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library(EnsDb.Hsapiens.v75)
## Making a "short cut"
edb <- EnsDb.Hsapiens.v75
## print some informations for this package
edb
## For what organism was the database generated?
organism(edb)
## ----no-network, echo = FALSE, results = "hide"----------------------------
## Disable code chunks that require network connection - conditionally
## disable this on Windows only. This is to avoid TIMEOUT errors on the
## Bioconductor Windows build maching (issue #47).
use_network <- FALSE
## ----filters---------------------------------------------------------------
supportedFilters(edb)
## ----transcripts-----------------------------------------------------------
Tx <- transcripts(edb, filter = list(GenenameFilter("BCL2L11")))
Tx
## as this is a GRanges object we can access e.g. the start coordinates with
head(start(Tx))
## or extract the biotype with
head(Tx$tx_biotype)
## ----transcripts-filter-expression-----------------------------------------
## Use a filter expression to perform the filtering.
transcripts(edb, filter = ~ genename == "ZBTB16")
## ----transcripts-filter----------------------------------------------------
library(magrittr)
filter(edb, ~ symbol == "BCL2" & tx_biotype != "protein_coding") %>% transcripts
## ----filter-Y--------------------------------------------------------------
edb_y <- addFilter(edb, SeqNameFilter("Y"))
## All subsequent filters on that EnsDb will only work on features encoded on
## chromosome Y
genes(edb_y)
## Get all lincRNAs on chromosome Y
genes(edb_y, filter = ~ gene_biotype == "lincRNA")
## ----list-columns----------------------------------------------------------
## list all database tables along with their columns
listTables(edb)
## list columns from a specific table
listColumns(edb, "tx")
## ----transcripts-example2--------------------------------------------------
Tx <- transcripts(edb,
columns = c(listColumns(edb , "tx"), "gene_name"),
filter = TxBiotypeFilter("nonsense_mediated_decay"),
return.type = "DataFrame")
nrow(Tx)
Tx
## ----cdsBy-----------------------------------------------------------------
yCds <- cdsBy(edb, filter = SeqNameFilter("Y"))
yCds
## ----genes-GRangesFilter---------------------------------------------------
## Define the filter
grf <- GRangesFilter(GRanges("11", ranges = IRanges(114000000, 114000050),
strand = "+"), type = "any")
## Query genes:
gn <- genes(edb, filter = grf)
gn
## Next we retrieve all transcripts for that gene so that we can plot them.
txs <- transcripts(edb, filter = GenenameFilter(gn$gene_name))
## ----tx-for-zbtb16, message=FALSE, fig.align='center', fig.width=7.5, fig.height=5----
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)
}
## ----transcripts-GRangesFilter---------------------------------------------
transcripts(edb, filter = grf)
## ----biotypes--------------------------------------------------------------
## Get all gene biotypes from the database. The GeneBiotypeFilter
## allows to filter on these values.
listGenebiotypes(edb)
## Get all transcript biotypes from the database.
listTxbiotypes(edb)
## ----genes-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 = GenenameFilter("BCL", condition = "startsWith"),
return.type = "DataFrame")
nrow(BCLs)
BCLs
## ----example-AnnotationFilterList------------------------------------------
## determine the average length of snRNA, snoRNA and rRNA genes encoded on
## chromosomes X and Y.
mean(lengthOf(edb, of = "tx", filter = AnnotationFilterList(
GeneBiotypeFilter(c("snRNA", "snoRNA", "rRNA")),
SeqNameFilter(c("X", "Y")))))
## determine the average length of protein coding genes encoded on the same
## chromosomes.
mean(lengthOf(edb, of = "tx", filter = ~ gene_biotype == "protein_coding" &
seq_name %in% c("X", "Y")))
## ----example-first-two-exons-----------------------------------------------
## 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 = "<")))
## ----transcriptsBy-X-Y-----------------------------------------------------
TxByGns <- transcriptsBy(edb, by = "gene", filter = SeqNameFilter(c("X", "Y")))
TxByGns
## ----exonsBy-RNAseq, message = FALSE, eval = FALSE-------------------------
# ## 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")))
#
## ----toSAF-RNAseq, message = FALSE, eval=FALSE-----------------------------
# ## Transforming the GRangesList into a data.frame in SAF format
# EnsGenes.SAF <- toSAF(EnsGenes)
#
## ----disjointExons, message = FALSE, eval=FALSE----------------------------
# ## Create a GRanges of non-overlapping exon parts.
# DJE <- disjointExons(edb, filter = AnnotationFilterList(
# SeqNameFilter(c(1:22, "X", "Y")),
# GeneIdFilter("ENSG%", "startsWith")))
#
## ----transcript-sequence-AnnotationHub, message = FALSE, eval = FALSE------
# 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)
#
## ----transcript-sequence-extractTranscriptSeqs, message = FALSE, eval = FALSE----
# ## 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)
#
## ----seqlevelsStyle, message = FALSE---------------------------------------
## 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)
## ----seqlevelsStyle-2, message = FALSE-------------------------------------
seqlevelsStyle(edb) <- "UCSC"
## Getting the default option:
getOption("ensembldb.seqnameNotFound")
## Listing all seqlevels in the database.
seqlevels(edb)[1:30]
## 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]
## Resetting the option.
options(ensembldb.seqnameNotFound = "ORIGINAL")
## ----extractTranscriptSeqs-BSGenome, warning = FALSE, message = FALSE------
library(BSgenome.Hsapiens.UCSC.hg19)
bsg <- BSgenome.Hsapiens.UCSC.hg19
## Get the genome version
unique(genome(bsg))
unique(genome(edb))
