Contents
Package: ensembldb
Authors: Johannes Rainer johannes.rainer@eurac.edu, Tim Triche tim.triche@usc.edu
Modified: 30 June, 2016
Compiled: Thu Jun 30 22:09:08 2016
Introduction
The ensembldb package provides functions to create and use transcript centric annotation databases/packages. The annotation for the databases are directly fetched from Ensembl 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. In the databases, along with the gene and transcript models and their chromosomal coordinates, additional annotations including the gene name (symbol) and NCBI Entrezgene identifiers as well as the gene and transcript biotypes are stored too (see Section 11 for the database layout and an overview of available attributes/columns).
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.v75 for human gene definitions of the Ensembl code database version 75). 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 75. The connection to the database is bound to the variable EnsDb.Hsapiens.v75.
library(EnsDb.Hsapiens.v75)
## Making a "short cut"
edb <- EnsDb.Hsapiens.v75
## print some informations for this package
edb
## EnsDb for Ensembl:
## |Db type: EnsDb
## |Type of Gene ID: Ensembl Gene ID
## |Supporting package: ensembldb
## |Db created by: ensembldb package from Bioconductor
## |script_version: 0.1.2
## |Creation time: Wed Mar 18 09:30:54 2015
## |ensembl_version: 75
## |ensembl_host: manny.i-med.ac.at
## |Organism: homo_sapiens
## |genome_build: GRCh37
## |DBSCHEMAVERSION: 1.0
## | No. of genes: 64102.
## | No. of transcripts: 215647.
## for what organism was the database generated?
organism(edb)
## [1] "Homo sapiens"
Using ensembldb annotation packages to retrieve specific annotations
The ensembldb package provides a set of filter objects allowing to specify which entries should be fetched from the database. The complete list of filters, which can be used individually or can be combined, is shown below (in alphabetical order):
ExonidFilter: allows to filter the result based on the (Ensembl) exon identifiers.ExonrankFilter: filter results 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.EntrezidFilter: allows to filter results based on NCBI Entrezgene identifiers of the genes.GenebiotypeFilter: allows to filter for the gene biotypes defined in the Ensembl database; use the listGenebiotypes method to list all available biotypes.GeneidFilter: allows to filter based on the Ensembl gene IDs.GenenameFilter: allows to filter based on the names (symbols) of the genes.GRangesFilter: allows to retrieve all features (genes, transcripts or exons) that are either within (setting =condition = “within”=) or partially overlapping (condition = "overlapping"=) 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.SeqendFilter: filter based on the chromosomal end coordinate of the exons, transcripts or genes (correspondingly set =feature = “exon”=, =feature = “tx”= or =feature = “gene”=).SeqnameFilter: filter by the name of the chromosomes the genes are encoded on.SeqstartFilter: filter based on the chromosomal start coordinates of the exons, transcripts or genes (correspondingly set =feature = “exon”=, =feature = “tx”= or =feature = “gene”=).SeqstrandFilter: filter for the chromosome strand on which the genes are encoded.TxbiotypeFilter: filter on the transcript biotype defined in Ensembl; use the listTxbiotypes method to list all available biotypes.TxidFilter: filter on the Ensembl transcript identifiers.
Each of the filter classes can take a single value or a vector of values (with the exception of the SeqendFilter and SeqstartFilter) for comparison. In addition, it is possible to specify the condition for the filter, e.g. setting condition to = to retrieve all entries matching the filter value, to != to negate the filter or setting condition = "like"= to allow partial matching. The =condition parameter for SeqendFilter and SeqendFilter can take the values = , >, >=, < and <= (since these filters base on numeric values).
A simple example 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 of the GRanges object 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 = "DataFrame"=, or =return.type“data.frame”= the method would return a DataFrame object or data.frame.
Tx <- transcripts(edb, filter = list(GenenameFilter("BCL2L11")))
