The ensembldb package provides functions to create and use transcript centric annotation databases/packages. The annotation for the databases are directly fetched from Ensembl 1 using their Perl API. The functionality and data is similar to that of the TxDb packages from the GenomicFeatures package, but, in addition to retrieve all gene/transcript models and annotations from the database, the ensembldb package provides also a filter framework allowing to retrieve annotations for specific entries like genes encoded on a chromosome region or transcript models of lincRNA genes. From version 1.7 on, EnsDb databases created by the ensembldb package contain also protein annotation data (see Section 11 for the database layout and an overview of available attributes/columns). For more information on the use of the protein annotations refer to the proteins vignette.

Another main goal of this package is to generate versioned annotation packages, i.e. annotation packages that are build for a specific Ensembl release, and are also named according to that (e.g. EnsDb.Hsapiens.v86 for human gene definitions of the Ensembl code database version 86). This ensures reproducibility, as it allows to load annotations from a specific Ensembl release also if newer versions of annotation packages/releases are available. It also allows to load multiple annotation packages at the same time in order to e.g. compare gene models between Ensembl releases.

In the example below we load an Ensembl based annotation package for Homo sapiens, Ensembl version 86. The EnsDb object providing access to the underlying SQLite database is bound to the variable name EnsDb.Hsapiens.v86.


## Making a "short cut"
edb <- EnsDb.Hsapiens.v86
## print some informations for this package

## For what organism was the database generated?
## Disable code chunks that require network connection - conditionally
## disable this on Windows only. This is to avoid TIMEOUT errors on the
## Bioconductor Windows build maching (issue #47).
use_network <- FALSE

Using ensembldb annotation packages to retrieve specific annotations

One of the strengths of the ensembldb package and the related EnsDb databases is its implementation of a filter framework that enables to efficiently extract data sub-sets from the databases. The ensembldb package supports most of the filters defined in the AnnotationFilter Bioconductor package and defines some additional filters specific to the data stored in EnsDb databases. Filters can be passed directly to all methods extracting data from an EnsDb (such as genes, transcripts or exons). Alternatively it is possible with the addFilter or filter functions to add a filter directly to an EnsDb which will then be used in all queries on that object.

The supportedFilters method can be used to get an overview over all supported filter classes, each of them (except the GRangesFilter) working on a single column/field in the database.


These filters can be divided into 3 main filter types:

The supported filters are:

In addition to the above listed DNA-RNA-based filters, protein-specific filters are also available:

These can however only be used on EnsDb databases that provide protein annotations, i.e. for which a call to hasProteinData returns TRUE.

EnsDb databases for more recent Ensembl versions (starting from Ensembl 87) provide also evidence levels for individual transcripts in the tx_support_level database column. Such databases support also a TxSupportLevelFilter filter to use this columns for filtering.

A simple use case for the filter framework would be to get all transcripts for the gene BCL2L11. To this end we specify a GeneNameFilter with the value BCL2L11. As a result we get a GRanges object with start, end, strand and seqname being the start coordinate, end coordinate, chromosome name and strand for the respective transcripts. All additional annotations are available as metadata columns. Alternatively, by setting return.type to "DataFrame", or "data.frame" the method would return a DataFrame or data.frame object instead of the default GRanges.

Tx <- transcripts(edb, filter = GeneNameFilter("BCL2L11"))


## as this is a GRanges object we can access e.g. the start coordinates with

## or extract the biotype with

The parameter columns of the extractor methods (such as exons, genes or transcripts) allows to specify which database attributes (columns) should be retrieved. The exons method returns by default all exon-related columns, the transcripts all columns from the transcript database table and the genes all from the gene table. Note however that in the example above we got also a column gene_name although this column is not present in the transcript database table. By default the methods return also all columns that are used by any of the filters submitted with the filter argument (thus, because a GeneNameFilter was used, the column gene_name is also returned). Setting returnFilterColumns(edb) <- FALSE disables this option and only the columns specified by the columns parameter are retrieved.

