Martin Morgan
October 28, 2014
Organism-level ('org') packages contain mappings between a central
identifier (e.g., Entrez gene ids) and other identifiers (e.g. GenBank
or Uniprot accession number, RefSeq id, etc.). The name of an org
package is always of the form org.<Sp>.<id>.db
(e.g. org.Sc.sgd.db) where <Sp> is a 2-letter abbreviation of
the organism (e.g. Sc for Saccharomyces cerevisiae) and <id> is
an abbreviation (in lower-case) describing the type of central
identifier (e.g. sgd for gene identifiers assigned by the
Saccharomyces Genome Database, or eg for Entrez gene ids). The
“How to use the '.db' annotation packages” vignette in the
[AnnotationDbi][] package (org packages are only one type of “.db”
annotation packages) is a key reference. The '.db' and most other
Bioconductor annotation packages are updated every 6 months.
Annotation packages usually contain an object named after the package
itself. These objects are collectively called AnnotationDb objects,
with more specific classes named OrgDb, ChipDb or TranscriptDb
objects. Methods that can be applied to these objects include
cols(), keys(), keytypes() and select(). Common operations
for retrieving annotations are summarized in the table.
| Category | Function | Description |
|---|---|---|
| Discover | columns() |
List the kinds of columns that can be returned |
keytypes() |
List columns that can be used as keys | |
keys() |
List values that can be expected for a given keytype | |
select() |
Retrieve annotations matching keys, keytype and columns |
|
| Manipulate | setdiff(), union(), intersect() |
Operations on sets |
duplicated(), unique() |
Mark or remove duplicates | |
%in%, match() |
Find matches | |
any(), all() |
Are any TRUE? Are all? |
|
merge() |
Combine two different \Robject{data.frames} based on shared keys | |
GRanges* |
transcripts(), exons(), cds() |
Features (transcripts, exons, coding sequence) as GRanges. |
transcriptsBy() , exonsBy() |
Features group by gene, transcript, etc., as GRangesList. |
|
cdsBy() |
Exercise: This exercise illustrates basic use of the select' interface to annotation packages.
OrgDb object for the org.Hs.eg.db package. Use
the columns() method to discover which sorts of annotations can
be extracted from it.keys() method to extract ENSEMBL identifiers and then
pass those keys in to the select() method in such a way that you
extract the SYMBOL (gene symbol) and GENENAME information for
each. Use the following ENSEMBL ids.ensids <- c("ENSG00000130720", "ENSG00000103257", "ENSG00000156414",
"ENSG00000144644", "ENSG00000159307", "ENSG00000144485")
Solution The OrgDb object is named org.Hs.eg.db.
library(org.Hs.eg.db)
keytypes(org.Hs.eg.db)
## [1] "ENTREZID" "PFAM" "IPI" "PROSITE"
## [5] "ACCNUM" "ALIAS" "CHR" "CHRLOC"
## [9] "CHRLOCEND" "ENZYME" "MAP" "PATH"
## [13] "PMID" "REFSEQ" "SYMBOL" "UNIGENE"
## [17] "ENSEMBL" "ENSEMBLPROT" "ENSEMBLTRANS" "GENENAME"
## [21] "UNIPROT" "GO" "EVIDENCE" "ONTOLOGY"
## [25] "GOALL" "EVIDENCEALL" "ONTOLOGYALL" "OMIM"
## [29] "UCSCKG"
columns(org.Hs.eg.db)
## [1] "ENTREZID" "PFAM" "IPI" "PROSITE"
## [5] "ACCNUM" "ALIAS" "CHR" "CHRLOC"
## [9] "CHRLOCEND" "ENZYME" "MAP" "PATH"
## [13] "PMID" "REFSEQ" "SYMBOL" "UNIGENE"
## [17] "ENSEMBL" "ENSEMBLPROT" "ENSEMBLTRANS" "GENENAME"
## [21] "UNIPROT" "GO" "EVIDENCE" "ONTOLOGY"
## [25] "GOALL" "EVIDENCEALL" "ONTOLOGYALL" "OMIM"
## [29] "UCSCKG"
cols <- c("SYMBOL", "GENENAME")
select(org.Hs.eg.db, keys=ensids, columns=cols, keytype="ENSEMBL")
## ENSEMBL SYMBOL
## 1 ENSG00000130720 FIBCD1
## 2 ENSG00000103257 SLC7A5
## 3 ENSG00000156414 TDRD9
## 4 ENSG00000144644 GADL1
## 5 ENSG00000159307 SCUBE1
## 6 ENSG00000144485 HES6
## GENENAME
## 1 fibrinogen C domain containing 1
## 2 solute carrier family 7 (amino acid transporter light chain, L system), member 5
## 3 tudor domain containing 9
## 4 glutamate decarboxylase-like 1
## 5 signal peptide, CUB domain, EGF-like 1
## 6 hes family bHLH transcription factor 6
A short summary of select Bioconductor packages enabling web-based queries is in following Table.
