ensembldb 2.30.0
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
.
library(EnsDb.Hsapiens.v86)
## Making a "short cut"
edb <- EnsDb.Hsapiens.v86
## print some informations for this package
edb
## EnsDb for Ensembl:
## |Backend: SQLite
## |Db type: EnsDb
## |Type of Gene ID: Ensembl Gene ID
## |Supporting package: ensembldb
## |Db created by: ensembldb package from Bioconductor
## |script_version: 0.3.0
## |Creation time: Thu May 18 16:32:27 2017
## |ensembl_version: 86
## |ensembl_host: localhost
## |Organism: homo_sapiens
## |taxonomy_id: 9606
## |genome_build: GRCh38
## |DBSCHEMAVERSION: 2.0
## | No. of genes: 63970.
## | No. of transcripts: 216741.
## |Protein data available.
## For what organism was the database generated?
organism(edb)
## [1] "Homo sapiens"
ensembldb
annotation packages to retrieve specific annotationsOne 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.
supportedFilters(edb)
## filter field
## 1 EntrezFilter entrez
## 2 ExonEndFilter exon_end
## 3 ExonIdFilter exon_id
## 4 ExonRankFilter exon_rank
## 5 ExonStartFilter exon_start
## 6 GRangesFilter <NA>
## 7 GeneBiotypeFilter gene_biotype
## 8 GeneEndFilter gene_end
## 9 GeneIdFilter gene_id
## 10 GeneNameFilter gene_name
## 11 GeneStartFilter gene_start
## 12 GenenameFilter genename
## 13 ProtDomIdFilter prot_dom_id
## 14 ProteinDomainIdFilter protein_domain_id
## 15 ProteinDomainSourceFilter protein_domain_source
## 16 ProteinIdFilter protein_id
## 17 SeqNameFilter seq_name
## 18 SeqStrandFilter seq_strand
## 19 SymbolFilter symbol
## 20 TxBiotypeFilter tx_biotype
## 21 TxEndFilter tx_end
## 22 TxIdFilter tx_id
## 23 TxNameFilter tx_name
## 24 TxStartFilter tx_start
## 25 UniprotDbFilter uniprot_db
## 26 UniprotFilter uniprot
## 27 UniprotMappingTypeFilter uniprot_mapping_type
These filters can be divided into 3 main filter types:
IntegerFilter
: filter classes extending this basic object can take a single
numeric value as input and support the conditions ==
, !=
, >
, <
, >=
and <=
. All filters that work on chromosomal coordinates, such as the
GeneEndFilter
extend IntegerFilter
.CharacterFilter
: filter classes extending this object can take a single or
multiple character values as input and allow conditions: ==
, !=
,
"startsWith"
, "endsWith"
and "contains"
. All filters working on IDs
extend this class.GRangesFilter
: takes a GRanges
object as input and supports all conditions
that findOverlaps
from the IRanges
package supports ("any"
, "start"
,
"end"
, "within"
, "equal"
). Note that these have to be passed using the
parameter type
to the constructor function.The supported filters are:
EntrezFilter
: allows to filter results based on NCBI Entrezgene identifiers
of the genes.ExonEndFilter
: filter using the chromosomal end coordinate of exons.ExonIdFilter
: filter based on the (Ensembl) exon identifiers.ExonRankFilter
: filter based on the rank (index) of an exon within the
transcript model. Exons are always numbered from 5’ to 3’ end of the
transcript, thus, also on the reverse strand, the exon 1 is the most 5’ exon
of the transcript.ExonStartFilter
: filter using the chromosomal start coordinate of exons.GeneBiotypeFilter
: filter using the gene biotypes defined in the Ensembl
database; use the listGenebiotypes
method to list all available biotypes.GeneEndFilter
: filter using the chromosomal end coordinate of gene.GeneIdFilter
: filter based on the Ensembl gene IDs.GeneNameFilter
: filter based on the names (symbols) of the genes.GeneStartFilter
: filter using the chromosomal start coordinate of gene.GRangesFilter
: allows to retrieve all features (genes, transcripts or exons)
that are either within (setting parameter type
to “within”) or partially
overlapping (setting type
to “any”) the defined genomic region/range. Note
that, depending on the called method (genes
, transcripts
or exons
) the
start and end coordinates of either the genes, transcripts or exons are used
for the filter. For methods exonsBy
, cdsBy
and txBy
the coordinates of
by
are used.SeqNameFilter
: filter by the name of the chromosomes the genes are encoded
on.SeqStrandFilter
: filter for the chromosome strand on which the genes are
encoded.SymbolFilter
: filter on gene symbols; note that no database columns symbol
is available in an EnsDb
database and hence the gene name is used for
filtering.TxBiotypeFilter
: filter on the transcript biotype defined in Ensembl; use
the listTxbiotypes
method to list all available biotypes.TxEndFilter
: filter using the chromosomal end coordinate of transcripts.TxIdFilter
: filter on the Ensembl transcript identifiers.TxNameFilter
: to be compliant with TxDb
annotation resources from the
GenomicFeatures
package, the tx_name
database column contains also the
transcript IDs and hence the TxNameFilter
is identical to the
TxIdFilter
. Transcript names (external name in the Ensembl databases) are
provided in column "tx_external_name"
.TxExternalNameFilter
: filter based on the transcript names which are
provided by Ensembl with the external name attribute (or are listed in the
"transcript_name"
field in GTF files.TxStartFilter
: filter using the chromosomal start coordinate of transcripts.In addition to the above listed DNA-RNA-based filters, protein-specific filters are also available:
ProtDomIdFilter
, ProteinDomainIdFilter
: filter by the protein domain ID.ProteinDomainSourceFilter
: filter by the source of the protein domain
(database or method, e.g. pfam).ProteinIdFilter
: filter by Ensembl protein ID filters.UniprotDbFilter
: filter by the name of the Uniprot database.UniprotFilter
: filter by the Uniprot ID.UniprotMappingTypeFilter
: filter by the mapping type of Ensembl protein IDs
to Uniprot IDs.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"))
