Annotating RangesAnnotating RangesBioconductor can import diverse sequence-related file types, including fasta, fastq, BAM, gff, bed, and wig files, among others. Packages support common and advanced sequence manipulation operations such as trimming, transformation, and alignment. Domain-specific analyses include quality assessment, ChIP-seq, differential expression, RNA-seq, and other approaches. Bioconductor includes an interface to the Sequence Read Archive (via the SRAdb package).
This workflow walks through the annotation of a generic set of ranges with Bioconductor packages. The ranges can be any user-defined region of interest or can be from a public file.
As a first step, data are put into a GRanges object so we can take advantage of overlap operations and store identifiers as metadata columns.
The first set of ranges are variants from a dbSNP Variant Call Format (VCF) file. This file can be downloaded from the ftp site at NCBI ftp://ftp.ncbi.nlm.nih.gov/snp/ and imported with readVcf() from the VariantAnnotation package. Alternatively, the file is available as a pre-parsed VCF object in the AnnotationHub.
## Note: the specification for S3 class "AsIs" in package 'RJSONIO' seems equivalent to one from package 'BiocGenerics': not turning on duplicate class definitions for this class.
library(VariantAnnotation)
library(AnnotationHub)
hub <- AnnotationHub()
vcf <- hub$dbSNP.organisms.human_9606.VCF.ByChromosome.22.12159.GIH.RData
dim(vcf)
## [1] 19698 88
When performing overlap operations the seqlevels and genome of the objects must match. Here were modify the VCF to match the TranscriptDb.
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
txdb_hg19 <- TxDb.Hsapiens.UCSC.hg19.knownGene
head(seqlevels(txdb_hg19))
## [1] "chr1" "chr2" "chr3" "chr4" "chr5" "chr6"
seqlevels(vcf)
## [1] "22"
seqlevels(vcf) <- paste0("chr", seqlevels(vcf))
unique(genome(txdb_hg19))
## [1] "hg19"
genome(vcf) <- "hg19"
Sanity check to confirm we have matching seqlevels.
intersect(seqlevels(txdb_hg19), seqlevels(vcf))
## [1] "chr22"
The GRanges in a VCF object is in the 'rowData' slot.
gr_hg19 <- rowData(vcf)
The second set of ranges is a user-defined region of chromosome 4 in mouse. The idea here is that any region, known or unknown, can be annotated with the following steps.
library(TxDb.Mmusculus.UCSC.mm10.ensGene)
txdb_mm10 <- TxDb.Mmusculus.UCSC.mm10.ensGene
We are creating the GRanges from scratch and can specify the seqlevels (chromosome names) to match the TranscriptDb.
head(seqlevels(txdb_mm10))
## [1] "chr1" "chr2" "chr3" "chr4" "chr5" "chr6"
gr_mm10 <- GRanges("chr4", IRanges(c(4000000, 107889000), width=1000))
Now assign the genome.
unique(genome(txdb_mm10))
## [1] "mm10"
genome(gr_mm10) <- "mm10"
locateVariants() in the VariantAnnotation package annotates ranges with transcript, exon, cds and gene ID's from a TranscriptDb. Various extractions are performed on the TranscriptDb (exonsBy(), transcripts(), cdsBy(), etc.) and the result is overlapped with the ranges. An appropriate GRangesList can also be supplied as the annotation. Different variants such as 'coding', 'fiveUTR', 'threeUTR', 'spliceSite', 'intron', 'promoter', and 'intergenic' can be searched for by passing the appropriate constructor as the 'region' argument. See ?locateVariants for details.
loc_hg19 <- locateVariants(gr_hg19, txdb_hg19, AllVariants())
## Warning: trimmed start values to be positive
## Warning: trimmed end values to be <= seqlengths
table(loc_hg19$LOCATION)
##
## spliceSite intron fiveUTR threeUTR coding intergenic
## 6 36416 246 1586 1690 8877
## promoter
## 2425
loc_mm10 <- locateVariants(gr_mm10, txdb_mm10, AllVariants())
## Warning: trimmed start values to be positive
## Warning: trimmed end values to be <= seqlengths
table(loc_mm10$LOCATION)
##
## spliceSite intron fiveUTR threeUTR coding intergenic
## 6 1 0 0 0 0
## promoter
## 12
The ID's returned from locateVariants() can be used in select() to map to ID's in other annotation packages.