| Package | Description |
|---|---|
| AnnotationHub | Ensembl, Encode, dbSNP, UCSC data objects |
| biomaRt | Ensembl and other annotations |
| PSICQUIC | Protein interactions |
| uniprot.ws | Protein annotations |
| KEGGREST | KEGG pathways |
| SRAdb | Sequencing experiments. |
| rtracklayer | genome tracks. |
| GEOquery | Array and other data |
| ArrayExpress] | Array and other data |
Using biomaRt
The biomaRt package offers access to the online
biomart resource. this consists of several
data base resources, referred to as 'marts'. Each mart allows access
to multiple data sets; the biomaRt package provides methods for
mart and data set discovery, and a standard method getBM() to
retrieve data.
Exercise
getBM(). In addition to
the mart to be accessed, this function takes filters and attributes
as arguments. Use filterOptions() and listAttributes() to
discover values for these arguments. Call getBM() using filters
and attributes of your choosing.Solution
## NEEDS INTERNET ACCESS !!
library(biomaRt)
head(listMarts(), 3) ## list the marts
head(listDatasets(useMart("ensembl")), 3) ## mart datasets
ensembl <- ## fully specified mart
useMart("ensembl", dataset = "hsapiens_gene_ensembl")
head(listFilters(ensembl), 3) ## filters
myFilter <- "chromosome_name"
substr(filterOptions(myFilter, ensembl), 1, 50) ## return values
myValues <- c("21", "22")
head(listAttributes(ensembl), 3) ## attributes
myAttributes <- c("ensembl_gene_id","chromosome_name")
## assemble and query the mart
res <- getBM(attributes = myAttributes, filters = myFilter,
values = myValues, mart = ensembl)
Exercise
As an optional exercise, annotate the genes that are differentially
expressed in the DESeq2 laboratory, e.g., find the \texttt{GENENAME}
associated with the five most differentially expressed genes. Do these
make biological sense? Can you merge() the annotation results with
the top table' results to provide a statistically and biologically
informative summary?
There are a diversity of packages and classes available for representing large genomes. Several include:
available.packages() for pre-packaged genomes, and the vignette
'How to forge a BSgenome data package' in theFaFile() (Rsamtools) for accessing indexed FASTA files.Genome-centric packages are very useful for annotations involving genomic coordinates. It is straight-forward, for instance, to discover the coordinates of coding sequences in regions of interest, and from these retrieve corresponding DNA or protein coding sequences. Other examples of the types of operations that are easy to perform with genome-centric annotations include defining regions of interest for counting aligned reads in RNA-seq experiments and retrieving DNA sequences underlying regions of interest in ChIP-seq analysis, e.g., for motif characterization.
Exercise
This exercise uses annotation resources to go from a gene symbol 'BRCA1' through to the genomic coordinates of each transcript associated with the gene, and finally to the DNA sequences of the transcripts.
select
command.TXNAME) corresponding to the BRCA1 Entrez
identifier. (The 'org*' packages are based on information from
NCBI, where Entrez identifiers are labeled ENTREZID; the 'TxDb*'
package we are using is from UCSC, where Entrez identifiers are
labelled GENEID).Use the cdsBy() function to retrieve the genomic coordinates of
all coding sequences grouped by transcript, and select the
transcripts corresponding to the identifiers we're interested
in. The coding sequences are returned as an GRangesList, where
each element of the list is a GRanges object representing the
exons in the coding sequence. As a sanity check, ensure that the
sum of the widths of the exons in each coding sequence is evenly
divisble by 3 (the R 'modulus' operator %% returns the remainder
of the division of one number by another, and might be helpful in
this case).