Tx
## GRanges object with 28 ranges and 7 metadata columns:
## seqnames ranges strand | tx_id
## <Rle> <IRanges> <Rle> | <character>
## ENST00000432179 2 111119378-111124112 + | ENST00000432179
## ENST00000308659 2 111120914-111165048 + | ENST00000308659
## ENST00000337565 2 111120914-111128844 + | ENST00000337565
## ENST00000622509 2 111120914-111168445 + | ENST00000622509
## ENST00000619294 2 111120914-111168445 + | ENST00000619294
## ... ... ... ... . ...
## ENST00000452231 2 111123746-111164231 + | ENST00000452231
## ENST00000361493 2 111123746-111164231 + | ENST00000361493
## ENST00000431217 2 111123746-111164352 + | ENST00000431217
## ENST00000439718 2 111123746-111164643 + | ENST00000439718
## ENST00000438054 2 111123752-111146284 + | ENST00000438054
## tx_biotype tx_cds_seq_start tx_cds_seq_end
## <character> <integer> <integer>
## ENST00000432179 protein_coding 111123746 111124112
## ENST00000308659 protein_coding 111123746 111164231
## ENST00000337565 protein_coding 111123746 111128751
## ENST00000622509 protein_coding 111123746 111161439
## ENST00000619294 protein_coding 111123746 111144501
## ... ... ... ...
## ENST00000452231 nonsense_mediated_de.. 111123746 111161439
## ENST00000361493 nonsense_mediated_de.. 111123746 111130235
## ENST00000431217 nonsense_mediated_de.. 111123746 111144501
## ENST00000439718 nonsense_mediated_de.. 111123746 111151851
## ENST00000438054 protein_coding 111123752 111144491
## gene_id tx_name gene_name
## <character> <character> <character>
## ENST00000432179 ENSG00000153094 ENST00000432179 BCL2L11
## ENST00000308659 ENSG00000153094 ENST00000308659 BCL2L11
## ENST00000337565 ENSG00000153094 ENST00000337565 BCL2L11
## ENST00000622509 ENSG00000153094 ENST00000622509 BCL2L11
## ENST00000619294 ENSG00000153094 ENST00000619294 BCL2L11
## ... ... ... ...
## ENST00000452231 ENSG00000153094 ENST00000452231 BCL2L11
## ENST00000361493 ENSG00000153094 ENST00000361493 BCL2L11
## ENST00000431217 ENSG00000153094 ENST00000431217 BCL2L11
## ENST00000439718 ENSG00000153094 ENST00000439718 BCL2L11
## ENST00000438054 ENSG00000153094 ENST00000438054 BCL2L11
## -------
## seqinfo: 1 sequence from GRCh38 genome
## as this is a GRanges object we can access e.g. the start coordinates with
head(start(Tx))
## [1] 111119378 111120914 111120914 111120914 111120914 111120914
## or extract the biotype with
head(Tx$tx_biotype)
## [1] "protein_coding" "protein_coding" "protein_coding" "protein_coding"
## [5] "protein_coding" "protein_coding"
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")
## GRanges object with 9 ranges and 7 metadata columns:
## seqnames ranges strand | tx_id
## <Rle> <IRanges> <Rle> | <character>
## ENST00000335953 11 114059593-114250676 + | ENST00000335953
## ENST00000541602 11 114059725-114189764 + | ENST00000541602
## ENST00000544220 11 114059737-114063646 + | ENST00000544220
## ENST00000535700 11 114060257-114063744 + | ENST00000535700
## ENST00000392996 11 114060507-114250652 + | ENST00000392996
## ENST00000539918 11 114064412-114247344 + | ENST00000539918
## ENST00000545851 11 114180766-114247296 + | ENST00000545851
## ENST00000535379 11 114237207-114250557 + | ENST00000535379
## ENST00000535509 11 114246790-114250476 + | ENST00000535509
## tx_biotype tx_cds_seq_start tx_cds_seq_end
## <character> <integer> <integer>
## ENST00000335953 protein_coding 114063301 114250555
## ENST00000541602 retained_intron <NA> <NA>
## ENST00000544220 protein_coding 114063301 114063646
## ENST00000535700 protein_coding 114063301 114063744
## ENST00000392996 protein_coding 114063301 114250555
## ENST00000539918 nonsense_mediated_de.. 114064412 114121827
## ENST00000545851 processed_transcript <NA> <NA>
## ENST00000535379 processed_transcript <NA> <NA>
## ENST00000535509 retained_intron <NA> <NA>
## gene_id tx_name gene_name
## <character> <character> <character>
## ENST00000335953 ENSG00000109906 ENST00000335953 ZBTB16
## ENST00000541602 ENSG00000109906 ENST00000541602 ZBTB16
## ENST00000544220 ENSG00000109906 ENST00000544220 ZBTB16
## ENST00000535700 ENSG00000109906 ENST00000535700 ZBTB16
## ENST00000392996 ENSG00000109906 ENST00000392996 ZBTB16
## ENST00000539918 ENSG00000109906 ENST00000539918 ZBTB16
## ENST00000545851 ENSG00000109906 ENST00000545851 ZBTB16
## ENST00000535379 ENSG00000109906 ENST00000535379 ZBTB16
## ENST00000535509 ENSG00000109906 ENST00000535509 ZBTB16
## -------
## seqinfo: 1 sequence from GRCh38 genome
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 |>
(pile operator).