Tx
## GRanges object with 17 ranges and 5 metadata columns:
## seqnames ranges strand | tx_id
## <Rle> <IRanges> <Rle> | <character>
## ENST00000432179 2 [111876955, 111881689] + | ENST00000432179
## ENST00000308659 2 [111878491, 111922625] + | ENST00000308659
## ENST00000357757 2 [111878491, 111919016] + | ENST00000357757
## ENST00000393253 2 [111878491, 111909428] + | ENST00000393253
## ENST00000337565 2 [111878491, 111886423] + | ENST00000337565
## ... ... ... ... . ...
## ENST00000452231 2 [111881323, 111921808] + | ENST00000452231
## ENST00000361493 2 [111881323, 111921808] + | ENST00000361493
## ENST00000431217 2 [111881323, 111921929] + | ENST00000431217
## ENST00000439718 2 [111881323, 111922220] + | ENST00000439718
## ENST00000438054 2 [111881329, 111903861] + | ENST00000438054
## tx_biotype tx_cds_seq_start tx_cds_seq_end
## <character> <numeric> <numeric>
## ENST00000432179 protein_coding 111881323 111881689
## ENST00000308659 protein_coding 111881323 111921808
## ENST00000357757 protein_coding 111881323 111919016
## ENST00000393253 protein_coding 111881323 111909428
## ENST00000337565 protein_coding 111881323 111886328
## ... ... ... ...
## ENST00000452231 nonsense_mediated_decay 111881323 111919016
## ENST00000361493 nonsense_mediated_decay 111881323 111887812
## ENST00000431217 nonsense_mediated_decay 111881323 111902078
## ENST00000439718 nonsense_mediated_decay 111881323 111909428
## ENST00000438054 protein_coding 111881329 111902068
## gene_id
## <character>
## ENST00000432179 ENSG00000153094
## ENST00000308659 ENSG00000153094
## ENST00000357757 ENSG00000153094
## ENST00000393253 ENSG00000153094
## ENST00000337565 ENSG00000153094
## ... ...
## ENST00000452231 ENSG00000153094
## ENST00000361493 ENSG00000153094
## ENST00000431217 ENSG00000153094
## ENST00000439718 ENSG00000153094
## ENST00000438054 ENSG00000153094
## -------
## seqinfo: 1 sequence from GRCh37 genome
## as this is a GRanges object we can access e.g. the start coordinates with
head(start(Tx))
## [1] 111876955 111878491 111878491 111878491 111878491 111878506
## 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 exons, genes and transcripts method allow to specify which database attributes (columns) should be retrieved. Note that these are not restricted to columns of the corresponding database table (e.g. columns of database table gene for genes). 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" "entrezid"
## [4] "gene_biotype" "gene_seq_start" "gene_seq_end"
## [7] "seq_name" "seq_strand" "seq_coord_system"
##
## $tx
## [1] "tx_id" "tx_biotype" "tx_seq_start"
## [4] "tx_seq_end" "tx_cds_seq_start" "tx_cds_seq_end"
## [7] "gene_id"
##
## $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"
##
## $metadata
## [1] "name" "value"
## list columns from a specific table
listColumns(edb, "tx")
## [1] "tx_id" "tx_biotype" "tx_seq_start"
## [4] "tx_seq_end" "tx_cds_seq_start" "tx_cds_seq_end"
## [7] "gene_id"
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] 13812
Tx
## DataFrame with 13812 rows and 8 columns
## tx_id tx_biotype tx_seq_start tx_seq_end
## <character> <character> <integer> <integer>
## 1 ENST00000495251 nonsense_mediated_decay 64085 69409
## 2 ENST00000462860 nonsense_mediated_decay 64085 69452
## 3 ENST00000483390 nonsense_mediated_decay 65739 68764
## 4 ENST00000538848 nonsense_mediated_decay 66411 68843
## 5 ENST00000567466 nonsense_mediated_decay 97578 99521
## ... ... ... ... ...
## 13808 ENST00000496411 nonsense_mediated_decay 249149927 249153217
## 13809 ENST00000483223 nonsense_mediated_decay 249150714 249152728
## 13810 ENST00000533647 nonsense_mediated_decay 249151472 249152523
## 13811 ENST00000528141 nonsense_mediated_decay 249151590 249153284
## 13812 ENST00000530986 nonsense_mediated_decay 249151668 249153284
## tx_cds_seq_start tx_cds_seq_end gene_id gene_name
## <numeric> <numeric> <character> <character>
## 1 68052 68789 ENSG00000234769 WASH4P
## 2 68052 68789 ENSG00000234769 WASH4P
## 3 66428 68764 ENSG00000234769 WASH4P
## 4 67418 68789 ENSG00000234769 WASH4P
## 5 98546 98893 ENSG00000261456 TUBB8
## ... ... ... ... ...
## 13808 249152153 249152508 ENSG00000171163 ZNF692
## 13809 249152153 249152508 ENSG00000171163 ZNF692
## 13810 249152153 249152508 ENSG00000171163 ZNF692
## 13811 249152203 249152508 ENSG00000171163 ZNF692
## 13812 249152203 249152508 ENSG00000171163 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 160:
## $ENST00000155093
## GRanges object with 7 ranges and 2 metadata columns:
## seqnames ranges strand | exon_id exon_rank
## <Rle> <IRanges> <Rle> | <character> <integer>
## [1] Y [2821978, 2822038] + | ENSE00002223884 2
## [2] Y [2829115, 2829687] + | ENSE00003645989 3
## [3] Y [2843136, 2843285] + | ENSE00003548678 4
## [4] Y [2843552, 2843695] + | ENSE00003611496 5
## [5] Y [2844711, 2844863] + | ENSE00001649504 6
## [6] Y [2845981, 2846121] + | ENSE00001777381 7
## [7] Y [2846851, 2848034] + | ENSE00001368923 8
##
## $ENST00000215473
## GRanges object with 6 ranges and 2 metadata columns:
## seqnames ranges strand | exon_id exon_rank
## [1] Y [4924865, 4925500] + | ENSE00001436852 1
## [2] Y [4966256, 4968748] + | ENSE00001640924 2
## [3] Y [5369098, 5369296] + | ENSE00001803775 3
## [4] Y [5483308, 5483316] + | ENSE00001731866 4
## [5] Y [5491131, 5491145] + | ENSE00001711324 5
## [6] Y [5605313, 5605983] + | ENSE00001779807 6
##
## $ENST00000215479
## GRanges object with 5 ranges and 2 metadata columns:
## seqnames ranges strand | exon_id exon_rank
## [1] Y [6740596, 6740649] - | ENSE00001671586 2
## [2] Y [6738047, 6738094] - | ENSE00001645681 3
## [3] Y [6736773, 6736817] - | ENSE00000652250 4
## [4] Y [6736078, 6736503] - | ENSE00001667251 5
## [5] Y [6734114, 6734119] - | ENSE00001494454 6
##
## ...
## <157 more elements>
## -------
## seqinfo: 1 sequence from GRCh37 genome
Using a GRangesFilter we can retrieve all features from the database that are either within or overlapping the specified genomic region. In the example below we query all genes that are partially overlapping with a small region on chromosome 11. The filter restricts to all genes for which either an exon or an intron is partially overlapping with the region.
