patzaw/BED
Biological Entity Dictionary (BED)

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In patzaw/BED: Biological Entity Dictionary (BED) 

reComp <- FALSE
library(knitr)
library(RColorBrewer)
library(VennDiagram)
library(beeswarm)
library(gridExtra)
flog.threshold(futile.logger::ERROR, name = "VennDiagramLogger")
opts_chunk$set(
   include=FALSE,
   warning=FALSE, echo=FALSE, message=FALSE,
   concordance=TRUE
)
bigFormat <- function(x) {format(x, big.mark=",")}
library(BED)
connectToBed(url="localhost:5454", username="neo4j", password="1234")

## Preprocess bib file
bib <- readLines("BED-References.bib", warn=FALSE)
bib <- gsub("α", "$\\\\alpha$", bib)
write(bib, file="BED-References.bib")

\newcommand{\tm}{\textsuperscript{\textregistered}} \newcommand{\neo}{Neo4j\tm{}} \newcommand{\cypher}{Cypher\tm{}} \newcommand{\docker}{Docker\tm{}} \newcommand{\metabase}{MetaBase\tm{}}

Introduction

Since the advent of genome sequencing projects, many technologies have been developed to get access to different molecular information on a large scale and with high throughput. DNA micro-arrays are probably the archetype of such technology because of their historical impact on gathering data related to nucleic acids: genomic DNA and RNA. They triggered the emergence of "omics" fields of research such as genomics, epigenomics or transcriptomics. Lately massive parallel sequencing further increased the throughput of data generation related to nucleic acids by several orders of magnitude. In a different way, mass spectrometry-related technologies allow the identification and the quantification of many kinds of molecular entities such as metabolites and proteins. Many information systems have been developed to manage the exploding amount of data and knowledge related to biological molecular entities. These resources manage different aspects of the data. For example, some are genome or proteome centered, whereas others are focused on molecular interactions and pathways. Thus, all these resources rely on different identifier systems to organize concepts of their interest. The value of all the experimental data and all the knowledge collected in public or private resources is very high as such but is also often synergistically leveraged by their cross comparison in a dedicated manner. Indeed, many data sets can be relevant when addressing the understanding of a specific biological system, a phenotypic trait or a disease for example. These data sets can focus on different biological entities such as transcripts or proteins in different tissues, conditions or organisms. Comparing all these data and integrating them with available knowledge requires the ability to map the identifiers on which each resource relies.

To achieve this task public and proprietary information systems provide mapping tables between their own identifiers (ID) and those from other resources. Furthermore, many tools have been developed to facilitate the access to this information. Ensembl BioMart [@kinsella_ensembl_2011], mygene [@wu_biogps_2013], and g:Profiler [@reimand_g:profiler-web_2016] are popular examples among many others. These resources are convenient and easy to use with information managed by their maintainers. However, in general (BioMart provides tools to configure marts), this information cannot be easily customized, optimized or extended by an empowered user according to his knowledge or to internal, non-public or non-standard data. Recognizing these challenges @van_iersel_bridgedb_2010 proposed the BridgeDb framework providing to bioinformatics developers a standard interface between tools and mapping services and also allowing the easy integration of custom data by a transitivity mechanism. However, to our knowledge, transitivity in the BridgeDb framework is not leveraged to improve the completeness of ID conversion (e.g. the number of Ensembl gene ID actually converted to Entrez gene ID).

Here we present BED: a 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.

To meet these challenges, we designed a graph data model describing possible relationships between different biological entities and their identifiers. This data model has been implemented with the \neo{} graph database [@neo4j_inc_neo4j_2017]. Graph databases are very efficient to implement biological data models. They are more and more used in different fields of application. For example, they are successfully used for integrating various pieces of knowledge [@pareja-tobes_bio4j:_2015, @yoon_use_2017], describing disease and phenotype relationships [@pareja-tobes_bio4j:_2015] or modeling molecular networks and pathways [@dai_hrgrn:_2016, @fabregat_reactome_2018-1].

In addition to the \neo{} graph database, conversion rules have been defined and coded in an R [@r_core_team_r:_2017] package. A particular attention has been put on the efficiency of the tool by implementing a cache system making recurrent queries fast.

Finally, we provide, for convenience, an instance of the BED database focused on human, mouse and rat organisms. Nevertheless, many functions are available in the R package to customize this instance or to construct other instances tailored to other needs.

Methods

Data model

include_graphics('img/BED-Data-Model.pdf')

The BED (Biological Entity Dictionary) system relies on a data model inspired by the central dogma of molecular biology [@crick_central_1970] and describing relationships between molecular concepts usually manipulated in the frame of genomics studies (Figure \@ref(fig:Data-Model)). A biological entity identifier (BEID) can identify either a Gene (GeneID), a Transcript (TranscriptID), a Peptide (PeptideID) or an Object (ObjectID). Object entities can correspond to complex concepts coded by any number of genes (i.e. a protein complex or a molecular function). BEID are extracted from public or private databases (BEDB). BEDB can provide an Attribute related to each BEID. For example, it can be the sequencing region provided by the Ensembl database [@zerbino_ensembl_2017] or the identifier status provided by Uniprot [@the_uniprot_consortium_uniprot:_2017]. BEID can have one or several associated names (BENames) and symbols (BESymbol). GeneID can have one or several homologs in other organisms belonging to the same GeneIDFamily. Many genomics platforms, such as micro-arrays, allow the identification of biological entities by using probes identified by ProbeID. In general, BEID can be targeted by several probes belonging to a Platform which is focused on one, and only one, type of entity (BEType) among those described above: Gene, Transcript, Peptide or Object.

According to this data model, the scope of an identifier is defined by three features:

A BEType can have several BEType products but can be the product of at most one BEType. This constraint allows the unambiguous identification of the most relevant path to convert identifiers from one scope to another and is fulfilled by the current data model: peptides are only produced from transcripts, which are only produced from genes, which can also code for objects.

BEID identifying the same biological entity are related through three different kinds of relationship according to the information available in the source databases, and to the decision made by the database administrator about how to use them. Two BEID which corresponds_to each other both identify the same biological entity. A BEID which is_associated_to or which is_replaced_by another BEID does not directly identify any biological entity: the link is always indirect through one or several other BEID. Therefore, by design a BEID which is_associated_to or which is_replaced_by another BEID can be related to several different biological entities. It is not the case for other BEID which identify one and only one biological entity. Relationships corresponds_to and is_associated_to are used to map identifiers from different databases with different transivity properties (as explained below), whereas is_replaced_by relationships are used to map deprecated identifiers from former versions of the same database. This set of possible relationships allows the indirect mapping of different identifiers not necessarily provided by any integrated resource.

Implementation

In order to efficiently leverage an indirect path through these different relationships, the data model has been implemented in a \neo{} graph database [@neo4j_inc_neo4j_2017].

Two R [@r_core_team_r:_2017] packages have been developed to feed and query the database. The first one, neo2R, provides low level functions to interact with \neo{}. The second R package, BED, provides functions to feed and query the BED \neo{} graph database according to the data model described above.

Feeding the database

Many functions are provided within the package to build a tailored BED database instance. These functions are not exported in order not to mislead the user when querying the database (which is the expected most frequent usage of the system). An R markdown document showing how to build a BED database instance for human, mouse and rat organisms is provided within the package. It can be adapted to other organisms or needs.