Instead of passing a filter object to the method it is also possible to provide a filter expression written as a formula. The formula has to be written in the form ~ <field> <condition> <value> with <field> being the field (database column) in the database, <condition> the condition for the filter object and <value> its value. Use the supportedFilter method to get the field names corresponding to each filter class.

## Use a filter expression to perform the filtering.
transcripts(edb, filter = ~ gene_name == "ZBTB16")

Filter expression have to be written as a formula (i.e. starting with a ~) in the form column name followed by the logical condition.

Alternatively, EnsDb objects can be filtered directly using the filter function. In the example below we use the filter function to filter the EnsDb object and pass that filtered database to the transcripts method using the %>% from the magrittr package.


edb %>% filter(~ symbol == "BCL2" & tx_biotype != "protein_coding") %>%

Adding a filter to an EnsDb enables this filter (globally) on all subsequent queries on that object. We could thus filter an EnsDb to (virtually) contain only features encoded on chromosome Y.

edb_y <- addFilter(edb, SeqNameFilter("Y"))

## All subsequent filters on that EnsDb will only work on features encoded on
## chromosome Y

## Get all lincRNAs on chromosome Y
genes(edb_y, filter = ~ gene_biotype == "lincRNA")

To get an overview of database tables and available columns the function listTables can be used. The method listColumns on the other hand lists columns for the specified database table.

## list all database tables along with their columns

## list columns from a specific table
listColumns(edb, "tx")

Thus, we could retrieve all transcripts of the biotype nonsense_mediated_decay (which, according to the definitions by Ensembl are transcribed, but most likely not translated in a protein, but rather degraded after transcription) along with the name of the gene for each transcript. Note that we are changing here the return.type to DataFrame, so the method will return a DataFrame with the results instead of the default GRanges.

Tx <- transcripts(edb,
          columns = c(listColumns(edb , "tx"), "gene_name"),
          filter = TxBiotypeFilter("nonsense_mediated_decay"),
          return.type = "DataFrame")

For protein coding transcripts, we can also specifically extract their coding region. In the example below we extract the CDS for all transcripts encoded on chromosome Y.

yCds <- cdsBy(edb, filter = SeqNameFilter("Y"))

Using a GRangesFilter we can retrieve all features from the database that are either within or overlapping the specified genomic region. In the example below we query all genes that are partially overlapping with a small region on chromosome 11. The filter restricts to all genes for which either an exon or an intron is partially overlapping with the region.

## Define the filter
grf <- GRangesFilter(GRanges("11", ranges = IRanges(114129278, 114129328),
                 strand = "+"), type = "any")

## Query genes:
gn <- genes(edb, filter = grf)

## Next we retrieve all transcripts for that gene so that we can plot them.
txs <- transcripts(edb, filter = GeneNameFilter(gn$gene_name))
plot(3, 3, pch = NA, xlim = c(start(gn), end(gn)), ylim = c(0, length(txs)),
     yaxt = "n", ylab = "")
## Highlight the GRangesFilter region
rect(xleft = start(grf), xright = end(grf), ybottom = 0, ytop = length(txs),
     col = "red", border = "red")
for(i in 1:length(txs)) {
    current <- txs[i]
    rect(xleft = start(current), xright = end(current), ybottom = i-0.975,
         ytop = i-0.125, border = "grey")
    text(start(current), y = i-0.5, pos = 4, cex = 0.75, labels = current$tx_id)

As we can see, 4 transcripts of the gene ZBTB16 are also overlapping the region. Below we fetch these 4 transcripts. Note, that a call to exons will not return any features from the database, as no exon is overlapping with the region.

transcripts(edb, filter = grf)

The GRangesFilter supports also GRanges defining multiple regions and a query will return all features overlapping any of these regions. Besides using the GRangesFilter it is also possible to search for transcripts or exons overlapping genomic regions using the exonsByOverlaps or transcriptsByOverlaps known from the GenomicFeatures package. Note that the implementation of these methods for EnsDb objects supports also to use filters to further fine-tune the query.