edb |>
filter(~ symbol == "BCL2" & tx_biotype != "protein_coding") |>
transcripts()
## GRanges object with 1 range and 6 metadata columns:
## seqnames ranges strand | tx_id
## <Rle> <IRanges> <Rle> | <character>
## ENST00000590515 18 63128212-63161869 - | ENST00000590515
## tx_biotype tx_cds_seq_start tx_cds_seq_end
## <character> <integer> <integer>
## ENST00000590515 processed_transcript <NA> <NA>
## gene_id tx_name
## <character> <character>
## ENST00000590515 ENSG00000171791 ENST00000590515
## -------
## seqinfo: 1 sequence from GRCh38 genome
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
genes(edb_y)
## GRanges object with 523 ranges and 6 metadata columns:
## seqnames ranges strand | gene_id
## <Rle> <IRanges> <Rle> | <character>
## ENSG00000251841 Y 2784749-2784853 + | ENSG00000251841
## ENSG00000184895 Y 2786855-2787699 - | ENSG00000184895
## ENSG00000237659 Y 2789827-2790328 + | ENSG00000237659
## ENSG00000232195 Y 2827982-2828218 + | ENSG00000232195
## ENSG00000129824 Y 2841486-2932000 + | ENSG00000129824
## ... ... ... ... . ...
## ENSG00000224240 Y 26549425-26549743 + | ENSG00000224240
## ENSG00000227629 Y 26586642-26591601 - | ENSG00000227629
## ENSG00000237917 Y 26594851-26634652 - | ENSG00000237917
## ENSG00000231514 Y 26626520-26627159 - | ENSG00000231514
## ENSG00000235857 Y 56855244-56855488 + | ENSG00000235857
## gene_name gene_biotype seq_coord_system
## <character> <character> <character>
## ENSG00000251841 RNU6-1334P snRNA chromosome
## ENSG00000184895 SRY protein_coding chromosome
## ENSG00000237659 RNASEH2CP1 processed_pseudogene chromosome
## ENSG00000232195 TOMM22P2 processed_pseudogene chromosome
## ENSG00000129824 RPS4Y1 protein_coding chromosome
## ... ... ... ...
## ENSG00000224240 CYCSP49 processed_pseudogene chromosome
## ENSG00000227629 SLC25A15P1 unprocessed_pseudogene chromosome
## ENSG00000237917 PARP4P1 unprocessed_pseudogene chromosome
## ENSG00000231514 FAM58CP processed_pseudogene chromosome
## ENSG00000235857 CTBP2P1 processed_pseudogene chromosome
## symbol entrezid
## <character> <list>
## ENSG00000251841 RNU6-1334P <NA>
## ENSG00000184895 SRY 6736
## ENSG00000237659 RNASEH2CP1 <NA>
## ENSG00000232195 TOMM22P2 <NA>
## ENSG00000129824 RPS4Y1 6192
## ... ... ...
## ENSG00000224240 CYCSP49 <NA>
## ENSG00000227629 SLC25A15P1 <NA>
## ENSG00000237917 PARP4P1 <NA>
## ENSG00000231514 FAM58CP <NA>
## ENSG00000235857 CTBP2P1 <NA>
## -------
## seqinfo: 1 sequence from GRCh38 genome
## Get all lincRNAs on chromosome Y
genes(edb_y, filter = ~ gene_biotype == "lincRNA")