Briefly, these functions can be divided according to three main levels:

Available database instance

An instance of the BED database (UCB-Human), built using the script provided in the BED R package, is available in a \docker{} image [@docker_inc_docker_2017] available here: https://hub.docker.com/r/patzaw/bed-ucb-human/ (tag 2018-04-30). This instance is focused on Homo sapiens, Mus musculus and Rattus norvegicus organisms and it has been built from these resources:

The results and the use cases described below were obtained and executed using this instance of the BED database.

toShow <- c()
for(be in listBe()){
   for(org in BED::listOrganisms()){
      toAdd <- listBeIdSources(
         be=be,
         organism=org,
         exclude=c("BEDTech_gene", "BEDTech_transcript"),
         verbose=FALSE
      )
      colnames(toAdd) <- c("Database", "nbBE", "BEID", "BE")
      toAdd$Organism <- org
      toShow <- rbind(
         toShow,
         toAdd[,c("BE", "Organism", "Database", "BEID")]
      )
   }
}
urls <- c()
for(i in 1:nrow(toShow)){
   urls <- c(urls, getBeIdURL("", toShow[i, "Database"]))
}
urls <- substr(
   urls, start=1, stop=regexpr("[^/][/][^/].*$", urls)
)
# urls <- sub("^https*[:][/][/]", "", urls)
toShow$URL <- urls
kable(
   toShow,
   row.names=FALSE,
   format='pandoc',
   format.args=list(big.mark=","),
   caption='Numbers of BEID available in the BED UCB-Human database instance.
   Numbers have been split according to the BEType
   and the organism.
   Only BEID which can be mapped to each other are taken into account
   (i.e. excluding deprecated identifiers without successor).'
)

load("~/Tmp/BEIDs-Stats.rda")

## Total number of BEID
nbBeIds <- bedCall(
   cypher,
   query <- prepCql(c(
      'MATCH (n:BEID)',
      #'-[:is_replaced_by|is_associated_to*0..]->(:BEID)-[:identifies]->()',
      'WHERE NOT n.database IN ["BEDTech_gene", "BEDTech_transcript"]',
      'RETURN count(distinct n);'
   ))
)
nbProbeIds <- bedCall(
   cypher,
   query <- prepCql(c(
      'MATCH (n:ProbeID)',
      'RETURN count(distinct n);'
   ))
)
save(
   nbBeIds, nbProbeIds,
   file="~/Tmp/BEIDs-Stats.rda"
)

The numbers of BEID available in this BED database instance and which can be mapped to each other are shown in Table \@ref(tab:ID-Numbers). In total, r bigFormat(nbBeIds) BEID are available in this BED instance. This number includes deprecated identifiers without successor and which therefore cannot be mapped to any other identifier. All the genomics platforms included in this BED database instance are shown in Table \@ref(tab:Platforms). They provide mapping to BEID from r bigFormat(nbProbeIds) ProbeID in total.

toShow <- listPlatforms()
colnames(toShow) <- c("Name", "Description", "BE")
toShow$Description <- sub(" [(].*$", "", toShow$Description)
kable(
   toShow,
   row.names=FALSE,
   format='pandoc',
   caption='Genomics platforms available in the BED UCB-Human database instance.'
)

Transitivity management

In the context of mapping identifiers, transitivity is the inference of a cross-reference between A and C based on existing cross-references between A and B and between B and C. Depending on how biological entities are defined, transitivity is desirable or not. In BED the transitivity mechanism is managed by the two following relationships: corresponds_to and is_associated_to. On one hand, the corresponds_to relationships make the mapping transitive since two BEIDs which are connected through this kind of relationship are considered to identify the same BE. On the other hand, a BEID which is_associated_to another one does not automatically identify the same BE making this kind of relationship not available for transitive mappings. When the BED database is fed, the user chooses which relationship should be of type corresponds_to or of type is_associated_to. For example, in the instance described above, cross-references provided by Ensembl from Ensembl gene identifiers to Entrez, HGNC and Vega gene identifiers are considered as corresponds_to relationships whereas cross-references to miRbase, Unigene and OMIM are considered as is_associated_to relationships.

Querying the database

The BED R package provides functions to connect to the \neo{} database (connectToBed) and to directly query it (bedCall). Beside these two functions, others of higher level are provided to explore available data, to manage identifiers and to convert them from one scope to another.

Different functions can be used to explore the data model by listing the type of BE (listBe) or describing their relationships (firstCommonUpstreamBe). Available organisms, databases and platforms can also be retrieved using listOrganisms, listBeIdSources and listPlatforms functions.

All BEID from a specific scope can be obtained with the getBeIds function. A specific BEID and its relationships with others can be graphically explored with the exploreBe function. The use and the results of these two functions are exemplified in Results. Also, the functions guessIdOrigin and checkBeIds are used to guess and to check the scope of any list of identifiers. This set of functions is completed by the getBeIdSymbols, getBeIdNames, getGeneDescription and getBeIdDescription functions which provide different ways to annotate identifiers taking advantage of information related to connected BEID. Other functions, searchId and getRelevantIds, are also provided to seek relevant identifiers for a specific BE. These functions are used by a shiny [@chang_shiny:_2017] gadget (findBe) providing an interactive dictionary of BEID which is also made available as an Rstudio add-in [@cheng_miniui:_2016; @allaire_rstudioapi:_2017].

As described above, the BED data model has been built to fulfill molecular biology processes in order to ensure the biological relevance of identifier mappings. The is_expressed_as and is_translated_in relationships correspond to the transcription and translation processes respectively whereas codes_for is a fuzzy relationship allowing the mapping of genes on objects not necessarily corresponding to the same kind of biological molecules. These processes are described in different databases with different levels of granularity. For example, Ensembl [@zerbino_ensembl_2017] provides possible transcripts for each gene specifying which of them is canonical. The getDirectProduct and getDirectOrigin functions allow the user to retrieve direct products or direct origins of such molecular biology processes.

The automatic conversion of identifiers from one scope to another is handled by the convBeIds function. This conversion process can be applied directly on lists of identifiers (convBeIdLists) or on data frames with an identifier column (convDfBeIds). Because such conversion can be intricate, the exploreConvPath function is provided to display the shortest relevant paths between two identifiers. These functions are also exemplified in Results and Use case.

Converting thousands of identifiers can take some time (generally a few seconds). Also, such conversions are often recurrent and redundant. In order to improve the performance for such recurrent and redundant queries, a cache system has been implemented. The first time, the query is run on \neo{} for all the relevant ID related to user input and the result is saved in a local file. Next time similar queries are requested, the system does not call \neo{} but loads the cached results and filters it according to user input. By default, the cache is flushed when the system detects inconsistencies with the BED database. If needed, it can also be manually flushed by using the lsBedCache and clearBedCache functions.

Operation

Minimal system requirements for running BED and neo2R R packages:

The graph database has been implemented with \neo{} version 3 [@neo4j_inc_neo4j_2017]. The BED R package depends on the following packages available in the Comprehensive R Archive Network [@cran_comprehensive_nodate]:

Results

The results below exemplify how BED efficiently tackle the three challenges described in the introduction. (1) The way identifiers are managed allow the mapping of deprecated identifiers. (2) The identifier conversion process takes advantage of the transitivity mechanism described in Methods to improve its completeness. (3) Mapping rules between different types of biological entities (BE) allow the correct and automatic conversion of identifiers. Finally, BED run time has been compared to three other tools in different contexts. These results were obtained using the instance of the BED database described in Methods and which is available in Docker Hub: https://hub.docker.com/r/patzaw/bed-ucb-human/ (tag 2018-04-30).

