This Biological Entity Dictionary (BED) has been developed to address three main challenges. The first one is related to the completeness of identifier mappings. Indeed, direct mapping information provided by the different systems are not always complete and can be enriched by mappings provided by other resources. More interestingly, direct mappings not identified by any of these resources can be indirectly inferred by using mappings to a third reference. For example, many human Ensembl gene ID are not directly mapped to any Entrez gene ID but such mappings can be inferred using respective mappings to HGNC ID. The second challenge is related to the mapping of deprecated identifiers. Indeed, entity identifiers can change from one resource release to another. The identifier history is provided by some resources, such as Ensembl or the NCBI, but it is generally not used by mapping tools. The third challenge is related to the automation of the mapping process according to the relationships between the biological entities of interest. Indeed, mapping between gene and protein ID scopes should not be done the same way than between two scopes regarding gene ID. Also, converting identifiers from different organisms should be possible using gene orthologs information.
This document shows how to use the BED (Biological Entity Dictionary) R package to get and explore mapping between identifiers of biological entities (BE). This package provides a way to connect to a BED Neo4j database in which the relationships between the identifiers from different sources are recorded.
This package and the underlying research has been published in this peer reviewed article:
This BED package depends on the following packages available in the CRAN repository:
All these packages must be installed before installing BED.
devtools::install_github("patzaw/BED")If you get an error like the following…
Error: package or namespace load failed for ‘BED’:
.onLoad failed in loadNamespace() for 'BED', details:
call: connections[[connection]][["cache"]]
error: subscript out of bounds… remove the BED folder located here:
file.exists(file.path(Sys.getenv("HOME"), "R", "BED"))Before using BED, the connection needs to be established with the underlying Neo4j DB. url, username and password should be adapted.
library(BED)connectToBed(url="localhost:5454", remember=FALSE, useCache=FALSE)The remember parameter can be set to TRUE in order to save connection information that will be automatically used the next time the connectToBed() function is called or the next time the BED library is loaded. By default, this parameter is set to FALSE to comply with CRAN policies. Saved connection can be managed with the lsBedConnections() and the forgetBedConnection() functions.
The useCache parameter is by default set to FALSE to comply with CRAN policies. However, it is recommended to set it to TRUE to improve the speed of recurrent queries: the results of some large queries are saved locally in a file.
The connection can be checked the following way.
checkBedConn(verbose=TRUE)## http://localhost:5454## BED## UCB-Human## 2020.12.11## Cache ON## [1] TRUE
## attr(,"dbVersion")
## name instance version
## 1 BED UCB-Human 2020.12.11If the verbose parameter is set to TRUE, the URL and the content version are displayed as messages.
lsBedConnections()## [[1]]
## [[1]]$url
## [1] "localhost:5454"
##
## [[1]]$username
## [1] NA
##
## [[1]]$password
## [1] NA
##
## [[1]]$cache
## [1] TRUE
##
## [[1]]$name
## [1] "BED"
##
## [[1]]$instance
## [1] "UCB-Human"
##
## [[1]]$version
## [1] "2020.05.03"
##
##
## [[2]]
## [[2]]$url
## [1] "localhost:5410"
##
## [[2]]$username
## [1] NA
##
## [[2]]$password
## [1] NA
##
## [[2]]$cache
## [1] FALSE
##
## [[2]]$name
## [1] "BED"
##
## [[2]]$instance
## [1] "UCB-Human"
##
## [[2]]$version
## [1] "2020.08.27"The connection param of the connectToBed function can be used to connect to a saved connection other than the last one.
The BED underlying data model can be shown at any time using the following command.
showBedDataModel()Cypher queries can be run directly on the Neo4j database using the cypher function from the neo2R package through the bedCall function.
results <- bedCall(
cypher,
query=prepCql(
'MATCH (n:BEID)',
'WHERE n.value IN $values',
'RETURN DISTINCT n.value AS value, labels(n), n.database'
),
parameters=list(values=c("10", "100"))
)
results## value labels(n) n.database
## 1 10 BEID || GeneID EntrezGene
## 2 10 BEID || ObjectID MetaBase_object
## 3 100 BEID || GeneID EntrezGene
## 4 100 BEID || GeneID HGNC
## 5 100 BEID || ObjectID MetaBase_objectMany functions are provided within the package to build your own BED database instance. These functions are not exported in order to avoid their use when interacting with BED normally. Information about how to get an instance of the BED neo4j database is provided here:
It can be adapted to user needs.