## GRanges object with 52 ranges and 6 metadata columns:
## seqnames ranges strand | gene_id
## <Rle> <IRanges> <Rle> | <character>
## ENSG00000278847 Y 2934406-2934771 - | ENSG00000278847
## ENSG00000231535 Y 3002912-3102272 + | ENSG00000231535
## ENSG00000229308 Y 4036497-4100320 + | ENSG00000229308
## ENSG00000277930 Y 4993858-4999650 - | ENSG00000277930
## ENSG00000237069 Y 6242446-6243610 - | ENSG00000237069
## ... ... ... ... . ...
## ENSG00000228296 Y 25063083-25099892 - | ENSG00000228296
## ENSG00000223641 Y 25183643-25184773 - | ENSG00000223641
## ENSG00000228786 Y 25378300-25394719 - | ENSG00000228786
## ENSG00000240450 Y 25482908-25486705 + | ENSG00000240450
## ENSG00000231141 Y 25728490-25733388 + | ENSG00000231141
## gene_name gene_biotype seq_coord_system symbol
## <character> <character> <character> <character>
## ENSG00000278847 RP11-414C23.1 lincRNA chromosome RP11-414C23.1
## ENSG00000231535 LINC00278 lincRNA chromosome LINC00278
## ENSG00000229308 AC010084.1 lincRNA chromosome AC010084.1
## ENSG00000277930 RP11-122L9.1 lincRNA chromosome RP11-122L9.1
## ENSG00000237069 TTTY23B lincRNA chromosome TTTY23B
## ... ... ... ... ...
## ENSG00000228296 TTTY4C lincRNA chromosome TTTY4C
## ENSG00000223641 TTTY17C lincRNA chromosome TTTY17C
## ENSG00000228786 LINC00266-4P lincRNA chromosome LINC00266-4P
## ENSG00000240450 CSPG4P1Y lincRNA chromosome CSPG4P1Y
## ENSG00000231141 TTTY3 lincRNA chromosome TTTY3
## entrezid
## <list>
## ENSG00000278847 <NA>
## ENSG00000231535 100873962
## ENSG00000229308 <NA>
## ENSG00000277930 <NA>
## ENSG00000237069 100101121,252955
## ... ...
## ENSG00000228296 474150,474149,114761
## ENSG00000223641 474152,474151,252949
## ENSG00000228786 <NA>
## ENSG00000240450 114758
## ENSG00000231141 474148,114760
## -------
## seqinfo: 1 sequence from GRCh38 genome
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
listTables(edb)
## $gene
## [1] "gene_id" "gene_name" "gene_biotype" "gene_seq_start"
## [5] "gene_seq_end" "seq_name" "seq_strand" "seq_coord_system"
## [9] "symbol"
##
## $tx
## [1] "tx_id" "tx_biotype" "tx_seq_start" "tx_seq_end"
## [5] "tx_cds_seq_start" "tx_cds_seq_end" "gene_id" "tx_name"
##
## $tx2exon
## [1] "tx_id" "exon_id" "exon_idx"
##
## $exon
## [1] "exon_id" "exon_seq_start" "exon_seq_end"
##
## $chromosome
## [1] "seq_name" "seq_length" "is_circular"
##
## $protein
## [1] "tx_id" "protein_id" "protein_sequence"
##
## $uniprot
## [1] "protein_id" "uniprot_id" "uniprot_db"
## [4] "uniprot_mapping_type"
##
## $protein_domain
## [1] "protein_id" "protein_domain_id" "protein_domain_source"
## [4] "interpro_accession" "prot_dom_start" "prot_dom_end"
##
## $entrezgene
## [1] "gene_id" "entrezid"
##
## $metadata
## [1] "name" "value"
## list columns from a specific table
listColumns(edb, "tx")
## [1] "tx_id" "tx_biotype" "tx_seq_start" "tx_seq_end"
## [5] "tx_cds_seq_start" "tx_cds_seq_end" "gene_id" "tx_name"
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")
nrow(Tx)