## Although differently named, both represent genome build GRCh37.
## Extract the full transcript sequences.
yTxSeqs <- extractTranscriptSeqs(bsg, exonsBy(edb, "tx",
filter = SeqNameFilter("chrY")))
yTxSeqs
## Extract just the CDS
Test <- cdsBy(edb, "tx", filter = SeqNameFilter("chrY"))
yTxCds <- extractTranscriptSeqs(bsg, cdsBy(edb, "tx",
filter = SeqNameFilter("chrY")))
yTxCds
## ----seqlevelsStyle-restore------------------------------------------------
seqlevelsStyle(edb) <- "Ensembl"
## ----gviz-plot, message=FALSE, fig.align='center', fig.width=7.5, fig.height=2.3----
## 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)
## ----message=FALSE---------------------------------------------------------
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)
## Define a genome axis track
gat <- GenomeAxisTrack()
plotTracks(list(gat, GeneRegionTrack(gr)))
## ----gviz-separate-tracks, message=FALSE, warning=FALSE, fig.align='center', fig.width=7.5, fig.height=2.25----
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"
## ----pplot-plot, message=FALSE, fig.align='center', fig.width=7.5, fig.height=4----
library(ggbio)
## Create a plot for all transcripts of the gene SKA2
autoplot(edb, ~ genename == "SKA2")
## ----pplot-plot-2, message=FALSE, fig.align='center', fig.width=7.5, fig.height=4----
## Get the chromosomal region in which the gene is encoded
ska2 <- genes(edb, filter = ~ genename == "SKA2")
strand(ska2) <- "*"
autoplot(edb, GRangesFilter(ska2), names.expr = "gene_name")
## ----AnnotationDbi, message = FALSE----------------------------------------
library(EnsDb.Hsapiens.v75)
edb <- EnsDb.Hsapiens.v75
## List all available columns in the database.
columns(edb)
## 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)
## List all of the supported key types.
keytypes(edb)
## Get all gene ids from the database.
gids <- keys(edb, keytype = "GENEID")
length(gids)
## Get all gene names for genes encoded on chromosome Y.
gnames <- keys(edb, keytype = "GENENAME", filter = SeqNameFilter("Y"))
head(gnames)
## ----select, message = FALSE, warning=FALSE--------------------------------
## Use the /standard/ way to fetch data.
select(edb, keys = c("BCL2", "BCL2L11"), keytype = "GENENAME",
columns = c("GENEID", "GENENAME", "TXID", "TXBIOTYPE"))
## Use the filtering system of ensembldb
select(edb, keys = ~ genename %in% c("BCL2", "BCL2L11") &
tx_biotype == "protein_coding",
columns = c("GENEID", "GENENAME", "TXID", "TXBIOTYPE"))
## ----mapIds, message = FALSE-----------------------------------------------
## Use the default method, which just returns the first value for multi mappings.
mapIds(edb, keys = c("BCL2", "BCL2L11"), column = "TXID", keytype = "GENENAME")
## Alternatively, specify multiVals="list" to return all mappings.
mapIds(edb, keys = c("BCL2", "BCL2L11"), column = "TXID", keytype = "GENENAME",
multiVals = "list")
## And, just like before, we can use filters to map only to protein coding transcripts.
mapIds(edb, keys = list(GenenameFilter(c("BCL2", "BCL2L11")),
TxBiotypeFilter("protein_coding")), column = "TXID",
multiVals = "list")
## ----AnnotationHub-query, message = FALSE, eval = use_network--------------
# library(AnnotationHub)
# ## Load the annotation resource.
# ah <- AnnotationHub()
#
# ## Query for all available EnsDb databases
# query(ah, "EnsDb")
#
## ----AnnotationHub-query-2, message = FALSE, eval = use_network------------
# ahDb <- query(ah, pattern = c("Xiphophorus Maculatus", "EnsDb", 87))
# ## What have we got
# ahDb
#
## ----AnnotationHub-fetch, message = FALSE, eval = FALSE--------------------
# ahEdb <- ahDb[[1]]
#
# ## retriebe all genes
# gns <- genes(ahEdb)
#
## ----edb-from-ensembl, message = FALSE, eval = FALSE-----------------------
# 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")
#
## ----gtf-gff-edb, message = FALSE, eval = FALSE----------------------------
# ## 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"]]
#
## ----EnsDb-from-Y-GRanges, message = FALSE, eval = use_network-------------
# ## Generate a sqlite database from a GRanges object specifying
# ## genes encoded on chromosome Y
# load(system.file("YGRanges.RData", package = "ensembldb"))
# Y
#
# ## Create the EnsDb database file
# DB <- ensDbFromGRanges(Y, path = tempdir(), version = 75,
# organism = "Homo_sapiens")
#
# ## Load the database
# edb <- EnsDb(DB)
# edb
#
## ----EnsDb-from-GTF, message = FALSE, eval = FALSE-------------------------
# 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")
#
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