This part is relevant if the useCache parameter is set to TRUE when calling connectToBed().
Functions of the BED package used to retrieve thousands of identifiers can take some time (generally a few seconds) before returning a result. Thus for this kind of query, the query is run for all the relevant ID in the DB and thanks to a cache system implemented in the package same queries with different filters should be much faster the following times.
By default the cache is flushed when the system detect inconsistencies with the BED database. However, it can also be manualy flushed if needed using the clearBedCache() function.
Queries already in cache can be listed using the lsBedCache() function which also return the occupied disk space.
BED is organized around the central concept of Biological Entity (BE). All supported types of BE can be listed.
listBe()## [1] "Gene" "Transcript" "Peptide" "Object"These BE are organized according to how they are related to each other. For example a Gene is_expressed_as a Transcript. This organization allows to find the first upstream BE common to a set of BE.
firstCommonUpstreamBe(c("Object", "Transcript"))## [1] "Gene"firstCommonUpstreamBe(c("Peptide", "Transcript"))## [1] "Transcript"Several organims can be supported by the BED underlying database. They can be listed the following way.
listOrganisms()## [1] "Danio rerio" "Homo sapiens" "Sus scrofa"
## [4] "Mus musculus" "Rattus norvegicus"Common names are also supported and the corresponding taxonomic identifiers can be retrieved. Conversely the organism names corresponding to a taxonomic ID can be listed.
getOrgNames(getTaxId("human"))## taxID name nameClass
## 1 9606 Homo sapiens Linnaeus, 1758 authority
## 2 9606 man common name
## 3 9606 human genbank common name
## 4 9606 Homo sapiens scientific nameThe main aim of BED is to allow the mapping of identifiers from different sources such as Ensembl or Entrez. Supported sources can be listed the following way for each supported organism.
listBeIdSources(be="Transcript", organism="human")## database nbBe nbId be
## 1 BEDTech_transcript 109339 109339 Transcript
## 2 Ens_transcript 253687 263044 Transcript
## 3 RefSeq 157876 167952 TranscriptThe database gathering the largest number of BE of specific type can also be identified.
largestBeSource(be="Transcript", organism="human", restricted=TRUE)## [1] "Ens_transcript"Finally, the getAllBeIdSources() function returns all the source databases of BE identifiers whatever the BE type.
BED also supports experimental platforms and provides mapping betweens probes and BE identifiers (BEID).
The supported platforms can be listed the following way. The getTargetedBe() function returns the type of BE on which a specific platform focus.
head(listPlatforms())## name description focus
## GPL6101 GPL6101 Illumina ratRef-12 v1.0 expression beadchip Gene
## GPL6887 GPL6887 Illumina MouseWG-6 v2.0 expression beadchip Gene
## GPL6947 GPL6947 Illumina HumanHT-12 V3.0 expression beadchip Gene
## GPL10558 GPL10558 Illumina HumanHT-12 V4.0 expression beadchip Gene
## GPL1355 GPL1355 [Rat230_2] Affymetrix Rat Genome 230 2.0 Array Gene
## GPL1261 GPL1261 [Mouse430_2] Affymetrix Mouse Genome 430 2.0 Array GenegetTargetedBe("GPL570")## [1] "Gene"All identifiers of an organism BEs from one source can be retrieved.
beids <- getBeIds(
be="Gene", source="EntrezGene", organism="human",
restricted=FALSE
)
dim(beids)## [1] 83367 5head(beids)## id preferred Gene db.version db.deprecated
## 1 504189 FALSE 1042307 20201211 FALSE
## 2 504190 FALSE 1042308 20201211 FALSE
## 3 4535 FALSE 1042682 20201211 FALSE
## 4 4536 FALSE 1042686 20201211 FALSE
## 5 100422875 TRUE 1118339 20201211 FALSE
## 6 10741 TRUE 1118341 20201211 FALSEThe first column, id, corresponds to the identifiers of the BE in the source. The column named according to the BE type (in this case Gene) corresponds to the internal identifier of the related BE. BE CAREFUL, THIS INTERNAL ID IS NOT STABLE AND CANNOT BE USED AS A REFERENCE. This internal identifier is useful to identify BEIDS corresponding to the same BE. The following code can be used to have an overview of such redundancy.