## [1] 14423
Tx
## DataFrame with 14423 rows and 9 columns
## tx_id tx_biotype tx_seq_start tx_seq_end
## <character> <character> <integer> <integer>
## 1 ENST00000567466 nonsense_mediated_de.. 47578 49521
## 2 ENST00000397876 nonsense_mediated_de.. 53887 57372
## 3 ENST00000428730 nonsense_mediated_de.. 58062 65039
## 4 ENST00000417043 nonsense_mediated_de.. 62973 65037
## 5 ENST00000622194 nonsense_mediated_de.. 85386 138349
## ... ... ... ... ...
## 14419 ENST00000496411 nonsense_mediated_de.. 248855728 248859018
## 14420 ENST00000483223 nonsense_mediated_de.. 248856515 248858529
## 14421 ENST00000533647 nonsense_mediated_de.. 248857273 248858324
## 14422 ENST00000528141 nonsense_mediated_de.. 248857391 248859085
## 14423 ENST00000530986 nonsense_mediated_de.. 248857469 248859085
## tx_cds_seq_start tx_cds_seq_end gene_id tx_name
## <integer> <integer> <character> <character>
## 1 48546 48893 ENSG00000261456 ENST00000567466
## 2 54017 56360 ENSG00000161981 ENST00000397876
## 3 62884 65015 ENSG00000007384 ENST00000428730
## 4 63904 65015 ENSG00000007384 ENST00000417043
## 5 117330 138267 ENSG00000103148 ENST00000622194
## ... ... ... ... ...
## 14419 248857954 248858309 ENSG00000171163 ENST00000496411
## 14420 248857954 248858309 ENSG00000171163 ENST00000483223
## 14421 248857954 248858309 ENSG00000171163 ENST00000533647
## 14422 248858004 248858309 ENSG00000171163 ENST00000528141
## 14423 248858004 248858309 ENSG00000171163 ENST00000530986
## gene_name
## <character>
## 1 TUBB8
## 2 SNRNP25
## 3 RHBDF1
## 4 RHBDF1
## 5 NPRL3
## ... ...
## 14419 ZNF692
## 14420 ZNF692
## 14421 ZNF692
## 14422 ZNF692
## 14423 ZNF692
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"))
yCds
## GRangesList object of length 151:
## $ENST00000155093
## GRanges object with 7 ranges and 3 metadata columns:
## seqnames ranges strand | seq_name exon_id exon_rank
## <Rle> <IRanges> <Rle> | <character> <character> <integer>
## [1] Y 2953937-2953997 + | Y ENSE00002223884 2
## [2] Y 2961074-2961646 + | Y ENSE00003645989 3
## [3] Y 2975095-2975244 + | Y ENSE00003764421 4
## [4] Y 2975511-2975654 + | Y ENSE00003768468 5
## [5] Y 2976670-2976822 + | Y ENSE00003766362 6
## [6] Y 2977940-2978080 + | Y ENSE00003766086 7
## [7] Y 2978810-2979993 + | Y ENSE00001368923 8
## -------
## seqinfo: 1 sequence from GRCh38 genome
##
## $ENST00000215473
## GRanges object with 2 ranges and 3 metadata columns:
## seqnames ranges strand | seq_name exon_id exon_rank
## <Rle> <IRanges> <Rle> | <character> <character> <integer>
## [1] Y 5056824-5057459 + | Y ENSE00001436852 1
## [2] Y 5098215-5100740 + | Y ENSE00003741448 2
## -------
## seqinfo: 1 sequence from GRCh38 genome
##
## $ENST00000215479
## GRanges object with 5 ranges and 3 metadata columns:
## seqnames ranges strand | seq_name exon_id exon_rank
## <Rle> <IRanges> <Rle> | <character> <character> <integer>
## [1] Y 6872555-6872608 - | Y ENSE00001671586 2
## [2] Y 6870006-6870053 - | Y ENSE00001645681 3
## [3] Y 6868732-6868776 - | Y ENSE00000652250 4
## [4] Y 6868037-6868462 - | Y ENSE00001667251 5
## [5] Y 6866073-6866078 - | Y ENSE00001494454 6
## -------
## seqinfo: 1 sequence from GRCh38 genome
##
## ...
## <148 more elements>
Using a GRangesFilter
we can retrieve all features from the database that are
either within or overlapping the specified genomic region. In the example below
we query all genes that are partially overlapping with a small region on
chromosome 11. The filter restricts to all genes for which either an exon or an
intron is partially overlapping with the region.
## Define the filter
grf <- GRangesFilter(GRanges("11", ranges = IRanges(114129278, 114129328),
strand = "+"), type = "any")
## Query genes:
gn <- genes(edb, filter = grf)
gn
## GRanges object with 1 range and 6 metadata columns:
## seqnames ranges strand | gene_id
## <Rle> <IRanges> <Rle> | <character>
## ENSG00000109906 11 114059593-114250676 + | ENSG00000109906
## gene_name gene_biotype seq_coord_system symbol
## <character> <character> <character> <character>
## ENSG00000109906 ZBTB16 protein_coding chromosome ZBTB16
## entrezid
## <list>
## ENSG00000109906 7704
## -------
## seqinfo: 1 sequence from GRCh38 genome
## Next we retrieve all transcripts for that gene so that we can plot them.
txs <- transcripts(edb, filter = GeneNameFilter(gn$gene_name))
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)
## GRanges object with 4 ranges and 6 metadata columns:
## seqnames ranges strand | tx_id
## <Rle> <IRanges> <Rle> | <character>
## ENST00000335953 11 114059593-114250676 + | ENST00000335953
## ENST00000541602 11 114059725-114189764 + | ENST00000541602
## ENST00000392996 11 114060507-114250652 + | ENST00000392996
## ENST00000539918 11 114064412-114247344 + | ENST00000539918
## tx_biotype tx_cds_seq_start tx_cds_seq_end
## <character> <integer> <integer>
## ENST00000335953 protein_coding 114063301 114250555
## ENST00000541602 retained_intron <NA> <NA>
## ENST00000392996 protein_coding 114063301 114250555
## ENST00000539918 nonsense_mediated_de.. 114064412 114121827
## gene_id tx_name
## <character> <character>
## ENST00000335953 ENSG00000109906 ENST00000335953
## ENST00000541602 ENSG00000109906 ENST00000541602
## ENST00000392996 ENSG00000109906 ENST00000392996
## ENST00000539918 ENSG00000109906 ENST00000539918
## -------
## seqinfo: 1 sequence from GRCh38 genome
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.
listGenebiotypes(edb)