The functions listGenebiotypes and listTxbiotypes can be used to get an overview of allowed/available gene and transcript biotype

## Get all gene biotypes from the database. The GeneBiotypeFilter
## allows to filter on these values.

## Get all transcript biotypes from the database.

Data can be fetched in an analogous way using the exons and genes methods. In the example below we retrieve gene_name, entrezid and the gene_biotype of all genes in the database which names start with "BCL2".

## We're going to fetch all genes which names start with BCL.
BCLs <- genes(edb,
          columns = c("gene_name", "entrezid", "gene_biotype"),
          filter = GeneNameFilter("BCL", condition = "startsWith"),
          return.type = "DataFrame")

Sometimes it might be useful to know the length of genes or transcripts (i.e. the total sum of nucleotides covered by their exons). Below we calculate the mean length of transcripts from protein coding genes on chromosomes X and Y as well as the average length of snoRNA, snRNA and rRNA transcripts encoded on these chromosomes. For the first query we combine two AnnotationFilter objects using an AnnotationFilterList object, in the second we define the query using a filter expression.

## determine the average length of snRNA, snoRNA and rRNA genes encoded on
## chromosomes X and Y.
mean(lengthOf(edb, of = "tx",
          filter = AnnotationFilterList(
          GeneBiotypeFilter(c("snRNA", "snoRNA", "rRNA")),
          SeqNameFilter(c("X", "Y")))))

## determine the average length of protein coding genes encoded on the same
## chromosomes.
mean(lengthOf(edb, of = "tx",
          filter = ~ gene_biotype == "protein_coding" &
          seq_name %in% c("X", "Y")))

Not unexpectedly, transcripts of protein coding genes are longer than those of snRNA, snoRNA or rRNA genes.

At last we extract the first two exons of each transcript model from the database.

## Extract all exons 1 and (if present) 2 for all genes encoded on the
## Y chromosome
exons(edb, columns = c("tx_id", "exon_idx"),
      filter = list(SeqNameFilter("Y"),
                    ExonRankFilter(3, condition = "<")))

Extracting gene/transcript/exon models for RNASeq feature counting

For the feature counting step of an RNAseq experiment, the gene or transcript models (defined by the chromosomal start and end positions of their exons) have to be known. To extract these from an Ensembl based annotation package, the exonsBy, genesBy and transcriptsBy methods can be used in an analogous way as in TxDb packages generated by the GenomicFeatures package. However, the transcriptsBy method does not, in contrast to the method in the GenomicFeatures package, allow to return transcripts by "cds". While the annotation packages built by the ensembldb contain the chromosomal start and end coordinates of the coding region (for protein coding genes) they do not assign an ID to each CDS.

A simple use case is to retrieve all genes encoded on chromosomes X and Y from the database.

TxByGns <- transcriptsBy(edb, by = "gene", filter = SeqNameFilter(c("X", "Y")))

Since Ensembl contains also definitions of genes that are on chromosome variants (supercontigs), it is advisable to specify the chromosome names for which the gene models should be returned.

In a real use case, we might thus want to retrieve all genes encoded on the standard chromosomes. In addition it is advisable to use a GeneIdFilter to restrict to Ensembl genes only, as also LRG (Locus Reference Genomic) genes2 are defined in the database, which are partially redundant with Ensembl genes.

## will just get exons for all genes on chromosomes 1 to 22, X and Y.
## Note: want to get rid of the "LRG" genes!!!
EnsGenes <- exonsBy(edb, by = "gene", filter = AnnotationFilterList(
                      SeqNameFilter(c(1:22, "X", "Y")),
                      GeneIdFilter("ENSG", "startsWith")))

The code above returns a GRangesList that can be used directly as an input for the summarizeOverlaps function from the GenomicAlignments package 3.