## Define the filter
grf <- GRangesFilter(GRanges("11", ranges = IRanges(114000000, 114000050),
strand="+"), condition = "overlapping")
## Query genes:
gn <- genes(edb, filter = grf)
gn
## GRanges object with 1 range and 5 metadata columns:
## seqnames ranges strand | gene_id
## <Rle> <IRanges> <Rle> | <character>
## ENSG00000109906 11 [113930315, 114121398] + | ENSG00000109906
## gene_name entrezid gene_biotype seq_coord_system
## <character> <character> <character> <character>
## ENSG00000109906 ZBTB16 7704 protein_coding chromosome
## -------
## seqinfo: 1 sequence from GRCh37 genome
## Next we retrieve all transcripts for that gene so that we can plot them.
txs <- transcripts(edb, filter = GenenameFilter(gn$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)
## GRanges object with 4 ranges and 5 metadata columns:
## seqnames ranges strand | tx_id
## <Rle> <IRanges> <Rle> | <character>
## ENST00000335953 11 [113930315, 114121398] + | ENST00000335953
## ENST00000541602 11 [113930447, 114060486] + | ENST00000541602
## ENST00000392996 11 [113931229, 114121374] + | ENST00000392996
## ENST00000539918 11 [113935134, 114118066] + | ENST00000539918
## tx_biotype tx_cds_seq_start tx_cds_seq_end
## <character> <numeric> <numeric>
## ENST00000335953 protein_coding 113934023 114121277
## ENST00000541602 retained_intron <NA> <NA>
## ENST00000392996 protein_coding 113934023 114121277
## ENST00000539918 nonsense_mediated_decay 113935134 113992549
## gene_id
## <character>
## ENST00000335953 ENSG00000109906
## ENST00000541602 ENSG00000109906
## ENST00000392996 ENSG00000109906
## ENST00000539918 ENSG00000109906
## -------
## seqinfo: 1 sequence from GRCh37 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.
To get an overview of allowed/available gene and transcript biotype the functions listGenebiotypes and listTxbiotypes can be used.
## Get all gene biotypes from the database. The GenebiotypeFilter
## allows to filter on these values.
listGenebiotypes(edb)
## [1] "protein_coding" "pseudogene"
## [3] "processed_transcript" "antisense"
## [5] "lincRNA" "polymorphic_pseudogene"
## [7] "IG_V_pseudogene" "IG_V_gene"
## [9] "sense_overlapping" "sense_intronic"
## [11] "TR_V_gene" "misc_RNA"
## [13] "snRNA" "miRNA"
## [15] "snoRNA" "rRNA"
## [17] "Mt_tRNA" "Mt_rRNA"
## [19] "IG_C_gene" "IG_J_gene"
## [21] "TR_J_gene" "TR_C_gene"
## [23] "TR_V_pseudogene" "TR_J_pseudogene"
## [25] "IG_D_gene" "IG_C_pseudogene"
## [27] "TR_D_gene" "IG_J_pseudogene"
## [29] "3prime_overlapping_ncrna" "processed_pseudogene"
## [31] "LRG_gene"
## Get all transcript biotypes from the database.
listTxbiotypes(edb)