Visualize the transcripts in genomic coordinates using the [Gviz][]
package to construct an AnnotationTrack, and plotting it using
plotTracks().
Use the Bsgenome.Hsapiens.UCSC.hg19 package and
extractTranscriptSeqs() function to extract the DNA sequence of
each transcript.
Solution
Retrieve the Entrez identifier corresponding to the BRCA1 gene symbol
library(org.Hs.eg.db)
eid <- select(org.Hs.eg.db, "BRCA1", "ENTREZID", "SYMBOL")[["ENTREZID"]]
Map from Entrez gene identifier to transcript name
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene
txid <- select(txdb, eid, "TXNAME", "GENEID")[["TXNAME"]]
## Warning in .generateExtraRows(tab, keys, jointype): 'select' resulted in
## 1:many mapping between keys and return rows
Retrieve all coding sequences grouped by transcript, and select those matching the transcript ids of interest, verifying that each coding sequence width is a multiple of 3
cds <- cdsBy(txdb, by="tx", use.names=TRUE)
brca1cds <- cds[names(cds) %in% txid]
class(brca1cds)
## [1] "GRangesList"
## attr(,"package")
## [1] "GenomicRanges"
length(brca1cds)
## [1] 20
brca1cds[[1]] # exons in cds
## GRanges object with 22 ranges and 3 metadata columns:
## seqnames ranges strand | cds_id cds_name
## <Rle> <IRanges> <Rle> | <integer> <character>
## [1] chr17 [41276034, 41276113] - | 186246 <NA>
## [2] chr17 [41267743, 41267796] - | 186245 <NA>
## [3] chr17 [41258473, 41258550] - | 186243 <NA>
## [4] chr17 [41256885, 41256973] - | 186241 <NA>
## [5] chr17 [41256139, 41256278] - | 186240 <NA>
## ... ... ... ... ... ... ...
## [18] chr17 [41209069, 41209152] - | 186218 <NA>
## [19] chr17 [41203080, 41203134] - | 186217 <NA>
## [20] chr17 [41201138, 41201211] - | 186215 <NA>
## [21] chr17 [41199660, 41199720] - | 186214 <NA>
## [22] chr17 [41197695, 41197819] - | 186212 <NA>
## exon_rank
## <integer>
## [1] 1
## [2] 2
## [3] 3
## [4] 4
## [5] 5
## ... ...
## [18] 18
## [19] 19
## [20] 20
## [21] 21
## [22] 22
## -------
## seqinfo: 93 sequences (1 circular) from hg19 genome
cdswidth <- width(brca1cds) # width of each exon
all((sum(cdswidth) %% 3) == 0) # sum within cds, modulus 3
## [1] TRUE
Visualize the BRCA1 transcirpts using [Gviz])
require(Gviz)
anno <- AnnotationTrack(brca1cds)
plotTracks(list(GenomeAxisTrack(), anno))
Extract the coding sequences of each transcript
library(BSgenome.Hsapiens.UCSC.hg19)
genome <- BSgenome.Hsapiens.UCSC.hg19
tx_seq <- extractTranscriptSeqs(genome, brca1cds)