Alternatively, the above GRangesList can be transformed to a data.frame in SAF format that can be used as an input to the featureCounts function of the Rsubread package 4.

## Transforming the GRangesList into a data.frame in SAF format
EnsGenes.SAF <- toSAF(EnsGenes)

Note that the ID by which the GRangesList is split is used in the SAF formatted data.frame as the GeneID. In the example below this would be the Ensembl gene IDs, while the start, end coordinates (along with the strand and chromosomes) are those of the the exons.

In addition, the disjointExons function (similar to the one defined in GenomicFeatures) can be used to generate a GRanges of non-overlapping exon parts which can be used in the DEXSeq package.

## Create a GRanges of non-overlapping exon parts.
DJE <- disjointExons(edb, filter = AnnotationFilterList(
                  SeqNameFilter(c(1:22, "X", "Y")),
                  GeneIdFilter("ENSG%", "startsWith")))

Retrieving sequences for gene/transcript/exon models

The methods to retrieve exons, transcripts and genes (i.e. exons, transcripts and genes) return by default GRanges objects that can be used to retrieve sequences using the getSeq method e.g. from BSgenome packages. The basic workflow is thus identical to the one for TxDb packages, however, it is not straight forward to identify the BSgenome package with the matching genomic sequence. Most BSgenome packages are named according to the genome build identifier used in UCSC which does not (always) match the genome build name used by Ensembl. Using the Ensembl version provided by the EnsDb, the correct genomic sequence can however be retrieved easily from the AnnotationHub using the getGenomeFaFile. If no Fasta file matching the Ensembl version is available, the function tries to identify a Fasta file with the correct genome build from the closest Ensembl release and returns that instead.

In the code block below we retrieve first the FaFile with the genomic DNA sequence, extract the genomic start and end coordinates for all genes defined in the package, subset to genes encoded on sequences available in the FaFile and extract all of their sequences. Note: these sequences represent the sequence between the chromosomal start and end coordinates of the gene.

edb <- EnsDb.Hsapiens.v86

## Get the FaFile with the genomic sequence matching the Ensembl version
## using the AnnotationHub package.
Dna <- getGenomeFaFile(edb)

## Get start/end coordinates of all genes.
genes <- genes(edb)
## Subset to all genes that are encoded on chromosomes for which
## we do have DNA sequence available.
genes <- genes[seqnames(genes) %in% seqnames(seqinfo(Dna))]

## Get the gene sequences, i.e. the sequence including the sequence of
## all of the gene's exons and introns.
geneSeqs <- getSeq(Dna, genes)

To retrieve the (exonic) sequence of transcripts (i.e. without introns) we can use directly the extractTranscriptSeqs method defined in the GenomicFeatures on the EnsDb object, eventually using a filter to restrict the query.

## get all exons of all transcripts encoded on chromosome Y
yTx <- exonsBy(edb, filter = SeqNameFilter("Y"))

## Retrieve the sequences for these transcripts from the FaFile.
yTxSeqs <- extractTranscriptSeqs(Dna, yTx)

## Extract the sequences of all transcripts encoded on chromosome Y.
yTx <- extractTranscriptSeqs(Dna, edb, filter = SeqNameFilter("Y"))

## Along these lines, we could use the method also to retrieve the coding sequence
## of all transcripts on the Y chromosome.
cdsY <- cdsBy(edb, filter = SeqNameFilter("Y"))
extractTranscriptSeqs(Dna, cdsY)

Next we retrieve transcript sequences from genes encoded on chromosome Y using the BSGenome package for the human genome. Ensembl version 86 based on the GRCh38 genome build and we thus load the corresponding BSGenome package.

bsg <- BSgenome.Hsapiens.NCBI.GRCh38

## Get the genome version

## Extract the full transcript sequences.
yTxSeqs <- extractTranscriptSeqs(bsg, exonsBy(edb, "tx",
                          filter = SeqNameFilter("Y")))