## [1] "protein_coding"
## [2] "processed_transcript"
## [3] "retained_intron"
## [4] "nonsense_mediated_decay"
## [5] "unitary_pseudogene"
## [6] "non_stop_decay"
## [7] "unprocessed_pseudogene"
## [8] "processed_pseudogene"
## [9] "transcribed_unprocessed_pseudogene"
## [10] "antisense"
## [11] "lincRNA"
## [12] "polymorphic_pseudogene"
## [13] "transcribed_processed_pseudogene"
## [14] "miRNA"
## [15] "pseudogene"
## [16] "IG_V_pseudogene"
## [17] "snoRNA"
## [18] "IG_V_gene"
## [19] "sense_overlapping"
## [20] "sense_intronic"
## [21] "TR_V_gene"
## [22] "snRNA"
## [23] "misc_RNA"
## [24] "rRNA"
## [25] "Mt_tRNA"
## [26] "Mt_rRNA"
## [27] "IG_C_gene"
## [28] "IG_J_gene"
## [29] "TR_J_gene"
## [30] "TR_C_gene"
## [31] "TR_V_pseudogene"
## [32] "TR_J_pseudogene"
## [33] "IG_D_gene"
## [34] "IG_C_pseudogene"
## [35] "TR_D_gene"
## [36] "IG_J_pseudogene"
## [37] "3prime_overlapping_ncrna"
## [38] "translated_processed_pseudogene"
## [39] "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. To this end
## we define a GenenameFilter with partial matching, i.e. condition "like"
## and a % for any character/string.
BCLs <- genes(edb,
columns = c("gene_name", "entrezid", "gene_biotype"),
filter = list(GenenameFilter("BCL%", condition = "like")),
return.type = "DataFrame")
nrow(BCLs)
## [1] 25
BCLs
## DataFrame with 25 rows and 3 columns
## gene_name entrezid gene_biotype
## <character> <character> <character>
## 1 BCLAF1 9774 protein_coding
## 2 BCL3 602;102465879 protein_coding
## 3 BCL7C 9274 protein_coding
## 4 BCL2L13 23786 protein_coding
## 5 BCL7B 9275 protein_coding
## ... ... ... ...
## 21 BCL9L 283149 protein_coding
## 22 BCL2L15 440603 protein_coding
## 23 BCL2L2-PABPN1 599;100529063 protein_coding
## 24 BCL7B 9275 protein_coding
## 25 BCL9 607 protein_coding
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.
## determine the average length of snRNA, snoRNA and rRNA genes encoded on
## chromosomes X and Y.
mean(lengthOf(edb, of = "tx",
filter = list(GenebiotypeFilter(c("snRNA", "snoRNA", "rRNA")),
SeqnameFilter(c("X", "Y")))))
## [1] 116.3046
## determine the average length of protein coding genes encoded on the same
## chromosomes.
mean(lengthOf(edb, of = "tx",
filter = list(GenebiotypeFilter("protein_coding"),
SeqnameFilter(c("X", "Y")))))
## [1] 1920
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 1287 ranges and 3 metadata columns:
## seqnames ranges strand | exon_id
## <Rle> <IRanges> <Rle> | <character>
## ENSE00002088309 Y [2652790, 2652894] + | ENSE00002088309
## ENSE00001494622 Y [2654896, 2655740] - | ENSE00001494622
## ENSE00002323146 Y [2655049, 2655069] - | ENSE00002323146
## ENSE00002201849 Y [2655075, 2655644] - | ENSE00002201849
## ENSE00002214525 Y [2655145, 2655168] - | ENSE00002214525
## ... ... ... ... . ...
## ENSE00001632993 Y [28737695, 28737748] - | ENSE00001632993
## ENSE00001616687 Y [28772667, 28773306] - | ENSE00001616687
## ENSE00001638296 Y [28779492, 28779578] - | ENSE00001638296
## ENSE00001797328 Y [28780670, 28780799] - | ENSE00001797328
## ENSE00001794473 Y [59001391, 59001635] + | ENSE00001794473
## tx_id exon_idx
## <character> <integer>
## ENSE00002088309 ENST00000516032 1
## ENSE00001494622 ENST00000383070 1
## ENSE00002323146 ENST00000525526 2
## ENSE00002201849 ENST00000525526 1
## ENSE00002214525 ENST00000534739 2
## ... ... ...
## ENSE00001632993 ENST00000456738 1
## ENSE00001616687 ENST00000435741 1
## ENSE00001638296 ENST00000435945 2
## ENSE00001797328 ENST00000435945 1
## ENSE00001794473 ENST00000431853 1
## -------
## seqinfo: 1 sequence from GRCh37 genome
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.
library(EnsDb.Hsapiens.v75)
library(Rsamtools)
edb <- EnsDb.Hsapiens.v75
## 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.
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)
Note: in the next section we describe how transcript sequences can be retrieved from a BSgenome package that is based on UCSC, not Ensembl.
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 = SeqnameFilter("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 = “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" "chr11" "chr12" "chr13"
## [6] "chr14" "chr15" "chr16" "chr17" "chr18"
## [11] "chr19" "chr2" "chr20" "chr21" "chr22"
## [16] "chr3" "chr4" "chr5" "chr6" "chr7"
## [21] "chr8" "chr9" "GL000191.1" "GL000192.1" "GL000193.1"
## [26] "GL000194.1" "GL000195.1" "GL000196.1" "GL000199.1" "GL000201.1"
## 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"
## [9] "chr17" "chr18" "chr19" "chr2" "chr20" "chr21" "chr22" "chr3"
## [17] "chr4" "chr5" "chr6" "chr7" "chr8" "chr9" NA NA
## [25] NA NA NA NA NA NA
## Resetting the option.
options(ensembldb.seqnameNotFound = "ORIGINAL")
Next we retrieve transcript sequences from genes encoded on chromosome Y using the BSGenome package for the human genome from UCSC. The specified version hg19 matches the genome build of Ensembl version 75, i.e. GRCh37. Note that while we changed the style of the seqnames to UCSC we did not change the naming of the genome release.