## [1] "protein_coding" "unitary_pseudogene"
## [3] "unprocessed_pseudogene" "processed_pseudogene"
## [5] "processed_transcript" "transcribed_unprocessed_pseudogene"
## [7] "antisense" "transcribed_unitary_pseudogene"
## [9] "polymorphic_pseudogene" "lincRNA"
## [11] "sense_intronic" "transcribed_processed_pseudogene"
## [13] "sense_overlapping" "IG_V_pseudogene"
## [15] "pseudogene" "TR_V_gene"
## [17] "3prime_overlapping_ncRNA" "IG_V_gene"
## [19] "bidirectional_promoter_lncRNA" "snRNA"
## [21] "miRNA" "misc_RNA"
## [23] "snoRNA" "rRNA"
## [25] "Mt_tRNA" "Mt_rRNA"
## [27] "IG_C_gene" "IG_J_gene"
## [29] "TR_J_gene" "TR_C_gene"
## [31] "TR_V_pseudogene" "TR_J_pseudogene"
## [33] "IG_D_gene" "ribozyme"
## [35] "IG_C_pseudogene" "TR_D_gene"
## [37] "TEC" "IG_J_pseudogene"
## [39] "scRNA" "scaRNA"
## [41] "vaultRNA" "sRNA"
## [43] "macro_lncRNA" "non_coding"
## [45] "IG_pseudogene" "LRG_gene"
## Get all transcript biotypes from the database.
listTxbiotypes(edb)
## [1] "protein_coding" "processed_transcript"
## [3] "nonsense_mediated_decay" "retained_intron"
## [5] "unitary_pseudogene" "TEC"
## [7] "miRNA" "misc_RNA"
## [9] "non_stop_decay" "unprocessed_pseudogene"
## [11] "processed_pseudogene" "transcribed_unprocessed_pseudogene"
## [13] "lincRNA" "antisense"
## [15] "transcribed_unitary_pseudogene" "polymorphic_pseudogene"
## [17] "sense_intronic" "transcribed_processed_pseudogene"
## [19] "sense_overlapping" "IG_V_pseudogene"
## [21] "pseudogene" "TR_V_gene"
## [23] "3prime_overlapping_ncRNA" "IG_V_gene"
## [25] "bidirectional_promoter_lncRNA" "snRNA"
## [27] "snoRNA" "rRNA"
## [29] "Mt_tRNA" "Mt_rRNA"
## [31] "IG_C_gene" "IG_J_gene"
## [33] "TR_J_gene" "TR_C_gene"
## [35] "TR_V_pseudogene" "TR_J_pseudogene"
## [37] "IG_D_gene" "ribozyme"
## [39] "IG_C_pseudogene" "TR_D_gene"
## [41] "IG_J_pseudogene" "scRNA"
## [43] "scaRNA" "vaultRNA"
## [45] "sRNA" "macro_lncRNA"
## [47] "non_coding" "IG_pseudogene"
## [49] "LRG_gene"
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")
nrow(BCLs)
## [1] 30
BCLs
## DataFrame with 30 rows and 4 columns
## gene_name entrezid gene_biotype gene_id
## <character> <list> <character> <character>
## 1 BCL10 8915 protein_coding ENSG00000142867
## 2 BCL11A 53335 protein_coding ENSG00000119866
## 3 BCL11B 64919 protein_coding ENSG00000127152
## 4 BCL2 596 protein_coding ENSG00000171791
## 5 BCL2A1 597 protein_coding ENSG00000140379
## ... ... ... ... ...
## 26 BCL9L 283149 protein_coding ENSG00000186174
## 27 BCL9P1 NA processed_pseudogene ENSG00000249238
## 28 BCLAF1 9774 protein_coding ENSG00000029363
## 29 BCLAF1P1 NA processed_pseudogene ENSG00000248966
## 30 BCLAF1P2 NA processed_pseudogene ENSG00000279800
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")))))
## [1] 118.2458
## 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")))
## [1] 1943.554
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 = "<")))
