.99 | version >= 1.99 |
|---|---|
ontologyLoadDate |
olsLoaded and olsUpdated |
ontologyNames |
Ontologies |
olsVersion |
olsVersion |
allIds |
terms |
isIdObsolete |
isObsolete |
rootId |
olsRoot |
olsQuery |
OlsSearch and olsSearch |
Not all functionality is currently available. If there is anything that you need but not available in the new version, please contact the maintained by opening an issue on the package development site.
The CVParam class is used to handle controlled vocabulary. It can be
used for user-defined parameters
CVParam(name = "A user param", value = "the value")## [, , A user param, the value]or official controlled vocabulary (which triggers a query to the OLS service)
CVParam(label = "MS", accession = "MS:1000073")## [MS, MS:1000073, electrospray ionization, ]CVParam(label = "MS", name ="electrospray ionization")## [MS, MS:1000073, electrospray ionization, ]CVParam(label = "MS", name ="ESI") ## using a synonym## [MS, MS:1000073, ESI, ]See ?CVParam for more details and examples.
## R version 3.5.2 (2018-12-20)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.5 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.8-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.8-bioc/R/lib/libRlapack.so
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## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] DT_0.5 rols_2.10.1 GO.db_3.7.0
## [4] AnnotationDbi_1.44.0 IRanges_2.16.0 S4Vectors_0.20.1
## [7] Biobase_2.42.0 BiocGenerics_0.28.0 BiocStyle_2.10.0
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## loaded via a namespace (and not attached):
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## [4] BiocManager_1.30.4 prettyunits_1.0.2 tools_3.5.2
## [7] progress_1.2.0 digest_0.6.18 bit_1.1-14
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