library(BSgenome.Hsapiens.UCSC.hg19)
bsg <- BSgenome.Hsapiens.UCSC.hg19
## Get the genome version
unique(genome(bsg))
## [1] "hg19"
unique(genome(edb))
## [1] "GRCh37"
## Although differently named, both represent genome build GRCh37.
## Extract the full transcript sequences.
yTxSeqs <- extractTranscriptSeqs(bsg, exonsBy(edb, "tx", filter = SeqnameFilter("chrY")))
yTxSeqs
## A DNAStringSet instance of length 731
## width seq names
## [1] 5239 GCCTAGTGCGCGCGCAGTAA...AAATGTTTACTTGTATATG ENST00000155093
## [2] 4023 ATGTTTAGGGTTGGCTTCTT...GGAAACACATCCCTTGTAA ENST00000215473
## [3] 802 AGAGGACCAAGCCTCCCTGT...TAAAATGTTTTAAAAATCA ENST00000215479
## [4] 910 TGTCTGTCAGAGCTGTCAGC...ACACTGGTATATTTCTGTT ENST00000250776
## [5] 1305 TTCCAGGATATGAACTCTAC...ATCCTGTGGCTGTAGGAAA ENST00000250784
## ... ... ...
## [727] 333 ATGGATGAAGAAGAGAAAAC...TGAACTTTCTAGATTGCAT ENST00000604924
## [728] 1247 CATGGCGGGGTTCCTGCCTT...TTTGGAGTAATGTCTTAGT ENST00000605584
## [729] 199 CAGTTCTCGCTCCTGTGCAG...GGTCTGGGTGGCTTCTGGA ENST00000605663
## [730] 276 GCCCCAGGAGGAAAGGGGGA...AATAAAGAACAGCGCATTC ENST00000606439
## [731] 444 ATGGGAGCCACTGGGCTTGG...CGTTCATGAAGAAGACTAA ENST00000607210
## Extract just the CDS
Test <- cdsBy(edb, "tx", filter = SeqnameFilter("chrY"))
yTxCds <- extractTranscriptSeqs(bsg, cdsBy(edb, "tx", filter = SeqnameFilter("chrY")))
yTxCds
## A DNAStringSet instance of length 160
## width seq names
## [1] 2406 ATGGATGAAGATGAATTTGA...AGAAGTTGGTCTGCCCTAA ENST00000155093
## [2] 4023 ATGTTTAGGGTTGGCTTCTT...GGAAACACATCCCTTGTAA ENST00000215473
## [3] 579 ATGGGGACCTGGATTTTGTT...GCAGGAGGAAGTGGATTAA ENST00000215479
## [4] 792 ATGGCCCGGGGCCCCAAGAA...CAAACAGAGCAGTGGCTAA ENST00000250784
## [5] 378 ATGAGTCCAAAGCCGAGAGC...TACTCCCCTATCTCCCTGA ENST00000250823
## ... ... ...
## [156] 63 CGCAAGGATTTAAAAGAGAT...ACCCTGTTGGCCAGGCTAG ENST00000601700
## [157] 42 CTTGATACAAAGAATCAATTTAATTTTAAGATTGTCTATCTT ENST00000601705
## [158] 33 ATGATGACGCTTGTCCCCAGAGCCAGGACACGT ENST00000602680
## [159] 33 ATGATGACGCTTGTCCCCAGAGCCAGGACACGT ENST00000602732
## [160] 33 ATGATGACGCTTGTCCCCAGAGCCAGGACACGT ENST00000602770
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
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
library(Gviz)
library(EnsDb.Hsapiens.v75)
edb <- EnsDb.Hsapiens.v75
## 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)
seqnames(gr)
## factor-Rle of length 218 with 1 run
## Lengths: 218
## Values : chrY
## Levels(1): chrY
## 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"
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.
library(EnsDb.Hsapiens.v75)
edb <- EnsDb.Hsapiens.v75
## List all available columns in the database.
columns(edb)
## [1] "ENTREZID" "EXONID" "EXONIDX" "EXONSEQEND"
## [5] "EXONSEQSTART" "GENEBIOTYPE" "GENEID" "GENENAME"
## [9] "GENESEQEND" "GENESEQSTART" "ISCIRCULAR" "SEQCOORDSYSTEM"
## [13] "SEQLENGTH" "SEQNAME" "SEQSTRAND" "TXBIOTYPE"
## [17] "TXCDSSEQEND" "TXCDSSEQSTART" "TXID" "TXSEQEND"
## [21] "TXSEQSTART"
## 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.
listColumns(edb)
## [1] "seq_name" "seq_length" "is_circular"
## [4] "exon_id" "exon_seq_start" "exon_seq_end"
## [7] "gene_id" "gene_name" "entrezid"
## [10] "gene_biotype" "gene_seq_start" "gene_seq_end"
## [13] "seq_name" "seq_strand" "seq_coord_system"
## [16] "name" "value" "tx_id"
## [19] "tx_biotype" "tx_seq_start" "tx_seq_end"
## [22] "tx_cds_seq_start" "tx_cds_seq_end" "gene_id"
## [25] "tx_id" "exon_id" "exon_idx"
## List all of the supported key types.
keytypes(edb)
## [1] "ENTREZID" "EXONID" "GENEBIOTYPE" "GENEID" "GENENAME"
## [6] "SEQNAME" "SEQSTRAND" "TXBIOTYPE" "TXID"
## Get all gene ids from the database.
gids <- keys(edb, keytype = "GENEID")
length(gids)
## [1] 64102
## Get all gene names for genes encoded on chromosome Y.
gnames <- keys(edb, keytype = "GENENAME", filter = SeqnameFilter("Y"))
head(gnames)
## [1] "KDM5D" "DDX3Y" "ZFY" "TBL1Y" "PCDH11Y" "AMELY"
In the next example we retrieve specific information from the database using the select method. First we fetch all transcripts for the genes ZBTB16, 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. Note that the results in the first query are ordered according to the specified keys while in the second query the ordering is arbitrary. Ordering by keys is only performed if keys is a character vector or a single filter object; for multiple filters the data is returned in an arbitrary ordering.