sort(table(table(beids$Gene)), decreasing = TRUE)##
## 1 2 3 4 5 6 7 8 9 10 11 12 13
## 47636 9072 3047 1008 385 151 89 44 24 11 10 6 2
## 14 16 30
## 2 1 1ambId <- sum(table(table(beids$Gene)[which(table(beids$Gene)>=10)]))In the example above we can see that most of Gene BE are identified by only one EntrezGene ID. However many of them are identified by two or more ID; 33 BE are even identified by 10 or more EntrezGeneID. In this case, most of these redundancies come from ID history extracted from Entrez. Legacy ID can be excluded from the retrieved ID by setting the restricted parameter to TRUE.
beids <- getBeIds(
be="Gene", source="EntrezGene", organism="human",
restricted = TRUE
)
dim(beids)## [1] 61670 5The same code as above can be used to identify remaining redundancies.
sort(table(table(beids$Gene)), decreasing = TRUE)##
## 1 2 3 4
## 61314 170 4 1In the example above we can see that allmost all Gene BE are identified by only one EntrezGene ID. However some of them are identified by two or more ID. This result comes from how the BED database is constructed according to the ID mapping provided by the different source databases. The graph below shows how the mapping was done for such a BE with redundant EntrezGene IDs.
This issue has been mainly solved by not taking into account ambigous mappings between NCBI Entrez gene identifiers and Ensembl gene identifier provided by Ensembl. It has been achieved using the cleanDubiousXRef() function from the 2019.10.11 version of the BED-UCB-Human database.
The way the ID correspondances are reported in the different source databases leads to this mapping ambiguity which has to be taken into account when comparing identifiers from different databases.
The getBeIds() returns other columns providing additional information about the id. The same function can be used to retrieved symbols or probe identifiers.
The BED database is constructed according to the relationships between identifiers provided by the different sources. Biological entities (BE) are identified as clusters of identifiers which correspond to each other directly or indirectly (corresponds_to relationship). Because of this design a BE can be identified by multiple identifiers (BEID) from the same database as shown above. These BEID are often related to alternate version of an entity.
For example, Ensembl provides different version (alternative sequences) of some chromosomes parts. And genes are also annotated on these alternative sequences. In Uniprot some unreviewed identifiers can correspond to reviewed proteins.
When available such kind of information is associated to an Attribute node through a has relationship providing the value of the attribute for the BEID. This information can also be used to define if a BEID is a preferred identifier for a BE.
The example below shows the case of the MAPT gene annotated on different version of human chromosome 17.
The origin of identifiers can be guessed as following.
oriId <- c(
"17237", "105886298", "76429", "80985", "230514", "66459",
"93696", "72514", "20352", "13347", "100462961", "100043346",
"12400", "106582", "19062", "245607", "79196", "16878", "320727",
"230649", "66880", "66245", "103742", "320145", "140795"
)
idOrigin <- guessIdScope(oriId)
print(idOrigin$be)## [1] "Gene"print(idOrigin$source)## [1] "EntrezGene"print(idOrigin$organism)## [1] "Mus musculus"The best guess is returned as a list but other possible origins are listed in the details attribute.
print(attr(idOrigin, "details"))## be source organism nb proportion
## 1 Gene EntrezGene Mus musculus 25 1.00
## 2 Gene HGNC Homo sapiens 3 0.12
## 3 Gene MGI Mus musculus 2 0.08If the origin of identifiers is already known, it can also be tested.
checkBeIds(ids=oriId, be="Gene", source="EntrezGene", organism="mouse")checkBeIds(ids=oriId, be="Gene", source="HGNC", organism="human")## Warning in checkBeIds(ids = oriId, be = "Gene", source = "HGNC", organism =
## "human"): Could not find 22 IDs among 25!Identifiers can be annotated with symbols and names according to available information. The following code returns the most relevant symbol and the most relevant name for each ID. Source URL can also be generated with the getBeIdURL() function.