library(org.Hs.eg.db)
cols <- c("UNIPROT", "PFAM")
keys <- unique(loc_hg19$GENEID)
head(select(org.Hs.eg.db, keys, cols, keytype="ENTREZID"))
## ENTREZID UNIPROT PFAM
## 1 150160 Q96SF2 PF00118
## 2 387590 <NA> <NA>
## 3 23783 <NA> <NA>
## 4 150165 Q5GH77 PF09815
## 5 27437 <NA> <NA>
## 6 128954 Q2WGN9 PF00169
The 'keytype' argument specifies that the mouse TranscriptDb contains Ensembl instead of Entrez gene id's.
library(org.Mm.eg.db)
keys <- unique(loc_mm10$GENEID)
head(select(org.Mm.eg.db, keys, cols, keytype="ENSEMBL"))
## ENSEMBL UNIPROT PFAM
## 1 ENSMUSG00000028236 Q7TQA3 PF00106
## 2 ENSMUSG00000028608 Q8BHG2 PF05907
Files stored in the AnnotationHub have been pre-processed into ranged-based R objects such as a GRanges, GAlignments and VCF. The positions in our GRanges can be overlapped with the ranges in the AnnotationHub files. This allows for easy subsetting of multiple files, resulting in only the ranges of interest.
Create a 'hub' from AnnotationHub and filter the files based on organism.
hub <- AnnotationHub()
filters(hub) <- list(Species = "Homo sapiens")
hg19_files <- names(hub)[grepl("hg19", names(hub))]
length(hg19_files)
## [1] 4609
Extract the matching ranges from the first 3 files.
ov_hg19 <- lapply(hg19_files[1:3], function(x)
subsetByOverlaps(hub$[[x]], gr_hg19))
Take a look at the results.
names(ov_hg19) <- hg19_files[1:3]
lapply(ov_hg19, head, n=3)
## $goldenpath.hg19.encodeDCC.wgEncodeSydhTfbs.wgEncodeSydhTfbsHepg2Brca1a300IggrabPk.narrowPeak_0.0.1.RData
## GRanges with 3 ranges and 6 metadata columns:
## seqnames ranges strand | name score
## <Rle> <IRanges> <Rle> | <character> <integer>
## [1] chr22 [43010733, 43011938] * | . 1000
## [2] chr22 [21355953, 21356739] * | . 1000
## [3] chr22 [21212791, 21213563] * | . 778
## signalValue pValue qValue peak
## <numeric> <numeric> <numeric> <integer>
## [1] 6.027 52.80 50.45 230
## [2] 12.615 23.34 21.41 420
## [3] 10.254 15.38 13.68 341
## ---
## seqlengths:
## chr1 chr10 chr11 ... chr9 chrX chrY
## 249250621 135534747 135006516 ... 141213431 155270560 59373566
##
## $goldenpath.hg19.encodeDCC.wgEncodeAffyRnaChip.wgEncodeAffyRnaChipFiltTransfragsGm12878CellTotal.broadPeak_0.0.1.RData
## GRanges with 3 ranges and 5 metadata columns:
## seqnames ranges strand | name score
## <Rle> <IRanges> <Rle> | <character> <integer>
## [1] chr22 [16886836, 16886882] * | . 0
## [2] chr22 [17012922, 17012968] * | . 0
## [3] chr22 [17306056, 17306167] * | . 0
## signalValue pValue qValue
## <numeric> <numeric> <numeric>
## [1] 177.7 -1 -1
## [2] 216.4 -1 -1
## [3] 147.8 -1 -1
## ---
## seqlengths:
## chr1 chr10 chr11 ... chrM chrX chrY
## 249250621 135534747 135006516 ... 16571 155270560 59373566
##
## $goldenpath.hg19.encodeDCC.wgEncodeAffyRnaChip.wgEncodeAffyRnaChipFiltTransfragsGm12878CytosolLongnonpolya.broadPeak_0.0.1.RData
## GRanges with 3 ranges and 5 metadata columns:
## seqnames ranges strand | name score
## <Rle> <IRanges> <Rle> | <character> <integer>
## [1] chr22 [17326625, 17326691] * | . 0
## [2] chr22 [17490889, 17490950] * | . 0
## [3] chr22 [17493505, 17493561] * | . 0
## signalValue pValue qValue
## <numeric> <numeric> <numeric>
## [1] 123.7 -1 -1
## [2] 167.1 -1 -1
## [3] 135.6 -1 -1
## ---
## seqlengths:
## chr1 chr10 chr11 ... chrM chrX chrY
## 249250621 135534747 135006516 ... 16571 155270560 59373566
Annotating the mouse ranges in the same fashion is left as an excercise.