Management of identifiers

Identifiers (ID) in BED can identify a biological entity (BEID) or a probe (ProbeID). The getBeIds function returns all ID from a specific scope. As described in Methods a scope is defined by its type (the type of BE or "Probe"), its source (a database for BEID or a platform for ProbeID) and the organism to which it refers. For example, the following code returns all the Ensembl identifiers of human genes.

beids <- getBeIds(
    be="Gene", source="Ens_gene", organism="human",
    restricted=FALSE
)
head(beids)

redUnRestIds <- table(table(beids$Gene))
redUnRestIds <- redUnRestIds[order(as.numeric(names(redUnRestIds)))]
ensVersion <- bedCall(
   cypher,
   query=prepCql(c(
      'MATCH (n:BEDB {name:"Ens_gene"}) RETURN n.currentVersion'
   ))
)

layout(matrix(c(1,1,1,1,2,2,2), 1, 7, byrow = TRUE))
par(mar=c(5.1, 4.5, 1.1, 0.5))
barplot(
   redUnRestIds,
   xlab="Number of BEID",
   ylab="Number of BE", log="y",
   las=2,
   cex.axis=0.7,
   cex.names=0.7
)
mtext(text="(a)", side=2, line=3, at=10^par()$usr[4], las=2, font=2)
##
rbeids <- getBeIds(
    be="Gene", source="Ens_gene", organism="human",
    restricted = TRUE
)
redIds <- table(table(rbeids$Gene))
redIds <- redIds[order(as.numeric(names(redIds)))]
barplot(
   redIds,
   xlab="Number of BEID",
   ylab="Number of BE", log="y",
   las=2,
   cex.axis=0.7,
   cex.names=0.7
)
mtext(text="(b)", side=2, line=3, at=10^par()$usr[4], las=2, font=2)

The id column corresponds to the BEID from the source of interest. The column named according to the type of BE (in this case Gene) corresponds to the internal identifiers of the related BE. This internal identifier is not a stable reference that can be used as such. Nevertheless, it is useful to identify BEID identifying the same BE. In the example above, even if most Gene BE are identified by only one Ensembl gene BEID, many of them are identified by two or more (r bigFormat(sum(redUnRestIds[which(as.numeric(names(redUnRestIds))>=2)])) / r bigFormat(sum(redUnRestIds)) $\approx$ r round(100*sum(redUnRestIds[which(as.numeric(names(redUnRestIds))>=2)])/sum(redUnRestIds))%); r sum(redUnRestIds[which(as.numeric(names(redUnRestIds))>=10)]) BE are even identified by more than 10 Ensembl BEID (Figure \@ref(fig:beidTables).a). In this case, most of these redundancies come from deprecated BEID from former versions of the Ensembl database (version in use here: r ensVersion) and can be excluded by setting the restricted parameter to TRUE when calling the getBeIds function (Figure \@ref(fig:beidTables).b). However, many BE are still identified by two or more current Ensembl BEID (r bigFormat(sum(redIds[which(as.numeric(names(redIds))>=2)])) / r bigFormat(sum(redIds)) $\approx$ r round(100*sum(redIds[which(as.numeric(names(redIds))>=2)])/sum(redIds))%). This result comes from the way the BED database is constructed: When two BEID from the same resource correspond to the same BEID in another resource (correspond_to relationship in the data model), all these BEID are considered to identify the same BE.

A complex example of such mapping is shown in Figure \@ref(fig:TAS2R8) mapping all the BEID of the human TAS2R8 gene which codes for a protein of the family of candidate taste receptors. There are three identifiers corresponding to this gene symbol in Ensembl. All these three BEID correspond to the same Entrez gene and the same HGNC identifiers. All these BEID are thus considered to identify the same gene. It turns out that the three Ensembl BEID correspond to the same gene mapped on different sequence versions of the chromosome 12: the canonical (ENSG00000121314), CHR_HSCHR12_2_CTG2 (ENSG00000272712) and CHR_HSCHR12_3_CTG2 (ENSG00000277316). This information provided by Ensembl is encoded in the seq_region attribute for each Ensembl BEID (see data model) and is used to define preferred BEID which are mapped on canonical versions of chromosome sequences. The ENSG00000272712 identifier shows also a complex history in former Ensembl versions.

```r identifiers. Solid arrows represent \emph{correspond\_to} (between two nodes of identical shape) and \emph{is\_known\_as} (between two nodes of different shapes) relationships. Dotted arrows represent \emph{is\_replaced\_by} (between two nodes of identical color) and \emph{is\_associated\_to} (between two nodes of different colors) relationships. This graph has been drawn with the \texttt{exploreBe} function.'} include_graphics('img/TAS2R8-Identifiers.png')

## Converting gene identifiers

```r
## Save/load results from commmands below to not recompute everything
load("~/Tmp/ConvTables-3rdTools.rda")

The main goal of BED is to convert identifiers from one scope to another easily, rapidly and with high completeness. It has been thought in order to allow recurring comparisons to each other of many lists of BEID from various origins.

A simple example regarding the conversion of human Ensembl gene to human Entrez gene identifiers is shown below and discussed hereafter. By setting the restricted parameter to TRUE the converted BEID are restricted to current - non-deprecated - version of Entrez gene identifiers. Nevertheless, all the input BEID are taken into account, current and deprecated ones.

bedConv <- convBeIds(
   ids=beids$id, from="Gene", from.source="Ens_gene", from.org="human",
   to.source="EntrezGene", restricted=TRUE
)

curBeid <- unique(beids$id[which(beids$db.deprecated=="FALSE")])
depBeid <- unique(beids$id[which(beids$db.deprecated!="FALSE")])

Among all the r bigFormat(length(curBeid)+ length(depBeid)) human Ensembl gene identifiers available in the database, r bigFormat(sum(is.na(bedConv$to))) (r round(100*sum(is.na(bedConv$to))/length(unique(bedConv$from)))%) were not converted to any human Entrez gene identifier: r bigFormat(sum(is.na(bedConv$to[which(bedConv$from %in% curBeid)]))) (r round(100*sum(is.na(bedConv$to[which(bedConv$from %in% curBeid)]))/length(curBeid))%) of the r bigFormat(length(curBeid)) non-deprecated and r bigFormat(sum(is.na(bedConv$to[which(bedConv$from %in% depBeid)]))) (r round(100*sum(is.na(bedConv$to[which(bedConv$from %in% depBeid)]))/length(depBeid))%) of the r bigFormat(length(depBeid)) deprecated identifiers.