toShow <- getBeIdDescription(
ids=oriId, be="Gene", source="EntrezGene", organism="mouse"
)
toShow$id <- paste0(
sprintf(
'<a href="%s" target="_blank">',
getBeIdURL(toShow$id, "EntrezGene")
),
toShow$id,
'<a>'
)
kable(toShow, escape=FALSE, row.names=FALSE)| id | symbol | name | preferred | db.version | db.deprecated |
|---|---|---|---|---|---|
| 17237 | Mgrn1 | mahogunin, ring finger 1 | TRUE | 20201211 | FALSE |
| 105886298 | Cmc4 | C-x(9)-C motif containing 4 | TRUE | 20201211 | FALSE |
| 76429 | Lhpp | phospholysine phosphohistidine inorganic pyrophosphate phosphatase | TRUE | 20201211 | FALSE |
| 80985 | Trim44 | tripartite motif-containing 44 | TRUE | 20201211 | FALSE |
| 230514 | Leprot | leptin receptor overlapping transcript | TRUE | 20201211 | FALSE |
| 66459 | Pyurf | Pigy upstream reading frame | TRUE | 20201211 | FALSE |
| 93696 | Chrac1 | chromatin accessibility complex 1 | TRUE | 20201211 | FALSE |
| 72514 | Fgfbp3 | fibroblast growth factor binding protein 3 | TRUE | 20201211 | FALSE |
| 20352 | Sema4b | sema domain, immunoglobulin domain (Ig), transmembrane domain (TM) and short cytoplasmic domain, (semaphorin) 4B | TRUE | 20201211 | FALSE |
| 13347 | Dffa | DNA fragmentation factor, alpha subunit | TRUE | 20201211 | FALSE |
| 100462961 | Gm16149 | predicted gene 16149 | TRUE | 20201211 | FALSE |
| 100043346 | Rpl10-ps3 | ribosomal protein L10, pseudogene 3 | TRUE | 20201211 | FALSE |
| 12400 | Cbfb | core binding factor beta | TRUE | 20201211 | FALSE |
| 106582 | Nrm | nurim (nuclear envelope membrane protein) | TRUE | 20201211 | FALSE |
| 19062 | Inpp5k | inositol polyphosphate 5-phosphatase K | TRUE | 20201211 | FALSE |
| 245607 | Gprasp2 | G protein-coupled receptor associated sorting protein 2 | TRUE | 20201211 | FALSE |
| 79196 | Osbpl5 | oxysterol binding protein-like 5 | TRUE | 20201211 | FALSE |
| 16878 | Lif | leukemia inhibitory factor | TRUE | 20201211 | FALSE |
| 320727 | Ipo8 | importin 8 | TRUE | 20201211 | FALSE |
| 230649 | Atpaf1 | ATP synthase mitochondrial F1 complex assembly factor 1 | TRUE | 20201211 | FALSE |
| 66880 | Rsrc1 | arginine/serine-rich coiled-coil 1 | TRUE | 20201211 | FALSE |
| 66245 | Hspbp1 | HSPA (heat shock 70kDa) binding protein, cytoplasmic cochaperone 1 | TRUE | 20201211 | FALSE |
| 103742 | Mien1 | migration and invasion enhancer 1 | TRUE | 20201211 | FALSE |
| 320145 | Sp8 | trans-acting transcription factor 8 | TRUE | 20201211 | FALSE |
| 140795 | P2ry14 | purinergic receptor P2Y, G-protein coupled, 14 | TRUE | 20201211 | FALSE |
All possible symbols and all possible names for each ID can also be retrieved using the following functions.