## Use the /standard/ way to fetch data.
select(edb, keys = c("ZBTB16", "BCL2", "BCL2L11"), keytype = "GENENAME",
columns = c("GENEID", "GENENAME", "TXID", "TXBIOTYPE"))
## GENENAME TXID TXBIOTYPE GENEID
## 1 ZBTB16 ENST00000335953 protein_coding ENSG00000109906
## 2 ZBTB16 ENST00000541602 retained_intron ENSG00000109906
## 3 ZBTB16 ENST00000544220 protein_coding ENSG00000109906
## 4 ZBTB16 ENST00000535700 protein_coding ENSG00000109906
## 5 ZBTB16 ENST00000392996 protein_coding ENSG00000109906
## 6 ZBTB16 ENST00000539918 nonsense_mediated_decay ENSG00000109906
## 7 ZBTB16 ENST00000545851 processed_transcript ENSG00000109906
## 8 ZBTB16 ENST00000535379 processed_transcript ENSG00000109906
## 9 ZBTB16 ENST00000535509 retained_intron ENSG00000109906
## 10 BCL2 ENST00000398117 protein_coding ENSG00000171791
## 11 BCL2 ENST00000333681 protein_coding ENSG00000171791
## 12 BCL2 ENST00000590515 processed_transcript ENSG00000171791
## 13 BCL2 ENST00000589955 protein_coding ENSG00000171791
## 14 BCL2 ENST00000444484 protein_coding ENSG00000171791
## 15 BCL2L11 ENST00000432179 protein_coding ENSG00000153094
## 16 BCL2L11 ENST00000308659 protein_coding ENSG00000153094
## 17 BCL2L11 ENST00000393256 protein_coding ENSG00000153094
## 18 BCL2L11 ENST00000393252 protein_coding ENSG00000153094
## 19 BCL2L11 ENST00000433098 nonsense_mediated_decay ENSG00000153094
## 20 BCL2L11 ENST00000405953 protein_coding ENSG00000153094
## 21 BCL2L11 ENST00000415458 nonsense_mediated_decay ENSG00000153094
## 22 BCL2L11 ENST00000436733 nonsense_mediated_decay ENSG00000153094
## 23 BCL2L11 ENST00000437029 nonsense_mediated_decay ENSG00000153094
## 24 BCL2L11 ENST00000452231 nonsense_mediated_decay ENSG00000153094
## 25 BCL2L11 ENST00000361493 nonsense_mediated_decay ENSG00000153094
## 26 BCL2L11 ENST00000431217 nonsense_mediated_decay ENSG00000153094
## 27 BCL2L11 ENST00000439718 nonsense_mediated_decay ENSG00000153094
## 28 BCL2L11 ENST00000438054 protein_coding ENSG00000153094
## 29 BCL2L11 ENST00000357757 protein_coding ENSG00000153094
## 30 BCL2L11 ENST00000393253 protein_coding ENSG00000153094
## 31 BCL2L11 ENST00000337565 protein_coding ENSG00000153094
## Use the filtering system of ensembldb
select(edb, keys = list(GenenameFilter(c("BCL2", "BCL2L11")),
TxbiotypeFilter("protein_coding")),
columns = c("GENEID", "GENENAME", "TXID", "TXBIOTYPE"))
## Note: ordering of the results might not match ordering of keys!
## GENENAME TXID TXBIOTYPE GENEID
## 1 BCL2L11 ENST00000432179 protein_coding ENSG00000153094
## 2 BCL2L11 ENST00000308659 protein_coding ENSG00000153094
## 3 BCL2L11 ENST00000393256 protein_coding ENSG00000153094
## 4 BCL2L11 ENST00000393252 protein_coding ENSG00000153094
## 5 BCL2L11 ENST00000405953 protein_coding ENSG00000153094
## 6 BCL2L11 ENST00000438054 protein_coding ENSG00000153094
## 7 BCL2L11 ENST00000357757 protein_coding ENSG00000153094
## 8 BCL2L11 ENST00000393253 protein_coding ENSG00000153094
## 9 BCL2L11 ENST00000337565 protein_coding ENSG00000153094
## 10 BCL2 ENST00000398117 protein_coding ENSG00000171791
## 11 BCL2 ENST00000333681 protein_coding ENSG00000171791
## 12 BCL2 ENST00000589955 protein_coding ENSG00000171791
## 13 BCL2 ENST00000444484 protein_coding ENSG00000171791
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")