## biomaRt
library(biomaRt)
bmEnsembl = useMart("ensembl",dataset="hsapiens_gene_ensembl")
bmConv <- getBM(
   values = unique(beids$id),
   filters = 'ensembl_gene_id',
   attributes=c('ensembl_gene_id', 'entrezgene'),
   mart = bmEnsembl
)

## mygene
library(mygene)
mgConv <- queryMany(
   qterm=unique(beids$id),
   scopes = "ensembl.gene",
   fields="entrezgene",
   species="human"
)

## gProfileR
library(gProfileR)
gpConv <- gconvert(
   query=unique(beids$id),
   target="ENTREZGENE_ACC",
   organism="hsapiens",
   filter_na=FALSE,
   df=FALSE
)
gpConv <- do.call(
   rbind,
   lapply(
      gpConv,
      function(x){
         data.frame(
            alias=as.character(x[,"alias"]),
            target=as.character(x[,"target"]),
            stringsAsFactors=FALSE
         )
      }
   )
)
gpConv$target <- sub("ENTREZGENE_ACC:", "", gpConv$target)
gpConv$target <- ifelse(gpConv$target=="N/A", NA, gpConv$target)
rownames(gpConv) <- c()
gpConv <- unique(gpConv)

evalDate <- Sys.Date()
save(
   bedConv, bmConv, mgConv, gpConv, evalDate,
   file="~/Tmp/ConvTables-3rdTools.rda"
)

In order to assess the improvement of completeness achieved by BED, we compared it to three other tools: biomaRt [@kinsella_ensembl_2011; @durinck_mapping_2009], mygene [@wu_biogps_2013; @mark_mygene:_2014], and gProfileR [@reimand_g:profiler-web_2016; @reimand_gprofiler:_2016]. All these tools were used on r format(evalDate, "%b %d, %Y") to perform the same conversion task. At that time, biomaRt was based on the Ensembl 92 release (as the BED database instance), mygene on release 91 and gProfileR on release 90.

gq.all <- unique(beids$id)
toComp.all <- list(
   "BED"=unique(bedConv$from[which(!is.na(bedConv$to))]),
   "biomaRt"=unique(bmConv$ensembl_gene_id[which(!is.na(bmConv$entrezgene))]),
   "mygene"=unique(mgConv$query[which(!is.na(mgConv$entrezgene))]),
   "gProfileR"=unique(gpConv$alias[which(!is.na(gpConv$target))])
)
gq.cur <- unique(beids$id[which(beids$db.deprecated=="FALSE")])
toComp.cur <- lapply(
   toComp.all,
   intersect,
   gq.cur
)

venn.plot.all <- venn.diagram(
   toComp.all, NULL,
   fill=brewer.pal(4, "Accent"),
   alpha=rep(0.5, 4),
   main="(a)", main.pos=c(0.05,0.95),
   main.fontface=2, main.cex=1.8
)
venn.plot.cur <- venn.diagram(
   toComp.cur, NULL,
   fill=brewer.pal(4, "Accent"),
   alpha=rep(0.5, 4),
   margin=0.1,
   main="(b)", main.pos=c(0.05,0.95),
   main.fontface=2, main.cex=1.8
)
grid.newpage()
grid.arrange(
   gTree(children=venn.plot.all, vp=viewport(width=0.8,height=0.9)),
   gTree(children=venn.plot.cur, vp=viewport(width=0.8,height=0.9)),
   ncol=2
)

## Exploring mappings only from BED
onlyInBed <- bedConv[
   which(
      !bedConv$from %in% unlist(toComp.all[-1]) &
      !is.na(bedConv$to)
   ),
]
onlyInBedCur <- onlyInBed[
   which(
      onlyInBed$from %in% beids$id[which(beids$db.deprecated=="FALSE")]
   ),
]
onlyInBedDep <- onlyInBed[
   which(onlyInBed$from %in% setdiff(onlyInBed$from, onlyInBedCur$from)),
]
head(onlyInBedDep[order(as.numeric(onlyInBedDep$to)),])
head(onlyInBedCur[order(as.numeric(onlyInBedCur$to)),])
sum(unique(onlyInBedCur$from) %in% beids$id[which(beids$preferred)])
##
fromNcbi <- bedCall(
   cypher,
   query=prepCql(c(
   'MATCH (f:GeneID {database:"Ens_gene"}) WHERE f.value IN $from',
   'MATCH (t:GeneID {database:"EntrezGene"})',
   'MATCH (f)-[:corresponds_to]-(t)',
   'RETURN f.value as from, t.value as to'
)),
   parameters=list(from=as.list(unique(onlyInBedCur$from)))
)
all(
   paste(fromNcbi$from, fromNcbi$to) %in%
      paste(onlyInBedCur$from, onlyInBedCur$to)
)
##
fromHgnc <- bedCall(
   cypher,
   query=prepCql(c(
   'MATCH (f:GeneID {database:"Ens_gene"}) WHERE f.value IN $from',
   'MATCH (i:GeneID {database:"HGNC"})',
   'MATCH (t:GeneID {database:"EntrezGene"})',
   'MATCH (f)-[:corresponds_to]-(i)-[:corresponds_to]-(t)',
   'RETURN f.value as from, t.value as to'
)),
   parameters=list(from=as.list(setdiff(onlyInBedCur$from, fromNcbi$from)))
)
##
remaining <- setdiff(
   onlyInBedCur$from, c(fromHgnc$from, fromNcbi$from)
)

## Numbers from venn diagram
onlyInGp <- setdiff(
   toComp.all$gProfileR,
   unlist(toComp.all[which(names(toComp.all)!="gProfileR")])
)
onlyInGp.l <- length(onlyInGp)
onlyInMg <- setdiff(
   toComp.all$mygene,
   unlist(toComp.all[which(names(toComp.all)!="mygene")])
)
onlyInMg.l <- length(onlyInMg)
onlyInGpAndMg <- setdiff(
   intersect(toComp.all$gProfileR, toComp.all$mygene),
   unlist(toComp.all[which(!names(toComp.all) %in% c("gProfileR", "mygene"))])
)
onlyInGpAndMg.l <- length(onlyInGpAndMg)
##
onlyInBedAndBm <- setdiff(
   intersect( toComp.all$BED, toComp.all$biomaRt),
   unlist(toComp.all[which(!names(toComp.all) %in% c("BED", "biomaRt"))])
)
onlyInBedAndBm.l <- length(onlyInBedAndBm)

The numbers of human Ensembl gene identifiers successfully converted by each method are compared in Figure \@ref(fig:vennTools). Mappings returned only by gProfileR or by mygene (r onlyInGp.l + r onlyInMg.l + r onlyInGpAndMg.l) are available in releases 90 and 91 of Ensembl respectively but not in release 92. They probably correspond to deprecated cross-references. Conversely, mappings returned by both BED and biomaRt but neither by gProfileR nor mygene (r onlyInBedAndBm.l) are available in release 92 of Ensembl but not in releases 91 or 90. All the gene identifiers successfully converted by biomaRt were also converted by BED. However, BED was able to map at least r bigFormat(length(unique(onlyInBed$from))) more identifiers than all the other tools (Figure \@ref(fig:vennTools).a). A few of these mappings (r bigFormat(length(unique(onlyInBedDep$from)))) are explained by the fact that BED is the only tool mapping deprecated identifiers to current versions. Nevertheless, even when focusing on the mapping of current versions of Ensembl identifiers, BED was able to map r bigFormat(length(unique(onlyInBedCur$from))) more identifiers than all the other tools (Figure \@ref(fig:vennTools).b). A few of these mappings (r bigFormat(length(unique(fromNcbi$from)))) are directly provided by the NCBI. But most of them (r bigFormat(length(unique(fromHgnc$from)))) are inferred from a mapping of the Ensembl and Entrez gene identifiers to the same HGNC [@gray_genenames.org:_2015] identifier.