res <- getBeIdSymbols(
ids=oriId, be="Gene", source="EntrezGene", organism="mouse",
restricted=FALSE
)
head(res)## id symbol canonical direct preferred entity
## 1 12400 PEA2 FALSE TRUE TRUE 3255635
## 2 12400 AI893578 FALSE TRUE TRUE 3255635
## 3 12400 Peb FALSE TRUE TRUE 3255635
## 4 12400 Pebp2 FALSE TRUE TRUE 3255635
## 5 12400 Pebp FALSE TRUE TRUE 3255635
## 6 12400 PE FALSE TRUE TRUE 3255635res <- getBeIdNames(
ids=oriId, be="Gene", source="EntrezGene", organism="mouse",
restricted=FALSE
)
head(res)## id name
## 1 12400 core binding factor beta
## 2 80985 tripartite motif-containing 44
## 3 105886298 C-x(9)-C motif containing 4
## 4 66245 HSPA (heat shock 70kDa) binding protein, cytoplasmic cochaperone 1
## 5 16878 leukemia inhibitory factor
## 6 320727 importin 8
## direct preferred entity
## 1 TRUE TRUE 3255635
## 2 TRUE TRUE 3304364
## 3 TRUE TRUE 3261210
## 4 TRUE TRUE 3302739
## 5 TRUE TRUE 3283261
## 6 TRUE TRUE 3263995Also probes and some biological entities do not have directly associated symbols or names. These elements can also be annotated according to information related to relevant genes.
someProbes <- c(
"238834_at", "1569297_at", "213021_at", "225480_at",
"216016_at", "35685_at", "217969_at", "211359_s_at"
)
toShow <- getGeneDescription(
ids=someProbes, be="Probe", source="GPL570", organism="human"
)
kable(toShow, escape=FALSE, row.names=FALSE)| id | Ens_gene | symbol | name |
|---|---|---|---|
| 238834_at | ENSG00000140795 | MYLK3 | myosin light chain kinase 3 |
| 1569297_at | ENSG00000253595 || ENSG00000282594 | LINC01300 | long intergenic non-protein coding RNA 1300 |
| 213021_at | ENSG00000108587 | GOSR1 | golgi SNAP receptor complex member 1 |
| 225480_at | ENSG00000197982 | C1orf122 | chromosome 1 open reading frame 122 |
| 216016_at | ENSG00000162711 || LRG_197 | NLRP3 | NLR family pyrin domain containing 3 |
| 35685_at | ENSG00000204227 || ENSG00000206287 || ENSG00000226788 || ENSG00000228520 || ENSG00000231115 || ENSG00000235107 | RING1 | ring finger protein 1 |
| 217969_at | ENSG00000149823 | VPS51 | VPS51 subunit of GARP complex |
| 211359_s_at | ENSG00000112038 | OPRM1 | opioid receptor mu 1 |
The BED data model has beeing built to fulfill molecular biology processes:
These processes are described in different databases with different level of granularity. For exemple, Ensembl provides possible transcripts for each gene specifying which one of them is canonical.
The following functions are used to retrieve direct products or direct origins of molecular biology processes.
getDirectProduct("ENSG00000145335", process="is_expressed_as")## origin osource product psource canonical
## 1 ENSG00000145335 Ens_gene ENST00000506244 Ens_transcript FALSE
## 2 ENSG00000145335 Ens_gene ENST00000336904 Ens_transcript FALSE
## 3 ENSG00000145335 Ens_gene ENST00000618500 Ens_transcript FALSE
## 4 ENSG00000145335 Ens_gene ENST00000394989 Ens_transcript FALSE
## 5 ENSG00000145335 Ens_gene ENST00000420646 Ens_transcript FALSE
## 6 ENSG00000145335 Ens_gene ENST00000673902 Ens_transcript FALSE
## 7 ENSG00000145335 Ens_gene ENST00000505199 Ens_transcript FALSE
## 8 ENSG00000145335 Ens_gene ENST00000508895 Ens_transcript FALSE
## 9 ENSG00000145335 Ens_gene ENST00000673766 Ens_transcript FALSE
## 10 ENSG00000145335 Ens_gene ENST00000674129 Ens_transcript FALSE
## 11 ENSG00000145335 Ens_gene ENST00000506691 Ens_transcript FALSE
## 12 ENSG00000145335 Ens_gene ENST00000673718 Ens_transcript FALSE
## 13 ENSG00000145335 