tx_seq
## A DNAStringSet instance of length 20
## width seq names
## [1] 2280 ATGGATTTATCTGCTCTTCG...TCCCCCACAGCCACTACTGA uc010whl.2
## [2] 5379 ATGAGCCTACAAGAAAGTAC...TCCCCCACAGCCACTACTGA uc002icp.4
## [3] 522 ATGGATGCTGAGTTTGTGTG...TCCCCCACAGCCACTACTGA uc010whm.2
## [4] 2100 ATGGATTTATCTGCTCTTCG...GCAATTGGGCAGATGTGTGA uc002icu.3
## [5] 5451 ATGCTGAAACTTCTCAACCA...TCCCCCACAGCCACTACTGA uc010cyx.3
## ... ... ...
## [16] 4095 ATGGATTTATCTGCTCTTCG...AAAGCATGGATTCAAACTTA uc010cyy.1
## [17] 4095 ATGGATTTATCTGCTCTTCG...AAAGCATGGATTCAAACTTA uc010whs.1
## [18] 3954 ATGCTGAAACTTCTCAACCA...AAAGCATGGATTCAAACTTA uc010cyz.2
## [19] 4017 ATGGATTTATCTGCTCTTCG...AAAGCATGGATTCAAACTTA uc010cza.2
## [20] 3207 ATGAATGTAGAAAAGGCTGA...AAAGCATGGATTCAAACTTA uc010wht.1
Intron coordinates can be identified by first calculating the range of the genome (from the start of the first exon to the end of the last exon) covered by each transcript, and then taking the (algebraic) set difference between this and the genomic coordinates covered by each exon
introns <- psetdiff(range(brca1cds), brca1cds)
Retrieve the intronic sequences with getSeq() (these are not
assembled, the way that extractTranscriptSeqs() assembles exon
sequences into mature transcripts); note that introns start and end
with the appropriate acceptor and donor site sequences.
seq <- getSeq(genome, introns)
names(seq)
## [1] "uc010whl.2" "uc002icp.4" "uc010whm.2" "uc002icu.3" "uc010cyx.3"
## [6] "uc002icq.3" "uc002ict.3" "uc010whn.2" "uc010who.3" "uc010whp.2"
## [11] "uc010whq.1" "uc002idc.1" "uc010whr.1" "uc002idd.3" "uc002ide.1"
## [16] "uc010cyy.1" "uc010whs.1" "uc010cyz.2" "uc010cza.2" "uc010wht.1"
seq[["uc010whl.2"]] # 21 introns
## A DNAStringSet instance of length 21
## width seq
## [1] 1840 GTAAGGTGCCTGCATGTACCTGTGCTATATG...CTAATCTCTGCTTGTGTTCTCTGTCTCCAG
## [2] 1417 GTAAGTATTGGGTGCCCTGTCAGAGAGGGAG...TTGAATGCTCTTTCCTTCCTGGGGATCCAG
## [3] 1868 GTAAGAGCCTGGGAGAACCCCAGAGTTCCAG...AGTGATTTTACATCTAAATGTCCATTTTAG
## [4] 5934 GTAAAGCTCCCTCCCTCAAGTTGACAAAAAT...CTGTCCCTCTCTCTTCCTCTCTTCTTCCAG
## [5] 6197 GTAAGTACTTGATGTTACAAACTAACCAGAG...TCCTGATGGGTTGTGTTTGGTTTCTTTCAG
## ... ... ...
## [17] 4241 GTAAAACCATTTGTTTTCTTCTTCTTCTTCT...TGCTTGACTGTTCTTTACCATACTGTTTAG
## [18] 606 GTAAGTGTTGAATATCCCAAGAATGACACTC...AAACATAATGTTTTCCCTTGTATTTTACAG
## [19] 1499 GTATATAATTTGGTAATGATGCTAGGTTGGA...GAGTGTGTTTCTCAAACAATTTAATTTCAG
## [20] 9192 GTAAGTTTGAATGTGTTATGTGGCTCCATTA...AATTGTTCTTTCTTTCTTTATAATTTATAG
## [21] 8237 GTAAGTCAGCACAAGAGTGTATTAATTTGGG...TTTTCTTTTTCTCCCCCCCTACCCTGCTAG
The rtracklayer package allows us to query the UCSC genome
browser, as well as providing import() and
export() functions for common annotation file formats like
GFF, GTF, and BED.
Exercise
Here we use rtracklayer to retrieve estrogen receptor binding sites identified across cell lines in the ENCODE project. We focus on binding sites in the vicinity of a particularly interesting region of interest.
GRanges instance with
appropriate genomic coordinates. Our region corresponds to 10Mb up-
and down-stream of a particular gene.Solution
Define the region of interest
library(GenomicRanges)
roi <- GRanges("chr10", IRanges(92106877, 112106876, names="ENSG00000099194"))
Create a session
library(rtracklayer)
session <- browserSession()
Query the UCSC for a particular track, table, and transcription factor, in our region of interest
trackName <- "wgEncodeRegTfbsClusteredV2"
tableName <- "wgEncodeRegTfbsClusteredV2"
trFactor <- "ERalpha_a"
ucscTable <- getTable(ucscTableQuery(session, track=trackName,
range=roi, table=tableName, name=trFactor))
Visualize the result
plot(score ~ chromStart, ucscTable, pch="+")
abline(v=start(roi) + (end(roi) - start(roi) + 1) / 2, col="blue")
Follow the Variants work flow.