## Gene locations
## Run "Analyses/S01-gff-processing.R" to generate the rda file used below
load("~/Tmp/gene-locations.rda")
mappings <- list()
## Reference
toAdd <- unique(bmConv[which(
   bmConv$ensembl_gene_id %in% intersect(
      intersect(toComp.all$BED, toComp.all$biomaRt),
      intersect(toComp.all$mygene, toComp.all$gProfileR)
   )
),])
colnames(toAdd) <- c("ensembl", "entrez")
toAdd$ensembl <- as.character(toAdd$ensembl)
toAdd$entrez <- as.character(toAdd$entrez)
toAdd <- toAdd[which(
      toAdd$ensembl %in% ensemblGenes$gene &
         toAdd$entrez %in% entrezGenes$gene
),]
toAdd <- cbind(
   cbind(toAdd, ensemblGenes[toAdd$ensembl,c("chromosome", "start", "stop")]),
   entrezGenes[toAdd$entrez,c("chromosome", "start", "stop")]
)
colnames(toAdd) <- c(
   "ensembl", "entrez",
   "chr.ens", "start.ens", "stop.ens",
   "chr.ncbi", "start.ncbi", "stop.ncbi"
)
mappings$"Reference" <- toAdd
## Former
toAdd <- unique(as.data.frame(mgConv[which(
   mgConv$query %in% setdiff(
      toComp.all$mygene,
      unlist(toComp.all[c("BED", "biomaRt")])
   )
), c("query", "entrezgene")]))
colnames(toAdd) <- c("ensembl", "entrez")
toAdd$ensembl <- as.character(toAdd$ensembl)
toAdd$entrez <- as.character(toAdd$entrez)
toAdd <- toAdd[which(
      toAdd$ensembl %in% ensemblGenes$gene &
         toAdd$entrez %in% entrezGenes$gene
),]
toAdd <- cbind(
   cbind(toAdd, ensemblGenes[toAdd$ensembl,c("chromosome", "start", "stop")]),
   entrezGenes[toAdd$entrez,c("chromosome", "start", "stop")]
)
colnames(toAdd) <- c(
   "ensembl", "entrez",
   "chr.ens", "start.ens", "stop.ens",
   "chr.ncbi", "start.ncbi", "stop.ncbi"
)
mappings$"Former" <- toAdd
##
toAdd <- unique(bedConv[which(
   bedConv$from %in% setdiff(
      toComp.cur$BED,
      unlist(toComp.cur[c("biomaRt", "mygene", "gProfileR")])
   )
), c("from", "to")])
colnames(toAdd) <- c("ensembl", "entrez")
toAdd$ensembl <- as.character(toAdd$ensembl)
toAdd$entrez <- as.character(toAdd$entrez)
toAdd <- toAdd[which(
      toAdd$ensembl %in% ensemblGenes$gene &
         toAdd$entrez %in% entrezGenes$gene
),]
toAdd <- cbind(
   cbind(toAdd, ensemblGenes[toAdd$ensembl,c("chromosome", "start", "stop")]),
   entrezGenes[toAdd$entrez,c("chromosome", "start", "stop")]
)
colnames(toAdd) <- c(
   "ensembl", "entrez",
   "chr.ens", "start.ens", "stop.ens",
   "chr.ncbi", "start.ncbi", "stop.ncbi"
)
mappings$"BED" <- toAdd

We assessed the validity of the mappings by comparing the location on the human genome (GRCh38) of Ensembl gene identifiers as reported by Ensembl and the location of the corresponding Entrez gene identifiers as reported by the NCBI. If the mapping between two identifiers is correct their location on the genome should be identical or highly similar (only gene identifiers located on canonical versions of chromosome sequences were considered for this comparison). We compared the following mapping results:

# par(mfrow=c(1,5))
i <- 10
layout(matrix(c(rep(1,i+1), rep(2,i), rep(3,i-1), rep(4,i), rep(5,i-1)), 1, (i*5)-1, byrow = TRUE))
## Chromosomes
par(mar=c(5.3, 4.5, 1.1, 0.5))
toPlot <- unlist(lapply(
   mappings,
   function(d){
      sum(d$chr.ens!=d$chr.ncbi)/nrow(d)
   }
))
names(toPlot) <- paste0(
   names(toPlot),
   "\n(N=", unlist(lapply(mappings, function(x) bigFormat(nrow(x)))), ")"
)
bp <- barplot(
   toPlot*100,
   ylab=expression("" != "chromosomes (%)"),
   las=3,
   beside=TRUE
)
text(
   bp, 1,
   paste(signif(toPlot*100,2), "%"),
   pos=3,
   srt=90
)
mtext(text="(a)", side=2, line=2.5, at=par()$usr[4], las=2, font=2)
## Starts
par(mar=c(5.3, 4.5, 1.1, 0))
toPlot <- lapply(
   mappings,
   function(d){
      d <- d[which(d$chr.ens==d$chr.ncbi),]
      abs(d$start.ens-d$start.ncbi)
   }
)
names(toPlot) <- paste0(
   names(toPlot),
   "\n(N=", unlist(lapply(mappings, function(x) bigFormat(nrow(x)))), ")"
)
bp <- boxplot(
   toPlot[1:2],
   col="grey",
   outline=FALSE,
   las=3,
   ylab=expression(group("|", Delta(start), "|"))
)
text(
   2, bp$stats[3,2],
   paste(" ", round(bp$stats[3,2])),
   pos=4,
   srt=90
)
text(
   2, bp$stats[4,2],
   paste(" ", round(bp$stats[4,2])),
   pos=4,
   srt=90
)
mtext(text="(b)", side=2, line=2.5, at=par()$usr[4], las=2, font=2)
par(mar=c(5.3, 2.5, 1.1, 0.5))
bp <- boxplot(
   toPlot[c(1,3)],
   col="grey",
   # ylab=expression(group("|", Delta(start), "|")),
   las=3,
   outline=FALSE
)
text(
   1:2, bp$stats[3,],
   paste(" ", round(bp$stats[3,])),
   pos=4,
   srt=90
)
text(
   1:2, bp$stats[4,],
   paste(" ", round(bp$stats[4,])),
   pos=4,
   srt=90
)
## Sizes
par(mar=c(5.3, 4.5, 1.1, 0))
toPlot <- lapply(
   mappings,
   function(d){
      d <- d[which(d$chr.ens==d$chr.ncbi),]
      abs(d$stop.ens-d$start.ens-d$stop.ncbi+d$start.ncbi)
   }
)
names(toPlot) <- paste0(
   names(toPlot),
   "\n(N=", unlist(lapply(mappings, function(x) bigFormat(nrow(x)))), ")"
)
bp <- boxplot(
   toPlot[1:2],
   col="grey",
   outline=FALSE,
   las=3,
   ylab=expression(group("|", Delta(size), "|"))
)
text(
   2, bp$stats[3,2],
   paste(" ", round(bp$stats[3,2])),
   pos=4,
   srt=90
)
text(
   2, bp$stats[4,2],
   paste(" ", round(bp$stats[4,2])),
   pos=4,
   srt=90
)
mtext(text="(c)", side=2, line=2.5, at=par()$usr[4], las=2, font=2)
par(mar=c(5.3, 2.5, 1.1, 0.5))
bp <- boxplot(
   toPlot[c(1,3)],
   col="grey",
   las=3,
   # ylab=expression(group("|", Delta(size), "|")),
   outline=FALSE
)
text(
   1:2, bp$stats[3,],
   paste("  ", round(bp$stats[3,])),
   pos=3,
   srt=90
)
text(
   1:2, bp$stats[4,],
   paste(" ", round(bp$stats[4,])),
   pos=4,
   srt=90
)

As shown in Figure \@ref(fig:geneLocations).a, the proportion of former cross-referenced identifiers ("Former") located on different chromosomes is much higher than for the reference ("Reference"). The proportion of BED cross-referenced identifiers ("BED") located on different chromosomes is higher than for "Reference" but still low. Figures \@ref(fig:geneLocations).a and b show that inconsistencies in gene start location and in gene size are much more important in "Former" mappings than in "Reference" mappings. Such difference is not observed between the "Reference" mappings and the additional mappings provided by BED. These results show that the general quality of "Former" mappings is lower than the "Reference", which is in agreement with their deprecation. More interestingly, additional mappings provided by BED are of similar quality to the "Reference" making their use as valuable.