Ens_gene ENST00000611107 Ens_transcript FALSE
## 14 ENSG00000145335 Ens_gene ENST00000394986 Ens_transcript FALSE
## 15 ENSG00000145335 Ens_gene ENST00000394991 Ens_transcript FALSE
## 16 ENSG00000145335 Ens_gene ENST00000345009 Ens_transcript FALSE
## 17 ENSG00000145335 Ens_gene ENST00000502987 Ens_transcript FALSEgetDirectProduct("ENST00000336904", process="is_translated_in")## origin osource product psource canonical
## 1 ENST00000336904 Ens_transcript ENSP00000338345 Ens_translation FALSEgetDirectOrigin("NM_001146055", process="is_expressed_as")## origin osource product psource canonical
## 1 6622 EntrezGene NM_001146055 RefSeq FALSEres <- convBeIds(
ids=oriId,
from="Gene",
from.source="EntrezGene",
from.org="mouse",
to.source="Ens_gene",
restricted=TRUE,
prefFilter=TRUE
)
head(res)## from to to.preferred to.entity
## 1 320145 ENSMUSG00000048562 TRUE 3255121
## 2 12400 ENSMUSG00000031885 TRUE 3255635
## 3 245607 ENSMUSG00000072966 TRUE 3257601
## 4 66880 ENSMUSG00000034544 TRUE 3260960
## 5 105886298 ENSMUSG00000090110 TRUE 3261210
## 6 13347 ENSMUSG00000028974 TRUE 3263851res <- convBeIds(
ids=oriId,
from="Gene",
from.source="EntrezGene",
from.org="mouse",
to="Peptide",
to.source="Ens_translation",
restricted=TRUE,
prefFilter=TRUE
)
head(res)## from to to.preferred to.entity
## 9 12400 ENSMUSP00000059382 TRUE 4667812
## 1 12400 ENSMUSP00000105019 TRUE 4667812
## 5 12400 ENSMUSP00000105021 TRUE 4667812
## 13 12400 ENSMUSP00000105022 TRUE 4667812
## 17 20352 ENSMUSP00000032754 TRUE 4672235
## 19 20352 ENSMUSP00000145622 TRUE 4672235res <- convBeIds(
ids=oriId,
from="Gene",
from.source="EntrezGene",
from.org="mouse",
to="Peptide",
to.source="Ens_translation",
to.org="human",
restricted=TRUE,
prefFilter=TRUE
)
head(res)## from to to.preferred to.entity
## 119 106582 ENSP00000397892 TRUE 2710584
## 339 16878 ENSP00000249075 TRUE 2722908
## 340 16878 ENSP00000384450 TRUE 2722908
## 1 320145 ENSP00000483588 TRUE 2734840
## 400 100043346 ENSP00000298283 TRUE 2736015
## 329 17237 ENSP00000468171 TRUE 2742476List of identifiers can be converted the following way. Only converted IDs are returned in this case.
humanEnsPeptides <- convBeIdLists(
idList=list(a=oriId[1:5], b=oriId[-c(1:5)]),
from="Gene",
from.source="EntrezGene",
from.org="mouse",
to="Peptide",
to.source="Ens_translation",
to.org="human",
restricted=TRUE,
prefFilter=TRUE
)
unlist(lapply(humanEnsPeptides, length))## a b
## 21 169lapply(humanEnsPeptides, head)## $a
## [1] "ENSP00000468171" "ENSP00000443810" "ENSP00000467414" "ENSP00000262370"
## [5] "ENSP00000382487" "ENSP00000393311"
##
## $b
## [1] "ENSP00000397892" "ENSP00000249075" "ENSP00000384450" "ENSP00000483588"
## [5] "ENSP00000298283" "ENSP00000422573"IDs in data frames can also be converted.
toConv <- data.frame(a=1:25, b=runif(25))
rownames(toConv) <- oriId
res <- convDfBeIds(
df=toConv,
from="Gene",
from.source="EntrezGene",
from.org="mouse",
to.source="Ens_gene",
restricted=TRUE,
prefFilter=TRUE
)
head(res)## a b conv.from conv.to
## 1 1 0.62531880 17237 ENSMUSG00000022517
## 2 2 0.33244364 105886298 ENSMUSG00000090110
## 3 3 0.32854053 76429 ENSMUSG00000030946
## 4 4 0.60124900 80985 ENSMUSG00000027189
## 5 5 0.06685129 230514 ENSMUSG00000035212
## 6 6 0.74965986 66459 ENSMUSG00000043162Because the conversion process takes into account several resources, it might be useful to explore the path between two identifiers which have been mapped. This can be achieved by the exploreConvPath function.