## Extract just the CDS
Test <- cdsBy(edb, "tx", filter = SeqNameFilter("Y"))
yTxCds <- extractTranscriptSeqs(bsg, cdsBy(edb, "tx",
                       filter = SeqNameFilter("Y")))

Integrating annotations from Ensembl based EnsDb packages with UCSC based annotations

Sometimes it might be useful to combine (Ensembl based) annotations from EnsDb packages/objects with annotations from other Bioconductor packages, that might base on UCSC annotations. To support such an integration of annotations, the ensembldb packages implements the seqlevelsStyle and seqlevelsStyle<- from the GenomeInfoDb package that allow to change the style of chromosome naming. Thus, sequence/chromosome names other than those used by Ensembl can be used in, and are returned by, the queries to EnsDb objects as long as a mapping for them is provided by the GenomeInfoDb package (which provides a mapping mostly between UCSC, NCBI and Ensembl chromosome names for the main chromosomes).

In the example below we change the seqnames style to UCSC.

## Change the seqlevels style form Ensembl (default) to UCSC:
seqlevelsStyle(edb) <- "UCSC"

## Now we can use UCSC style seqnames in SeqNameFilters or GRangesFilter:
genesY <- genes(edb, filter = ~ seq_name == "chrY")
## The seqlevels of the returned GRanges are also in UCSC style

Note that in most instances no mapping is available for sequences not corresponding to the main chromosomes (i.e. contigs, patched chromosomes etc). What is returned in cases in which no mapping is available can be specified with the global ensembldb.seqnameNotFound option. By default (with ensembldb.seqnameNotFound set to "ORIGINAL"), the original seqnames (i.e. the ones from Ensembl) are returned. With ensembldb.seqnameNotFound "MISSING" each time a seqname can not be found an error is thrown. For all other cases (e.g. ensembldb.seqnameNotFound = NA) the value of the option is returned.

seqlevelsStyle(edb) <- "UCSC"

## Getting the default option:

## Listing all seqlevels in the database.

## Setting the option to NA, thus, for each seqname for which no mapping is available,
## NA is returned.

## Resetting the option.
options(ensembldb.seqnameNotFound = "ORIGINAL")

At last changing the seqname style to the default value "Ensembl".

seqlevelsStyle(edb) <- "Ensembl"

Interactive annotation lookup using the shiny web app

In addition to the genes, transcripts and exons methods it is possibly to search interactively for gene/transcript/exon annotations using the internal, shiny based, web application. The application can be started with the runEnsDbApp() function. The search results from this app can also be returned to the R workspace either as a data.frame or GRanges object.

Plotting gene/transcript features using ensembldb and Gviz and ggbio

The Gviz package provides functions to plot genes and transcripts along with other data on a genomic scale. Gene models can be provided either as a data.frame, GRanges, TxDB database, can be fetched from biomart and can also be retrieved from ensembldb.

Below we generate a GeneRegionTrack fetching all transcripts from a certain region on chromosome Y.

Note that if we want in addition to work also with BAM files that were aligned against DNA sequences retrieved from Ensembl or FASTA files representing genomic DNA sequences from Ensembl we should change the ucscChromosomeNames option from Gviz to FALSE (i.e. by calling options(ucscChromosomeNames = FALSE)). This is not necessary if we just want to retrieve gene models from an EnsDb object, as the ensembldb package internally checks the ucscChromosomeNames option and, depending on that, maps Ensembl chromosome names to UCSC chromosome names.