## GRanges object with 1294 ranges and 3 metadata columns:
## seqnames ranges strand | tx_id exon_idx
## <Rle> <IRanges> <Rle> | <character> <integer>
## ENSE00002088309 Y 2784749-2784853 + | ENST00000516032 1
## ENSE00001494622 Y 2786855-2787699 - | ENST00000383070 1
## ENSE00001772499 Y 2789827-2790328 + | ENST00000454281 1
## ENSE00001614266 Y 2827982-2828218 + | ENST00000430735 1
## ENSE00002490412 Y 2841486-2841627 + | ENST00000250784 1
## ... ... ... ... . ... ...
## ENSE00001632993 Y 26591548-26591601 - | ENST00000456738 1
## ENSE00001616687 Y 26626520-26627159 - | ENST00000435741 1
## ENSE00001638296 Y 26633345-26633431 - | ENST00000435945 2
## ENSE00001797328 Y 26634523-26634652 - | ENST00000435945 1
## ENSE00001794473 Y 56855244-56855488 + | ENST00000431853 1
## exon_id
## <character>
## ENSE00002088309 ENSE00002088309
## ENSE00001494622 ENSE00001494622
## ENSE00001772499 ENSE00001772499
## ENSE00001614266 ENSE00001614266
## ENSE00002490412 ENSE00002490412
## ... ...
## ENSE00001632993 ENSE00001632993
## ENSE00001616687 ENSE00001616687
## ENSE00001638296 ENSE00001638296
## ENSE00001797328 ENSE00001797328
## ENSE00001794473 ENSE00001794473
## -------
## seqinfo: 1 sequence from GRCh38 genome
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")))
TxByGns
## GRangesList object of length 2922:
## $ENSG00000000003
## GRanges object with 5 ranges and 6 metadata columns:
## seqnames ranges strand | tx_id
## <Rle> <IRanges> <Rle> | <character>
## [1] X 100633442-100639991 - | ENST00000494424
## [2] X 100627109-100637104 - | ENST00000612152
## [3] X 100632063-100637104 - | ENST00000614008
## [4] X 100628670-100636806 - | ENST00000373020
## [5] X 100632541-100636689 - | ENST00000496771
## tx_biotype tx_cds_seq_start tx_cds_seq_end gene_id
## <character> <integer> <integer> <character>
## [1] processed_transcript <NA> <NA> ENSG00000000003
## [2] protein_coding 100630798 100635569 ENSG00000000003
## [3] protein_coding 100632063 100635569 ENSG00000000003
## [4] protein_coding 100630798 100636694 ENSG00000000003
## [5] processed_transcript <NA> <NA> ENSG00000000003
## tx_name
## <character>
## [1] ENST00000494424
## [2] ENST00000612152
## [3] ENST00000614008
## [4] ENST00000373020
## [5] ENST00000496771
## -------
## seqinfo: 2 sequences from GRCh38 genome
##
## $ENSG00000000005
## GRanges object with 2 ranges and 6 metadata columns:
## seqnames ranges strand | tx_id
## <Rle> <IRanges> <Rle> | <character>
## [1] X 100584802-100599885 + | ENST00000373031
## [2] X 100593624-100597531 + | ENST00000485971
## tx_biotype tx_cds_seq_start tx_cds_seq_end gene_id
## <character> <integer> <integer> <character>
## [1] protein_coding 100585019 100599717 ENSG00000000005
## [2] processed_transcript <NA> <NA> ENSG00000000005
## tx_name
## <character>
## [1] ENST00000373031
## [2] ENST00000485971
## -------
## seqinfo: 2 sequences from GRCh38 genome
##
## $ENSG00000001497
## GRanges object with 5 ranges and 6 metadata columns:
## seqnames ranges strand | tx_id
## <Rle> <IRanges> <Rle> | <character>
## [1] X 65512583-65534775 - | ENST00000484069
## [2] X 65512582-65534756 - | ENST00000374811
## [3] X 65512583-65534756 - | ENST00000374804
## [4] X 65512582-65534754 - | ENST00000374807
## [5] X 65520429-65523617 - | ENST00000469091
## tx_biotype tx_cds_seq_start tx_cds_seq_end gene_id
## <character> <integer> <integer> <character>
## [1] nonsense_mediated_de.. 65525021 65534715 ENSG00000001497
## [2] protein_coding 65512775 65534715 ENSG00000001497
## [3] protein_coding 65512775 65534715 ENSG00000001497
## [4] protein_coding 65512775 65534715 ENSG00000001497
## [5] protein_coding 65520655 65523617 ENSG00000001497
## tx_name
## <character>
## [1] ENST00000484069
## [2] ENST00000374811
## [3] ENST00000374804
## [4] ENST00000374807
## [5] ENST00000469091
## -------
## seqinfo: 2 sequences from GRCh38 genome
##
## ...
## <2919 more elements>
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.
Also functions from the GenomicFeatures
package can be applied to EnsDb
databases, such as the exonicParts
function to extract a GRanges
of
non-overlapping exon parts which can be used in the DEXSeq
package.
## Create a GRanges of non-overlapping exon parts.
edb_sub <- filter(edb, filter = AnnotationFilterList(
SeqNameFilter(c(1:22, "X", "Y")),
GeneIdFilter("ENSG%", "startsWith")))
DJE <- exonicParts(edb_sub)
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
getGenomeTwoBitFile
. If no 2bit file matching the Ensembl version is available,
the function tries to identify a file with the correct genome build from the
closest Ensembl release and returns that instead.
In the code block below we retrieve first the TwoBitFile
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 TwoBitFile
and extract all of their sequences. Note: these sequences represent the sequence
between the chromosomal start and end coordinates of the gene.
library(EnsDb.Hsapiens.v86)
edb <- EnsDb.Hsapiens.v86
## Get the TwoBit with the genomic sequence matching the Ensembl version
## using the AnnotationHub package.
dna <- ensembldb:::getGenomeTwoBitFile(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 TwoBitile.
library(GenomicFeatures)
yTxSeqs <- extractTranscriptSeqs(dna, yTx)
yTxSeqs
## 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.
library(BSgenome.Hsapiens.NCBI.GRCh38)
bsg <- BSgenome.Hsapiens.NCBI.GRCh38
## Get the genome version
unique(genome(bsg))
## [1] "GRCh38"
unique(genome(edb))
## [1] "GRCh38"
## Extract the full transcript sequences.
yTxSeqs <- extractTranscriptSeqs(
bsg, exonsBy(edb, "tx", filter = SeqNameFilter("Y")))