The figure above shows how the ILMN_1220595 ProbeID, targeting the mouse NM_010552 transcript, can be associated to the Q16552 human protein ID in Uniprot.
Canonical and non-canonical symbols are associated to genes. In some cases the same symbol (canonical or not) can be associated to several genes. This can lead to ambiguous mapping. The strategy to apply for such mapping depends on the aim of the user and his knowledge about the origin of the symbols to consider.
The complete mapping between Ensembl gene identifiers and symbols is retrieved by using the getBeIDSymbolTable function.
compMap <- getBeIdSymbolTable(
be="Gene", source="Ens_gene", organism="rat",
restricted=FALSE
)
dim(compMap)## [1] 96928 6head(compMap)## id symbol canonical direct preferred entity
## 1 ENSRNOG00000051002 DMb FALSE TRUE TRUE 8052779
## 2 ENSRNOG00000051002 RT1.Mb FALSE TRUE TRUE 8052779
## 3 ENSRNOG00000051002 RT1.DMb FALSE TRUE TRUE 8052779
## 4 ENSRNOG00000051002 Hla-dmb FALSE TRUE TRUE 8052779
## 5 ENSRNOG00000051002 LOC108348139 TRUE TRUE TRUE 8052779
## 6 ENSRNOG00000049491 Hla-dmb FALSE TRUE TRUE 8052779The canonical field indicates if the symbol is canonical for the identifier. The direct field indicates if the symbol is directly associated to the identifier or indirectly through a relationship with another identifier.
As an example, let’s consider the “Snca” symbol in rat. As shown below, this symbol is associated to 2 genes; it is canonical for one gene and not for another. These 2 genes are also associated to other symbols.
sncaEid <- compMap[which(compMap$symbol=="Snca"),]
sncaEid## id symbol canonical direct preferred entity
## 20345 ENSRNOG00000029408 Snca FALSE TRUE TRUE 5166818
## 60335 ENSRNOG00000008656 Snca TRUE TRUE TRUE 5140165compMap[which(compMap$id %in% sncaEid$id),]## id symbol canonical direct preferred entity
## 20344 ENSRNOG00000029408 LOC317274 FALSE TRUE TRUE 5166818
## 20345 ENSRNOG00000029408 Snca FALSE TRUE TRUE 5166818
## 20346 ENSRNOG00000029408 Mageb16 TRUE TRUE TRUE 5166818
## 60334 ENSRNOG00000008656 MGC105443 FALSE TRUE TRUE 5140165
## 60335 ENSRNOG00000008656 Snca TRUE TRUE TRUE 5140165The getBeIdDescription function described before, reports only one symbol for each identifier. Canonical and direct symbols are prioritized.
getBeIdDescription(
sncaEid$id,
be="Gene", source="Ens_gene", organism="rat"
)## id symbol name preferred
## ENSRNOG00000029408 ENSRNOG00000029408 Mageb16 MAGE family member B16 TRUE
## ENSRNOG00000008656 ENSRNOG00000008656 Snca synuclein alpha TRUE
## db.version db.deprecated
## ENSRNOG00000029408 102 FALSE
## ENSRNOG00000008656 102 FALSEThe convBeIds works differently in order to provide a mapping as exhaustive as possible. If a symbol is associated to several input identifiers, non-canonical associations with this symbol are removed if a canonical association exists for any other identifier. This can lead to inconsistent results, depending on the user input, as show below.