As shown above, additional mappings between Ensembl and Entrez gene identifiers inferred thanks to the use of HGNC cross-references by BED are many and of good quality. However, this transitivity mechanism is not always desirable depending on how different resources define biological entities. It is especially true for gene which is an unstable concept as described by @gerstein_what_2007. For example, in Entrez the Hs.103110 UniGene identifier is mapped to 5465 and to 150383 Entrez gene identifiers which correspond to two different genes in Entrez (PPARA and CDPF1) but also in Ensembl (ENSG00000186951 and ENSG00000205643) and in HGNC (9232 and 33710). These two genes are located closely on the same chromosome but at different positions and on different directions. The same feature has been observed for many UniGene identifiers which makes UniGene unsuitable for transitivity mappings between gene identifiers. Therefore, in this instance of the BED database, as described in Methods, cross-references between Ensembl and Unigene gene identifiers are considered as is_associated_to relationships avoiding transitivity.

## Other examples can be found using the following query
x <- bedCall(
   cypher,
   query='
   MATCH (g2:Gene)<-[:identifies]-(tgid2:GeneID)<-[:is_associated_to]-
   (ogid:GeneID)-[:is_associated_to]->(tgid1:GeneID)-[:identifies]->(g1:Gene)
   OPTIONAL MATCH (tgid1)-[:is_known_as]->(tgn1:BESymbol)
   WHERE g1<>g2 AND tgid1.database=tgid2.database
   RETURN ogid.value, ogid.database, tgid1.value, tgid1.database, tgid2.value,
   tgid2.database, tgn1.value order by tgn1.value;'
)

Mapping rules

Beside cross-referencing identifiers of identical type of BE, BED uses the biological relationship between genes, transcript and peptides to convert identifiers across different type of BE. For example, when converting peptides identifiers from the same species it uses only mapping done at the peptide level and does not use mapping to transcripts and genes. This strategy seems to be applied by biomaRt but not by mygene nor by gProfileR which map, for example, one Uniprot identifier to all the Ensembl peptide identifier coded by the same gene. For example, the A6NI28 Uniprot identifier is unambiguously mapped to the ENSP00000298815 Ensembl peptide identifier by BED and biomaRt but is wrongly mapped to three additional Ensembl peptide identifiers by mygene and gProfileR (ENSP00000431776, ENSP00000434304 and ENSP00000435961) which are encoded by the same gene (ENSG00000165895).

Furthermore, in biomaRt, mygene and gProfileR, mapping of BEIDs that are not genes from two different organisms using orthologs information requires at least two steps: one to find the ortholog genes and the other to find the relevant BEID. These two steps are integrated and transparent in BED.

In general, thanks to the BED data model and to mapping rules, identifier conversions from one scope to another are biologically relevant and automatic as exemplified in the use case described below.

Because all these indirect mappings can be intricate, BED provides a function to show the shortest relevant paths between two different identifiers (Figure \@ref(fig:explConv)).

```r relationship. BEID surrounded in bold correspond to \emph{preferred} identifiers. This graph has been drawn with the \texttt{exploreConvPath} function.'} include_graphics('img/ILMN_1220595-Conversion.png')

## Performance

The time taken by BED to convert different sets of identifiers from one scope
to another has been compared to the time taken by biomaRt, mygene and gProfileR.
Three different queries have been executed
starting from 100 or 20,000 identifiers.