## Loading the Gviz library
edb <- EnsDb.Hsapiens.v86

## Retrieving a Gviz compatible GRanges object with all genes
## encoded on chromosome Y.
gr <- getGeneRegionTrackForGviz(edb, chromosome = "Y",
                start = 20400000, end = 21400000)
## Define a genome axis track
gat <- GenomeAxisTrack()

## We have to change the ucscChromosomeNames option to FALSE to enable Gviz usage
## with non-UCSC chromosome names.
options(ucscChromosomeNames = FALSE)

plotTracks(list(gat, GeneRegionTrack(gr)))

options(ucscChromosomeNames = TRUE)

Above we had to change the option ucscChromosomeNames to FALSE in order to use it with non-UCSC chromosome names. Alternatively, we could however also change the seqnamesStyle of the EnsDb object to UCSC. Note that we have to use now also chromosome names in the UCSC style in the SeqNameFilter (i.e. "chrY" instead of "Y").

seqlevelsStyle(edb) <- "UCSC"
## Retrieving the GRanges objects with seqnames corresponding to UCSC chromosome names.
gr <- getGeneRegionTrackForGviz(edb, chromosome = "chrY",
                                start = 20400000, end = 21400000)
## Define a genome axis track
gat <- GenomeAxisTrack()
plotTracks(list(gat, GeneRegionTrack(gr)))

We can also use the filters from the ensembldb package to further refine what transcripts are fetched, like in the example below, in which we create two different gene region tracks, one for protein coding genes and one for lincRNAs.

protCod <- getGeneRegionTrackForGviz(edb, chromosome = "chrY",
                     start = 20400000, end = 21400000,
                     filter = GeneBiotypeFilter("protein_coding"))
lincs <- getGeneRegionTrackForGviz(edb, chromosome = "chrY",
                   start = 20400000, end = 21400000,
                   filter = GeneBiotypeFilter("lincRNA"))

plotTracks(list(gat, GeneRegionTrack(protCod, name = "protein coding"),
        GeneRegionTrack(lincs, name = "lincRNAs")), transcriptAnnotation = "symbol")

## At last we change the seqlevels style again to Ensembl
seqlevelsStyle <- "Ensembl"

Alternatively, we can also use ggbio for plotting. For ggbio we can directly pass the EnsDb object along with optional filters (or as in the example below a filter expression as a formula).


## Create a plot for all transcripts of the gene SKA2
autoplot(edb, ~ gene_name == "SKA2")

To plot the genomic region and plot genes from both strands we can use a GRangesFilter.

## Get the chromosomal region in which the gene is encoded
ska2 <- genes(edb, filter = ~ gene_name == "SKA2")
strand(ska2) <- "*"
autoplot(edb, GRangesFilter(ska2), names.expr = "gene_name")

Using EnsDb objects in the AnnotationDbi framework

Most of the methods defined for objects extending the basic annotation package class AnnotationDbi are also defined for EnsDb objects (i.e. methods columns, keytypes, keys, mapIds and select). While these methods can be used analogously to basic annotation packages, the implementation for EnsDb objects also support the filtering framework of the ensembldb package.

In the example below we first evaluate all the available columns and keytypes in the database and extract then the gene names for all genes encoded on chromosome X.

edb <- EnsDb.Hsapiens.v86

## List all available columns in the database.

## Note that these do *not* correspond to the actual column names
## of the database that can be passed to methods like exons, genes,
## transcripts etc. These column names can be listed with the listColumns
## method.

## List all of the supported key types.

## Get all gene ids from the database.
gids <- keys(edb, keytype = "GENEID")

## Get all gene names for genes encoded on chromosome Y.
gnames <- keys(edb, keytype = "GENENAME", filter = SeqNameFilter("Y"))

In the next example we retrieve specific information from the database using the select method. First we fetch all transcripts for the genes BCL2 and BCL2L11. In the first call we provide the gene names, while in the second call we employ the filtering system to perform a more fine-grained query to fetch only the protein coding transcripts for these genes.

## Use the /standard/ way to fetch data.
select(edb, keys = c("BCL2", "BCL2L11"), keytype = "GENENAME",
       columns = c("GENEID", "GENENAME", "TXID", "TXBIOTYPE"))

## Use the filtering system of ensembldb
select(edb, keys = ~ gene_name %in% c("BCL2", "BCL2L11") &
        tx_biotype == "protein_coding",
       columns = c("GENEID", "GENENAME", "TXID", "TXBIOTYPE"))

Finally, we use the mapIds method to establish a mapping between ids and values. In the example below we fetch transcript ids for the two genes from the example above.