convBeIds(
sncaEid$id[1],
from="Gene", from.source="Ens_gene", from.org="rat",
to.source="Symbol"
)## from to to.preferred to.entity
## 3 ENSRNOG00000029408 LOC317274 NA 5166818
## 2 ENSRNOG00000029408 Mageb16 NA 5166818
## 1 ENSRNOG00000029408 Snca NA 5166818convBeIds(
sncaEid$id[2],
from="Gene", from.source="Ens_gene", from.org="rat",
to.source="Symbol"
)## from to to.preferred to.entity
## 2 ENSRNOG00000008656 MGC105443 NA 5140165
## 1 ENSRNOG00000008656 Snca NA 5140165convBeIds(
sncaEid$id,
from="Gene", from.source="Ens_gene", from.org="rat",
to.source="Symbol"
)## from to to.preferred to.entity
## 2 ENSRNOG00000008656 MGC105443 NA 5140165
## 1 ENSRNOG00000008656 Snca NA 5140165
## 5 ENSRNOG00000029408 LOC317274 NA 5166818
## 4 ENSRNOG00000029408 Mageb16 NA 5166818In the example above, when the query is run for each identifier independently, the association to the “Snca” symbol is reported for both. However, when running the same query with the 2 identifiers at the same time, the “Snca” symbol is reported only for one gene corresponding to the canonical association. Finally, as show below, when running the query the other way, “Snca” is only associated to the gene for which it is the canonical symbol.
convBeIds(
"Snca",
from="Gene", from.source="Symbol", from.org="rat",
to.source="Ens_gene"
)## from to to.preferred to.entity
## 1 Snca ENSRNOG00000008656 TRUE 5140165Therefore, the user should chose the function to use with care when needing to convert from or to gene symbol.
IDs, symbols and names can be seeked without knowing the original biological entity or probe. Then the results can be converted to the context of interest.
searched <- searchBeid("sv2A")
toTake <- which(searched$organism=="Homo sapiens")[1]
relIds <- geneIDsToAllScopes(
geneids=searched$GeneID[toTake],
source=searched$Gene_source[toTake],
organism=searched$organism[toTake]
)A Shiny gadget integrating these two function has been developped and is also available as an Rstudio addins.
relIds <- findBeids()It relies on a Shiny module (beidsServer() and beidsUI() functions) made to facilitate the development of applications focused on biological entity related information. The code below shows a minimum example of such an application.
library(shiny)
library(BED)
library(DT)
ui <- fluidPage(
beidsUI("be"),
fluidRow(
column(
12,
tags$br(),
h3("Selected gene entities"),
DTOutput("result")
)
)
)
server <- function(input, output){
found <- beidsServer("be", toGene=TRUE, multiple=TRUE, tableHeight=250)
output$result <- renderDT({
req(found())
toRet <- found()
datatable(toRet, rownames=FALSE)
})
}
shinyApp(ui = ui, server = server)## R version 4.0.2 (2020-06-22)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Red Hat Enterprise Linux
##
## Matrix products: default
## BLAS/LAPACK: /usr/lib64/libopenblasp-r0.3.3.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 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
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] BED_1.4.3 visNetwork_2.0.9 neo2R_2.1.0 knitr_1.30
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.5 highr_0.8 pillar_1.4.7 compiler_4.0.2
## [5] later_1.1.0.1 base64enc_0.1-3 bitops_1.0-6 tools_4.0.2
## [9] digest_0.6.27 jsonlite_1.7.1 evaluate_0.14 lifecycle_0.2.0
## [13] tibble_3.0.4 pkgconfig_2.0.3 rlang_0.4.8 shiny_1.5.0
## [17] yaml_2.2.1 xfun_0.19 fastmap_1.0.1 dplyr_1.0.2
## [21] stringr_1.4.0 generics_0.1.0 vctrs_0.3.5 htmlwidgets_1.5.2
## [25] DT_0.16 tidyselect_1.1.0 glue_1.4.2 R6_2.5.0
## [29] rmarkdown_2.5 purrr_0.3.4 magrittr_2.0.1 promises_1.1.1
## [33] ellipsis_0.3.1 htmltools_0.5.0 mime_0.9 xtable_1.8-4
## [37] httpuv_1.5.4 stringi_1.5.3 miniUI_0.1.1.1 RCurl_1.98-1.2
## [41] crayon_1.3.4