For the set of dbSNP variants that fall in coding regions, amino acid changes can be computed. The output contains one line for each variant-transcript match which can result in multiple lines for each variant.
library(BSgenome.Hsapiens.UCSC.hg19)
head(predictCoding(vcf, txdb_hg19, Hsapiens), 3)
## GRanges with 3 ranges and 17 metadata columns:
## seqnames ranges strand | paramRangeID
## <Rle> <IRanges> <Rle> | <factor>
## rs2236639 chr22 [17072483, 17072483] - | <NA>
## rs5747988 chr22 [17073066, 17073066] - | <NA>
## rs5748622 chr22 [17264565, 17264565] - | <NA>
## REF ALT QUAL FILTER
## <DNAStringSet> <DNAStringSetList> <numeric> <character>
## rs2236639 A G <NA> .
## rs5747988 A G <NA> .
## rs5748622 G T <NA> .
## varAllele CDSLOC PROTEINLOC QUERYID
## <DNAStringSet> <IRanges> <IntegerList> <integer>
## rs2236639 C [ 958, 958] 320 34
## rs5747988 C [ 375, 375] 125 35
## rs5748622 A [1324, 1324] 442 99
## TXID CDSID GENEID CONSEQUENCE REFCODON
## <character> <integer> <character> <factor> <DNAStringSet>
## rs2236639 74436 216505 150160 nonsynonymous TGG
## rs5747988 74436 216505 150160 synonymous GCT
## rs5748622 74439 216506 150165 nonsynonymous CAC
## VARCODON REFAA VARAA
## <DNAStringSet> <AAStringSet> <AAStringSet>
## rs2236639 CGG W R
## rs5747988 GCC A A
## rs5748622 AAC H N
## ---
## seqlengths:
## chr22
## NA
The ensemblVEP package provides access to the online Ensembl Variant Effect Predictor (VEP tool). The VEP tool ouputs predictions of functional consequences of known and unknown variants as reported by Sequence Ontology or Ensembl. Regulatory region consequences, HGNC, Ensembl protein identifiers, HGVS, co-located variants are optional outputs. ensemblVEP() accepts the name of a VCF file and returns a VCF on disk or GRanges in the R workspace.
library(ensemblVEP)
fl <- system.file("extdata", "ex2.vcf",
package="VariantAnnotation")
gr <- ensemblVEP(fl)
head(gr, 3)
## GRanges with 3 ranges and 13 metadata columns:
## seqnames ranges strand | Allele
## <Rle> <IRanges> <Rle> | <factor>
## rs6054257 20 [ 14370, 14370] * | A
## 20:17330_T/A 20 [ 17330, 17330] * | A
## rs6040355 20 [1110696, 1110696] * | T
## Gene Feature Feature_type
## <factor> <factor> <factor>
## rs6054257 <NA> <NA> <NA>
## 20:17330_T/A <NA> <NA> <NA>
## rs6040355 ENSG00000125818 ENST00000381898 Transcript
## Consequence cDNA_position CDS_position
## <factor> <factor> <factor>
## rs6054257 intergenic_variant <NA> <NA>
## 20:17330_T/A intergenic_variant <NA> <NA>
## rs6040355 intron_variant <NA> <NA>
## Protein_position Amino_acids Codons Existing_variation
## <factor> <factor> <factor> <factor>
## rs6054257 <NA> <NA> <NA> <NA>
## 20:17330_T/A <NA> <NA> <NA> <NA>
## rs6040355 <NA> <NA> <NA> <NA>
## DISTANCE STRAND
## <factor> <factor>
## rs6054257 <NA> <NA>
## 20:17330_T/A <NA> <NA>
## rs6040355 <NA> 1
## ---
## seqlengths:
## 20
## 62435964
Exercise 1: VCF header and reading data subsets.
VCF files can be large and it's often the case that only a subset of variables or genomic positions are of interest. The scanVcfHeader() function in the VariantAnnotation package retrieves header information from a VCF file. Based on the information returned from scanVcfHeader() a ScanVcfParam() object can be created to read in a subset of data from a VCF file.
Exercise 2: Annotate the mouse ranges in 'gr_mm10' with AnnotationHub files.
Exercise 3: Annotate a gene range from Saccharomyces Scerevisiae.
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