```r
## The files used below were generated by calling
## the "Analyses/S03-Performance.R" script several times
i <- 10
j <- 2
k <- 2
lm <- c(rep(1,i+k), rep(2,i+j), rep(3,i), rep(4,i+j), rep(5,i), rep(6,i+j))
layout(matrix(lm, 1, length(lm), byrow = TRUE))
################################################################################
par(mar=c(7.1, 4.5, 1.1, 0.1))
bn <- "Human-Genes-Ensembl-Entrez"
main <- "Ensembl Genes to Entrez genes (human)"
n <- 100
toPlot <- list()
for(i in 1:4){
   rdir <- sprintf("Analyses/Perf-Results/Day0%s", i)
   load(sprintf("%s/%s-%s.rda", rdir, bn, n))
   toPlot <- list(
      "BED"=c(toPlot$"BED", bedTC[,"elapsed"]),
      "biomaRt"=c(toPlot$"biomaRt", bmTC[,"elapsed"]),
      "mygene"=c(toPlot$"mygene", mgTC[,"elapsed"]),
      "gProfileR"=c(toPlot$"gProfileR", gpTC[,"elapsed"])
   )
}
## Remove strong outliers probably because of network issue
toPlot <- lapply(
   toPlot,
   function(x){
      x[which(x < median(x)+40*mad(x))]
   }
)
boxplot(
   toPlot,
   ylab="Elapsed time (seconds)",
   # main=paste(n, main),
   col="transparent",
   las=3,
   outline=FALSE
)
beeswarm(
   toPlot,
   col=c("#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3"),
   add=TRUE,
   corral="random"
)
mtext(text="(a)", side=2, line=2.5, at=par()$usr[4], las=2, font=2)
#############################
par(mar=c(7.1, 2.5, 1.1, 0.1))
n <- 20000
toPlot <- list()
for(i in 1:4){
   rdir <- sprintf("Analyses/Perf-Results/Day0%s", i)
   load(sprintf("%s/%s-%s.rda", rdir, bn, n))
   toPlot <- list(
      "BED"=c(toPlot$"BED", bedTC[,"elapsed"]),
      "BED (cached)"=c(toPlot$"BED (cached)", bedcTC[,"elapsed"]),
      "biomaRt"=c(toPlot$"biomaRt", bmTC[,"elapsed"]),
      "mygene"=c(toPlot$"mygene", mgTC[,"elapsed"]),
      "gProfileR"=c(toPlot$"gProfileR", gpTC[,"elapsed"])
   )
}
## Remove strong outliers probably because of network issue
toPlot <- lapply(
   toPlot,
   function(x){
      x[which(x < median(x)+40*mad(x))]
   }
)
boxplot(
   toPlot,
   # ylab="Elapsed time (seconds)",
   # main=paste(n, main),
   las=3,
   col="transparent",
   outline=FALSE
)
beeswarm(
   toPlot,
   col=c("#66C2A5", "#A6D854", "#FC8D62", "#8DA0CB", "#E78AC3"),
   add=TRUE,
   corral="random"
)
mtext(text="(b)", side=2, line=0.5, at=par()$usr[4], las=2, font=2)
################################################################################
par(mar=c(7.1, 2.5, 1.1, 0.1))
bn <- "Mouse-Peptides-Uniprot-Transcript-Ensembl"
main <- "Uniprot Peptides to Ensembl Transcripts (mouse)"
n <- 100
toPlot <- list()
for(i in 1:4){
   rdir <- sprintf("Analyses/Perf-Results/Day0%s", i)
   load(sprintf("%s/%s-%s.rda", rdir, bn, n))
   toPlot <- list(
      "BED"=c(toPlot$"BED", bedTC[,"elapsed"]),
      "biomaRt"=c(toPlot$"biomaRt", bmTC[,"elapsed"]),
      "mygene"=c(toPlot$"mygene", mgTC[,"elapsed"]),
      "gProfileR"=c(toPlot$"gProfileR", gpTC[,"elapsed"])
   )
}
## Remove strong outliers probably because of network issue
toPlot <- lapply(
   toPlot,
   function(x){
      x[which(x < median(x)+40*mad(x))]
   }
)
boxplot(
   toPlot,
   ylab="Elapsed time (seconds)",
   # main=paste(n, main),
   col="transparent",
   las=3,
   outline=FALSE
)
beeswarm(
   toPlot,
   col=c("#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3"),
   add=TRUE,
   corral="random"
)
mtext(text="(c)", side=2, line=0.5, at=par()$usr[4], las=2, font=2)
#############################
par(mar=c(7.1, 2.5, 1.1, 0.1))
n <- 20000
toPlot <- list()
for(i in 1:4){
   rdir <- sprintf("Analyses/Perf-Results/Day0%s", i)
   load(sprintf("%s/%s-%s.rda", rdir, bn, n))
   toPlot <- list(
      "BED"=c(toPlot$"BED", bedTC[,"elapsed"]),
      "BED (cached)"=c(toPlot$"BED (cached)", bedcTC[,"elapsed"]),
      "biomaRt"=c(toPlot$"biomaRt", bmTC[,"elapsed"]),
      "mygene"=c(toPlot$"mygene", mgTC[,"elapsed"]),
      "gProfileR"=c(toPlot$"gProfileR", gpTC[,"elapsed"])
   )
}
## Remove strong outliers probably because of network issue
toPlot <- lapply(
   toPlot,
   function(x){
      x[which(x < median(x)+40*mad(x))]
   }
)
boxplot(
   toPlot,
   # ylab="Elapsed time (seconds)",
   # main=paste(n, main),
   las=3,
   col="transparent",
   outline=FALSE
)
beeswarm(
   toPlot,
   col=c("#66C2A5", "#A6D854", "#FC8D62", "#8DA0CB", "#E78AC3"),
   add=TRUE,
   corral="random"
)
mtext(text="(d)", side=2, line=0.5, at=par()$usr[4], las=2, font=2)
################################################################################
par(mar=c(7.1, 2.5, 1.1, 0.1))
bn <- "Mouse-Affy-Peptide-Ensembl"
main <- "Affymetrix Probes to Ensembl Peptide (mouse)"
n <- 100
toPlot <- list()
for(i in 1:4){
   rdir <- sprintf("Analyses/Perf-Results/Day0%s", i)
   load(sprintf("%s/%s-%s.rda", rdir, bn, n))
   toPlot <- list(
      "BED"=c(toPlot$"BED", bedTC[,"elapsed"]),
      "biomaRt"=c(toPlot$"biomaRt", bmTC[,"elapsed"]),
      "mygene"=c(toPlot$"mygene", mgTC[,"elapsed"]),
      "gProfileR"=c(toPlot$"gProfileR", gpTC[,"elapsed"])
   )
}
## Remove strong outliers probably because of network issue
toPlot <- lapply(
   toPlot,
   function(x){
      x[which(x < median(x)+40*mad(x))]
   }
)
boxplot(
   toPlot,
   ylab="Elapsed time (seconds)",
   # main=paste(n, main),
   col="transparent",
   las=3,
   outline=FALSE
)
beeswarm(
   toPlot,
   col=c("#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3"),
   add=TRUE,
   corral="random"
)
mtext(text="(e)", side=2, line=0.5, at=par()$usr[4], las=2, font=2)
#############################
par(mar=c(7.1, 2.5, 1.1, 0.1))
n <- 20000
toPlot <- list()
for(i in 1:4){
   rdir <- sprintf("Analyses/Perf-Results/Day0%s", i)
   load(sprintf("%s/%s-%s.rda", rdir, bn, n))
   toPlot <- list(
      "BED"=c(toPlot$"BED", bedTC[,"elapsed"]),
      "BED (cached)"=c(toPlot$"BED (cached)", bedcTC[,"elapsed"]),
      "biomaRt"=c(toPlot$"biomaRt", bmTC[,"elapsed"]),
      "mygene"=c(toPlot$"mygene", mgTC[,"elapsed"]),
      "gProfileR"=c(toPlot$"gProfileR", gpTC[,"elapsed"])
   )
}
## Remove strong outliers probably because of network issue
toPlot <- lapply(
   toPlot,
   function(x){
      x[which(x < median(x)+40*mad(x))]
   }
)
boxplot(
   toPlot,
   # ylab="Elapsed time (seconds)",
   # main=paste(n, main),
   las=3,
   col="transparent",
   outline=FALSE
)
beeswarm(
   toPlot,
   col=c("#66C2A5", "#A6D854", "#FC8D62", "#8DA0CB", "#E78AC3"),
   add=TRUE,
   corral="random"
)
mtext(text="(f)", side=2, line=0.5, at=par()$usr[4], las=2, font=2)

As shown in Figure \@ref(fig:perf), BED outperforms mygene and gProfileR in all the tested cases even when not using its cache system. It also outperforms biomaRt for converting Affymetrix probe ID into Ensembl mouse peptide ID (Figures \@ref(fig:perf).e and \@ref(fig:perf).f). Without using its cache system, BED performs equally well as biomaRt for converting Ensembl human gene ID into Entrez gene ID (Figures \@ref(fig:perf).a and \@ref(fig:perf).b). It outperforms biomaRt for converting 100 Uniprot mouse peptide ID into Ensembl transcript ID (Figures \@ref(fig:perf).c) but is outperformed by biomaRt when 20,000 identifiers are considered (Figures \@ref(fig:perf).d). Nevertheless, when cache is used, BED outperforms all the tools whatever the conversion scenario (Figures \@ref(fig:perf).b, \@ref(fig:perf).d and \@ref(fig:perf).f). These results show that BED, as a dedicated and locally available tool, is a very efficient option to convert large lists of identifiers on the fly and recurrently.

Use Case: comparing transcriptomics data sets

In this use case, we show how BED facilitates the comparison of data sets relying on identifiers with different scopes. Indeed, the mechanisms implemented in the BED R package make easy the conversion of a set of identifiers from one scope to any other. Thus, the comparison of data from different genome wide experiments is straightforward from a technical point of view.