## BCL2 BCL2L11
## "ENST00000398117" "ENST00000432179"
## Alternatively, specify multiVals="list" to return all mappings.
mapIds(edb, keys = c("BCL2", "BCL2L11"), column = "TXID", keytype = "GENENAME",
multiVals = "list")
## $BCL2
## [1] "ENST00000398117" "ENST00000333681" "ENST00000590515" "ENST00000589955"
## [5] "ENST00000444484"
##
## $BCL2L11
## [1] "ENST00000432179" "ENST00000308659" "ENST00000393256"
## [4] "ENST00000393252" "ENST00000433098" "ENST00000405953"
## [7] "ENST00000415458" "ENST00000436733" "ENST00000437029"
## [10] "ENST00000452231" "ENST00000361493" "ENST00000431217"
## [13] "ENST00000439718" "ENST00000438054" "ENST00000357757"
## [16] "ENST00000393253" "ENST00000337565"
## 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")
## Warning in .mapIds(x = x, keys = keys, column = column, keytype =
## keytype, : Got 2 filter objects. Will use the keys of the first for the
## mapping!
## Note: ordering of the results might not match ordering of keys!
## $BCL2L11
## [1] "ENST00000432179" "ENST00000308659" "ENST00000393256" "ENST00000393252"
## [5] "ENST00000405953" "ENST00000438054" "ENST00000357757" "ENST00000393253"
## [9] "ENST00000337565"
##
## $BCL2
## [1] "ENST00000398117" "ENST00000333681" "ENST00000589955" "ENST00000444484"
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):
The ordering of the results returned by the genes, exons, transcripts methods can be specified with the order.by parameter. The ordering of the results does however not correspond to the ordering of values in submitted filter objects. The only exception is the select method where the ordering of the results matches the ordering of the keys or of the values of the filter is a single filter is submitted.
Results of exonsBy, transcriptsBy are always ordered by the by argument.
The CDS provided by EnsDb objects always includes both, the start and the stop codon.
Transcripts with multiple CDS are at present not supported by EnsDb.
At present, EnsDb support only genes/transcripts for which all of their exons are encoded on the same chromosome and the same strand.
Building an transcript centric database package based on Ensembl annotation
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 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.
Requirements
The fetchTablesFromEnsembl function of the package uses the Ensembl Perl API to retrieve the required annotations from an Ensembl database (e.g. from the main site ensembldb.ensembl.org). Thus, to use the functionality to built databases, the Ensembl Perl API needs to be installed (see for details).
Alternatively, the ensDbFromAH, ensDbFromGff, ensDbFromGRanges and ensDbFromGtf functions allow to build EnsDb SQLite files from a GRanges object or GFF/GTF files from Ensembl. 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.
Building an annotation package
The functions below use the Ensembl Perl API to fetch the required data directly from the Ensembl core databases. Thus, the path to the Perl API specific for the desired Ensembl version needs to be added to the PERL5LIB environment variable.
An annotation package containing all human genes for Ensembl version 75 can be created using the code in the block below.
library(ensembldb)
## 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 <johannes.rainer@eurac.edu>",
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 =“mysql-eg-publicsql.ebi.ac.uk”=, port to 4157 and species to e.g. =“arabidopsis thaliana”=.
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.
library(AnnotationHub)
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)
library(Rsamtools)