## Use the default method, which just returns the first value for multi mappings.
mapIds(edb, keys = c("BCL2", "BCL2L11"), column = "TXID", keytype = "GENENAME")

## Alternatively, specify multiVals="list" to return all mappings.
mapIds(edb, keys = c("BCL2", "BCL2L11"), column = "TXID", keytype = "GENENAME",
       multiVals = "list")

## And, just like before, we can use filters to map only to protein coding transcripts.
mapIds(edb, keys = list(GeneNameFilter(c("BCL2", "BCL2L11")),
                        TxBiotypeFilter("protein_coding")), column = "TXID",
       multiVals = "list")

Note that, if the filters are used, the ordering of the result does no longer match the ordering of the genes.

Important notes

These notes might explain eventually unexpected results (and, more importantly, help avoiding them):

Getting or building EnsDb databases/packages

Some of the code in this section is not supposed to be automatically executed when the vignette is built, as this would require a working installation of the Ensembl Perl API, which is not expected to be available on each system. Also, building EnsDb from alternative sources, like GFF or GTF files takes some time and thus also these examples are not directly executed when the vignette is build.

Getting EnsDb databases

Some EnsDb databases are available as R packages from Bioconductor and can be simply installed with the install function from the BiocManager package. The name of such annotation packages starts with EnsDb followed by the abbreviation of the organism and the Ensembl version on which the annotation bases. EnsDb.Hsapiens.v86 provides thus an EnsDb database for homo sapiens with annotations from Ensembl version 86.

Since Bioconductor version 3.5 EnsDb databases can also be retrieved directly from AnnotationHub.

## Load the annotation resource.
ah <- AnnotationHub()

## Query for all available EnsDb databases
query(ah, "EnsDb")

We can simply fetch one of the databases.

ahDb <- query(ah, pattern = c("Xiphophorus Maculatus", "EnsDb", 87))
## What have we got

Fetch the EnsDb and use it.

ahEdb <- ahDb[[1]]

## retriebe all genes
gns <- genes(ahEdb)

We could even make an annotation package from this EnsDb object using the makeEnsembldbPackage and passing dbfile(dbconn(ahEdb)) as ensdb argument.

Building annotation packages

Directly from Ensembl databases

The fetchTablesFromEnsembl function uses the Ensembl Perl API to retrieve the required annotations from an Ensembl database (e.g. from the main site Thus, to use this functionality to build databases, the Ensembl Perl API needs to be installed (see 5 for details).

Below we create an EnsDb database by fetching the required data directly from the Ensembl core databases. The makeEnsembldbPackage function is then used to create an annotation package from this EnsDb containing all human genes for Ensembl version 75.


## get all human gene/transcript/exon annotations from Ensembl (75)
## the resulting tables will be stored by default to the current working
## directory
fetchTablesFromEnsembl(75, species = "human")

## These tables can then be processed to generate a SQLite database
## containing the annotations (again, the function assumes the required
## txt files to be present in the current working directory)
DBFile <- makeEnsemblSQLiteFromTables()

## and finally we can generate the package
makeEnsembldbPackage(ensdb = DBFile, version = "0.99.12",
                     maintainer = "Johannes Rainer <[email protected]>",
                     author = "J Rainer")

The generated package can then be build using R CMD build EnsDb.Hsapiens.v75 and installed with R CMD INSTALL EnsDb.Hsapiens.v75*. Note that we could directly generate an EnsDb instance by loading the database file, i.e. by calling edb <- EnsDb(DBFile) and work with that annotation object.

To fetch and build annotation packages for plant genomes (e.g. arabidopsis thaliana), the Ensembl genomes should be specified as a host, i.e. setting host to "", port to 4157 and species to e.g. "arabidopsis thaliana".