To exemplify this statement, we show below how to compare differential expression data from three experiments addressing the understanding of Psoriasis mechanisms with different experimental designs and platforms:

## DE data
## Run "Analyses/S02-Datasets.R" to generate the rda file used below
load("~/Tmp/BED-Comp-UseCase/DEG.rda")
hsSkin.DE <- hsSkin.DE[,c("logFC", "P.Value", "adj.P.Val")]
hsKera.DE <- hsKera.DE[,c("logFC", "P.Value", "adj.P.Val")]
mmSkin.DE <- mmSkin.DE[,c("logFC", "P.Value", "adj.P.Val")]

The first rows of these three data tables are shown below.

## E-GEOD-13355: Human skin
head(hsSkin.DE, n=3)
## E-GEOD-24767: Human keratinocytes
head(hsKera.DE, n=3)
## E-GEOD-27628: Mouse skin
head(mmSkin.DE, n=3)

The aim of the following commands is to allow the comparison of the three logFC values by converting ProbeID (row names) of one or the other data set. The function guessIdOrigin is used to identify the scope of the different sets of identifiers.

scopes <- data.frame(
   "Human skin"=unlist(guessIdOrigin(rownames(hsSkin.DE), tcLim=1000)),
   "Human keratinocytes"=unlist(guessIdOrigin(rownames(hsKera.DE), tcLim=1000)),
   "Mouse skin"=unlist(guessIdOrigin(rownames(mmSkin.DE), tcLim=1000)),
   stringsAsFactors=FALSE, check.names=FALSE
)
scopes

To compare the two human data sets, the keratinocyte data set can be converted to the same scope of the skin data set using the convDfBeIds function. After conversion, the two data sets can be merged before computing the correlation between logFC values.

## Human skin VS keratinocytes
convHsKera.DE <- convDfBeIds(
   df=hsKera.DE,
   from=scopes["be", "Human keratinocytes"],
   from.source=scopes["source", "Human keratinocytes"],
   from.org=scopes["organism", "Human keratinocytes"],
   to=scopes["be", "Human skin"],
   to.source=scopes["source", "Human skin"],
   to.org=scopes["organism", "Human skin"],
   restricted=TRUE
)
toCompare <- merge(
   hsSkin.DE[,"logFC", drop=FALSE],
   convHsKera.DE[,c("conv.to", "logFC")],
   by.x=0, by.y="conv.to"
)
colnames(toCompare) <- c("ID", "Skin", "Keratinocytes")
round(cor(toCompare$"Skin", toCompare$"Keratinocytes"), 2)

A similar procedure can be applied to compare the two skin data sets or to compare mouse skin and human keratinocytes.

## Human skin VS mouse skin
convMmSkin.DE <- convDfBeIds(
   df=mmSkin.DE,
   from=scopes["be", "Mouse skin"],
   from.source=scopes["source", "Mouse skin"],
   from.org=scopes["organism", "Mouse skin"],
   to=scopes["be", "Human skin"],
   to.source=scopes["source", "Human skin"],
   to.org=scopes["organism", "Human skin"],
   restricted=TRUE
)
toCompare <- merge(
   hsSkin.DE[,"logFC", drop=FALSE],
   convMmSkin.DE[,c("conv.to", "logFC")],
   by.x=0, by.y="conv.to"
)
colnames(toCompare) <- c("ID", "Human", "Mouse")
round(cor(toCompare$"Human", toCompare$"Mouse"), 2)

## Mouse skin VS human keratinocytes
conv2HsKera.DE <- convDfBeIds(
   df=hsKera.DE,
   from=scopes["be", "Human keratinocytes"],
   from.source=scopes["source", "Human keratinocytes"],
   from.org=scopes["organism", "Human keratinocytes"],
   to=scopes["be", "Mouse skin"],
   to.source=scopes["source", "Mouse skin"],
   to.org=scopes["organism", "Mouse skin"],
   restricted=TRUE
)
toCompare <- merge(
   mmSkin.DE[,"logFC", drop=FALSE],
   conv2HsKera.DE[,c("conv.to", "logFC")],
   by.x=0, by.y="conv.to"
)
colnames(toCompare) <- c("ID", "Mouse skin", "Human keratinocytes")
round(cor(toCompare$"Mouse skin", toCompare$"Human keratinocytes"), 2)

As shown above, converting the identifier scope of transcriptomic data sets is straightforward and quickly executed using BED. It makes the comparison (such as correlation analyses performed in this use case) and the integration of multiple heterogeneous data very easy to achieve.

Additional examples of BED functionalities are provided in the R package vignette.

Conclusions

The appearance of "omics" technologies, biological knowledge databases and systems biology analytical approaches have opened the possibility to integrate various data sets to get a better understanding of biological processes underlying different complex phenotypes such as diseases. However, this promising interoperability of data sets is largely hampered by the heterogeneity of identifiers used by technical platforms but also those used by knowledge databases to organize information. Comparing and integrating all these data requires the ability to map the identifiers on which each resource relies. Many tools have been developed to achieve this task (e.g. @kinsella_ensembl_2011, @wu_biogps_2013, @reimand_g:profiler-web_2016 or @van_iersel_bridgedb_2010). However, we identified three challenges generally not addressed by the available tools:

BED is a system dedicated to the mapping between identifiers of molecular biological entities. It relies on a graph data model implemented with \neo{} and on rules coded in an R package. BED leverages mapping information provided by different resources in order to increase the mapping completeness between each of them. It also allows the mapping of deprecated identifiers. Rules are used to automatically convert identifiers from one scope to another using the most appropriate path.

The intent of BED is to be tailored to specific needs and beside functions for querying the system the BED R package provides functions to build custom instances of the database, including internal or proprietary resources for example. Database instances can be locally installed or shared across a community. This design combined with a cache system makes BED efficient for converting large lists of identifiers from and to a large variety of scopes. Because of our research field we provide an instance focused on human, mouse and rat organisms. This database instance can be directly used in relevant projects but it can also be enriched depending on user or community needs.

Beside a casual use for analyzing and comparing data in the frame of a research project, BED can be advantageously employed by systems dealing with biological and molecular information from heterogeneous sources. Indeed, various pieces of knowledge can be efficiently managed in their original scope (e.g. gene ID for genomics or epigenomics data, transcript ID for transcriptomics data and protein ID for proteomics data). Thanks to the automatic and fast conversion of identifiers all these data can then be compared to each other or to any user input. We do not provide BED API for other languages than R but we expect that publishing the source code under an open source license (GPL-3) will encourage other developers to extend or improve it according to their needs and their expertise.

Software availability

Latest source code is available at:

https://github.com/patzaw/BED

https://github.com/patzaw/neo2R

Archived source code as at time of publication:

http://doi.org/10.5281/zenodo.1244150 [@godard_patzaw/bed:_2018]

http://doi.org/10.5281/zenodo.1167670 [@godard_patzaw/neo2r:_2018]

Archived database docker image as at time of publication:

http://doi.org/10.5281/zenodo.1311362 [@godard_docker-bed-ucb-human-2018.04.30_2018]

Software is available to use under a GPL-3 license

Competing interests

No competing interests were disclosed.

Grant information

This work was entirely supported by UCB Pharma. The authors declared that no grants were involved in supporting this work.

Acknowledgments

We are grateful to Frédéric Vanclef, Malte Lucken, Liesbeth François, Matthew Page, Massimo de Francesco, and Marina Bessarabova for fruitful discussions and constructive criticisms.



patzaw/BED documentation built on April 30, 2024, 5:31 a.m.

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