## 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"))
Y
## GRanges object with 7155 ranges and 16 metadata columns:
## seqnames ranges strand | source
## <Rle> <IRanges> <Rle> | <factor>
## [1] Y [2652790, 2652894] + | snRNA
## [2] Y [2652790, 2652894] + | snRNA
## [3] Y [2652790, 2652894] + | snRNA
## [4] Y [2654896, 2655740] - | protein_coding
## [5] Y [2654896, 2655740] - | protein_coding
## ... ... ... ... . ...
## [7151] Y [28772667, 28773306] - | processed_pseudogene
## [7152] Y [28772667, 28773306] - | processed_pseudogene
## [7153] Y [59001391, 59001635] + | pseudogene
## [7154] Y [59001391, 59001635] + | processed_pseudogene
## [7155] Y [59001391, 59001635] + | processed_pseudogene
## type score phase gene_id gene_name
## <factor> <numeric> <integer> <character> <character>
## [1] gene <NA> <NA> ENSG00000251841 RNU6-1334P
## [2] transcript <NA> <NA> ENSG00000251841 RNU6-1334P
## [3] exon <NA> <NA> ENSG00000251841 RNU6-1334P
## [4] gene <NA> <NA> ENSG00000184895 SRY
## [5] transcript <NA> <NA> ENSG00000184895 SRY
## ... ... ... ... ... ...
## [7151] transcript <NA> <NA> ENSG00000231514 FAM58CP
## [7152] exon <NA> <NA> ENSG00000231514 FAM58CP
## [7153] gene <NA> <NA> ENSG00000235857 CTBP2P1
## [7154] transcript <NA> <NA> ENSG00000235857 CTBP2P1
## [7155] exon <NA> <NA> ENSG00000235857 CTBP2P1
## gene_source gene_biotype transcript_id transcript_name
## <character> <character> <character> <character>
## [1] ensembl snRNA <NA> <NA>
## [2] ensembl snRNA ENST00000516032 RNU6-1334P-201
## [3] ensembl snRNA ENST00000516032 RNU6-1334P-201
## [4] ensembl_havana protein_coding <NA> <NA>
## [5] ensembl_havana protein_coding ENST00000383070 SRY-001
## ... ... ... ... ...
## [7151] havana pseudogene ENST00000435741 FAM58CP-001
## [7152] havana pseudogene ENST00000435741 FAM58CP-001
## [7153] havana pseudogene <NA> <NA>
## [7154] havana pseudogene ENST00000431853 CTBP2P1-001
## [7155] havana pseudogene ENST00000431853 CTBP2P1-001
## transcript_source exon_number exon_id tag
## <character> <numeric> <character> <character>
## [1] <NA> <NA> <NA> <NA>
## [2] ensembl <NA> <NA> <NA>
## [3] ensembl 1 ENSE00002088309 <NA>
## [4] <NA> <NA> <NA> <NA>
## [5] ensembl_havana <NA> <NA> CCDS
## ... ... ... ... ...
## [7151] havana <NA> <NA> <NA>
## [7152] havana 1 ENSE00001616687 <NA>
## [7153] <NA> <NA> <NA> <NA>
## [7154] havana <NA> <NA> <NA>
## [7155] havana 1 ENSE00001794473 <NA>
## ccds_id protein_id
## <character> <character>
## [1] <NA> <NA>
## [2] <NA> <NA>
## [3] <NA> <NA>
## [4] <NA> <NA>
## [5] CCDS14772 <NA>
## ... ... ...
## [7151] <NA> <NA>
## [7152] <NA> <NA>
## [7153] <NA> <NA>
## [7154] <NA> <NA>
## [7155] <NA> <NA>
## -------
## seqinfo: 1 sequence from GRCh37 genome
DB <- ensDbFromGRanges(Y, path=tempdir(), version = 75,
organism = "Homo_sapiens")
## Warning in ensDbFromGRanges(Y, path = tempdir(), version = 75, organism
## = "Homo_sapiens"): I'm missing column(s): 'entrezid'. The corresponding
## database column(s) will be empty!
edb <- EnsDb(DB)
edb
## EnsDb for Ensembl:
## |Db type: EnsDb
## |Type of Gene ID: Ensembl Gene ID
## |Supporting package: ensembldb
## |Db created by: ensembldb package from Bioconductor
## |script_version: 0.0.1
## |Creation time: Thu Jun 30 22:09:41 2016
## |ensembl_version: 75
## |ensembl_host: unknown
## |Organism: Homo_sapiens
## |genome_build: GRCh37
## |DBSCHEMAVERSION: 1.0
## |source_file: GRanges object
## | No. of genes: 495.
## | No. of transcripts: 731.
## As shown in the example below, we could make an EnsDb package on
## this DB object using the makeEnsembldbPackage function.
Alternatively we can build the annotation database using the ensDbFromGtf ensDbFromGff functions, that extracts most of the required data from a GTF respectively GFF (version 3) file which can be downloaded from Ensembl (e.g. from ftp://ftp.ensembl.org/pub/release-75/gtf/homo_sapiens for human gene definitions from Ensembl version 75; for plant genomes etc files can be retrieved from ftp://ftp.ensemblgenomes.org). All information except the chromosome lengths and the NCBI Entrezgene IDs 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.
library(ensembldb)
## the GTF file can be downloaded from
## ftp://ftp.ensembl.org/pub/release-75/gtf/homo_sapiens/
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 <johannes.rainer@eurac.edu>",
author = "J Rainer")
Database layout
The database consists of the following tables and attributes (the layout is also shown in Figure 110):
- gene: all gene specific annotations.
gene_id: the Ensembl ID of the gene.gene_name: the name (symbol) of the gene.entrezid: the NCBI Entrezgene ID(s) of the gene. Note that this can be a ; separated list of IDs for genes that are mapped to more than one Entrezgene.gene_biotype: the biotype of the gene.gene_seq_start: the start coordinate of the gene on the sequence (usually a chromosome).gene_seq_end: the end coordinate of the gene on the sequence.seq_name: the name of the sequence (usually the chromosome name).seq_strand: the strand on which the gene is encoded.seq_coord_system: the coordinate system of the sequence.
- tx: all transcript related annotations.
tx_id: the Ensembl transcript ID.tx_biotype: the biotype of the transcript.tx_seq_start: the start coordinate of the transcript.tx_seq_end: the end coordinate of the transcript.tx_cds_seq_start: the start coordinate of the coding region of the transcript (NULL for non-coding transcripts).tx_cds_seq_end: the end coordinate of the coding region of the transcript.gene_id: the gene to which the transcript belongs.
- exon: all exon related annotation.
exon_id: the Ensembl exon ID.exon_seq_start: the start coordinate of the exon.exon_seq_end: the end coordinate of the exon.
- tx2exon: provides the n:m mapping between transcripts and exons.
tx_id: the Ensembl transcript ID.exon_id: the Ensembl exon ID.exon_idx: the index of the exon in the corresponding transcript, always from 5’ to 3’ of the transcript.
- chromosome: provides some information about the chromosomes.
seq_name: the name of the sequence/chromosome.seq_length: the length of the sequence.is_circular: whether the sequence in circular.
- information: some additional, internal, informations (Genome build, Ensembl version etc).