From a GTF or GFF file

Alternatively, the ensDbFromAH, ensDbFromGff, ensDbFromGRanges and ensDbFromGtf functions allow to build EnsDb SQLite files from a GRanges object or GFF/GTF files from Ensembl (either provided as files or via AnnotationHub). These functions do not depend on the Ensembl Perl API, but require a working internet connection to fetch the chromosome lengths from Ensembl as these are not provided within GTF or GFF files. Also note that protein annotations are usually not available in GTF or GFF files, thus, such annotations will not be included in the generated EnsDb database - protein annotations are only available in EnsDb databases created with the Ensembl Perl API (such as the ones provided through AnnotationHub or as Bioconductor packages).

In the next example we create an EnsDb database using the AnnotationHub package and load also the corresponding genomic DNA sequence matching the Ensembl version. We thus first query the AnnotationHub package for all resources available for Mus musculus and the Ensembl release 77. Next we create the EnsDb object from the appropriate AnnotationHub resource. We then use the getGenomeFaFile method on the EnsDb to directly look up and retrieve the correct or best matching FaFile with the genomic DNA sequence. At last we retrieve the sequences of all exons using the getSeq method.

## Load the AnnotationHub data.
ah <- AnnotationHub()

## Query all available files for Ensembl release 77 for
## Mus musculus.
query(ah, c("Mus musculus", "release-77"))

## Get the resource for the gtf file with the gene/transcript definitions.
Gtf <- ah["AH28822"]
## Create a EnsDb database file from this.
DbFile <- ensDbFromAH(Gtf)
## We can either generate a database package, or directly load the data
edb <- EnsDb(DbFile)

## Identify and get the FaFile object with the genomic DNA sequence matching
## the EnsDb annotation.
Dna <- getGenomeFaFile(edb)
## We next retrieve the sequence of all exons on chromosome Y.
exons <- exons(edb, filter = SeqNameFilter("Y"))
exonSeq <- getSeq(Dna, exons)

## Alternatively, look up and retrieve the toplevel DNA sequence manually.
Dna <- ah[["AH22042"]]

In the example below we load a GRanges containing gene definitions for genes encoded on chromosome Y and generate a EnsDb SQLite database from that information.

## Generate a sqlite database from a GRanges object specifying
## genes encoded on chromosome Y
load(system.file("YGRanges.RData", package = "ensembldb"))

## Create the EnsDb database file
DB <- ensDbFromGRanges(Y, path = tempdir(), version = 75,
               organism = "Homo_sapiens")

## Load the database
edb <- EnsDb(DB)

Alternatively we can build the annotation database using the ensDbFromGtf ensDbFromGff functions, that extract most of the required data from a GTF respectively GFF (version 3) file which can be downloaded from Ensembl (e.g. from for human gene definitions from Ensembl version 75; for plant genomes etc, files can be retrieved from All information except the chromosome lengths, the NCBI Entrezgene IDs and protein annotations can be extracted from these GTF files. The function also tries to retrieve chromosome length information automatically from Ensembl.

Below we create the annotation from a gtf file that we fetch directly from Ensembl.


## the GTF file can be downloaded from
gtffile <- "Homo_sapiens.GRCh37.75.gtf.gz"
## generate the SQLite database file
DB <- ensDbFromGtf(gtf = gtffile)

## load the DB file directly
EDB <- EnsDb(DB)

## alternatively, build the annotation package
## and finally we can generate the package
makeEnsembldbPackage(ensdb = DB, version = "0.99.12",
                     maintainer = "Johannes Rainer <[email protected]>",
                     author = "J Rainer")

Database layout

The database consists of the following tables and attributes (the layout is also shown in Figure 165). Note that the protein-specific annotations might not be available in all EnsDB databases (e.g. such ones created with ensembldb version < 1.7 or created from GTF or GFF files).

The database layout: as already described above, protein related annotations (green) might not be available in each EnsDb database.


Session information








jotsetung/ensembldb documentation built on Dec. 9, 2018, 10:15 a.m.