yTxSeqs
## DNAStringSet object of length 740:
## width seq names
## [1] 5239 GCCTAGTGCGCGCGCAGTAACC...AATAAATGTTTACTTGTATATG ENST00000155093
## [2] 4595 CTGGTGGTCCAGTACCTCCAAA...TGAGCCCTTCAGAAGACATTCT ENST00000215473
## [3] 802 AGAGGACCAAGCCTCCCTGTGT...CAATAAAATGTTTTAAAAATCA ENST00000215479
## [4] 910 TGTCTGTCAGAGCTGTCAGCCT...TAAACACTGGTATATTTCTGTT ENST00000250776
## [5] 1305 TTCCAGGATATGAACTCTACAG...TAAATCCTGTGGCTGTAGGAAA ENST00000250784
## ... ... ...
## [736] 792 ATGGCCCGGGGCCCCAAGAAGC...TGCCAAACAGAGCAGTGGCTAA ENST00000629237
## [737] 344 GGTTGCCACTTCAAGGGACTAC...CTGGCTCTTCTGGCAGTTTTTT ENST00000631331
## [738] 933 CTCTCCCAGCTTCTACCCACAG...GCATACTATAAAAATGCTTTAA ENST00000634531
## [739] 1832 ATGTCTGCTGCAAATCCTGAGA...AGTATTTAAATCTGTTGGATCC ENST00000634662
## [740] 890 CTCTCCCAGCTTCTACCCACAG...GCATACTATAAAAATGCTTTAA ENST00000635343
## Extract just the CDS
Test <- cdsBy(edb, "tx", filter = SeqNameFilter("Y"))
yTxCds <- extractTranscriptSeqs(
bsg, cdsBy(edb, "tx", filter = SeqNameFilter("Y")))
yTxCds
## DNAStringSet object of length 151:
## width seq names
## [1] 2406 ATGGATGAAGATGAATTTGAAT...TAAAGAAGTTGGTCTGCCCTAA ENST00000155093
## [2] 3162 ATGTTTAGGGTTGGCTTCTTAA...AGTTTCTAACACAACTTTCTAA ENST00000215473
## [3] 579 ATGGGGACCTGGATTTTGTTTG...CAAGCAGGAGGAAGTGGATTAA ENST00000215479
## [4] 792 ATGGCCCGGGGCCCCAAGAAGC...CACCAAACAGAGCAGTGGCTAA ENST00000250784
## [5] 378 ATGAGTCCAAAGCCGAGAGCCT...ATCTACTCCCCTATCTCCCTGA ENST00000250823
## ... ... ...
## [147] 387 ATGCAAAGCCAGAGAGGTCTCC...CACACTCTGTGTCCCAAAATGA ENST00000624507
## [148] 78 ATGAGAGCCAAGTGGAGGAAGA...GATGAGGCAGAAGTCCAAGTAA ENST00000624575
## [149] 1833 ATGGATGAAGATGAATTTGAAT...TAAAGAAGTTGGTCTGCCCTAA ENST00000625061
## [150] 792 ATGGCCCGGGGCCCCAAGAAGC...TGCCAAACAGAGCAGTGGCTAA ENST00000629237
## [151] 1740 ATGTCTGCTGCAAATCCTGAGA...TTTAATCCAGAGAAGAGACTGA ENST00000634662
EnsDb
packages with UCSC based annotationsSometimes 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
seqlevels(genesY)
## [1] "chrY"
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:
getOption("ensembldb.seqnameNotFound")
## [1] "ORIGINAL"
## Listing all seqlevels in the database.
seqlevels(edb)[1:30]
## Warning in .formatSeqnameByStyleFromQuery(x, sn, ifNotFound): More than 5
## seqnames with seqlevels style of the database (Ensembl) could not be mapped to
## the seqlevels style: UCSC!) Returning the orginal seqnames for these.
## [1] "chr1" "chr10"
## [3] "chr11" "chr12"
## [5] "chr13" "chr14"
## [7] "chr15" "chr16"
## [9] "chr17" "chr18"
## [11] "chr19" "chr2"
## [13] "chr20" "chr21"
## [15] "chr22" "chr3"
## [17] "chr4" "chr5"
## [19] "chr6" "chr7"
## [21] "chr8" "chr9"
## [23] "CHR_HG107_PATCH" "CHR_HG126_PATCH"
## [25] "CHR_HG1311_PATCH" "CHR_HG1342_HG2282_PATCH"
## [27] "CHR_HG1362_PATCH" "CHR_HG142_HG150_NOVEL_TEST"
## [29] "CHR_HG151_NOVEL_TEST" "CHR_HG1651_PATCH"
## Setting the option to NA, thus, for each seqname for which no mapping is available,
## NA is returned.
options(ensembldb.seqnameNotFound=NA)
seqlevels(edb)[1:30]
## Warning in .formatSeqnameByStyleFromQuery(x, sn, ifNotFound): More than 5
## seqnames with seqlevels style of the database (Ensembl) could not be mapped to
## the seqlevels style: UCSC!) Returning NA for these.
## [1] "chr1" "chr10" "chr11" "chr12" "chr13" "chr14" "chr15" "chr16" "chr17"
## [10] "chr18" "chr19" "chr2" "chr20" "chr21" "chr22" "chr3" "chr4" "chr5"
## [19] "chr6" "chr7" "chr8" "chr9" NA NA NA NA NA
## [28] NA NA NA
## Resetting the option.
options(ensembldb.seqnameNotFound = "ORIGINAL")
As an alternative, seqlevelsStyle
for EnsDb
supports also to define custom
renaming. Below we thus define a data.frame
with new names for some specific
chromosomes. A column "Ensembl"
is expected to contain the original chromosome
names and the second column the new names. In the example below we simply want
to rename some selected chromosomes, thus we define the mapping data.frame
and
pass that to the seqlevelsStyle
method.
mymap <- data.frame(
Ensembl = c(1, 21, "X", "Y"),
myway = c("one", "twentyone", "chrX", "chrY")
)
seqlevelsStyle(edb) <- mymap
With that we have now chromosomes 1, 21, X and Y renamed to the new names. Below we list the last 6 values showing the new names for chromosomes X and Y.
tail(seqlevels(edb))
## Warning in .formatSeqnameByStyleFromQuery(x, sn, ifNotFound): More than 5
## seqnames with seqlevels style of the database (Ensembl) could not be mapped to
## the seqlevels style: myway!) Returning the orginal seqnames for these.
## [1] "LRG_721" "LRG_741" "LRG_93" "MT" "chrX" "chrY"
At last changing the seqname style to the default value "Ensembl"
.
seqlevelsStyle(edb) <- "Ensembl"
shiny
web appIn addition to the genes
, transcripts
and