Bioconductor can import diverse sequence-related file types, including fasta, fastq, BAM, VCF, 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.
## Warning: package 'VariantAnnotation' was built under R version 3.4.3## Warning: package 'SummarizedExperiment' was built under R version 3.4.3## Warning: package 'rtracklayer' was built under R version 3.4.3## Warning: package 'biomaRt' was built under R version 3.4.3The Hub returns a VcfFile object with a reference to the file on disk.
hub <- AnnotationHub()## snapshotDate(): 2017-10-27Query the Hub for clinvar VCF files build GRCh37:
mcols(query(hub, "clinvar.vcf", "GRCh37"))[,"sourceurl", drop=FALSE]## DataFrame with 8 rows and 1 column
## sourceurl
## <character>
## AH57956 ftp://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh37/archive_1.0/2016/clinvar_20160203.vcf.gz
## AH57957 ftp://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh37/archive_1.0/2016/clinvar_20160203_papu.vcf.gz
## AH57958 ftp://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh37/archive_1.0/2016/common_and_clinical_20160203.vcf.gz
## AH57959 ftp://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh37/archive_1.0/2016/common_no_known_medical_impact_20160203.vcf.gz
## AH57960 ftp://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/archive_1.0/2016/clinvar_20160203.vcf.gz
## AH57961 ftp://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/archive_1.0/2016/clinvar_20160203_papu.vcf.gz
## AH57962 ftp://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/archive_1.0/2016/common_and_clinical_20160203.vcf.gz
## AH57963 ftp://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/archive_1.0/2016/common_no_known_medical_impact_20160203.vcf.gzRetrieve one of the files:
fl <- query(hub, "clinvar.vcf", "GRCh37")[[1]]## loading from cache 'C:/Rtools34/home/IP-AC1E008B$/AppData/.AnnotationHub/64694'
## 'C:/Rtools34/home/IP-AC1E008B$/AppData/.AnnotationHub/64695'Read the data into a VCF object:
vcf <- readVcf(fl, "hg19")
dim(vcf)## [1] 109721 0Overlap operations require that seqlevels and the genome of the objects match. Here the VCF seqlevels are modified to match the TxDb.
txdb_hg19 <- TxDb.Hsapiens.UCSC.hg19.knownGene
head(seqlevels(txdb_hg19))## [1] "chr1" "chr2" "chr3" "chr4" "chr5" "chr6"seqlevels(vcf)## [1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12" "13" "14"
## [15] "15" "16" "17" "18" "19" "20" "21" "22" "X" "Y" "MT"seqlevels(vcf) <- paste0("chr", seqlevels(vcf))For this example we’ll annotate chromosomes 3 and 18:
seqlevels(vcf, pruning.mode="coarse") <- c("chr3", "chr18")
seqlevels(txdb_hg19) <- c("chr3", "chr18")Sanity check to confirm we have matching seqlevels.
intersect(seqlevels(txdb_hg19), seqlevels(vcf))## [1] "chr3" "chr18"The genomes already match so no change is needed.
unique(genome(txdb_hg19))## [1] "hg19"unique(genome(vcf))## [1] "hg19"The GRanges in a VCF object is extracted with ‘rowRanges()’.
gr_hg19 <- rowRanges(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.
Load the TxDb package and keep only the standard chromosomes.
txdb_mm10 <- keepStandardChromosomes(TxDb.Mmusculus.UCSC.mm10.ensGene)We are creating the GRanges from scratch and can specify the seqlevels (chromosome names) to match the TxDb.
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 TxDb. Various extractions are performed on the TxDb (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())## 'select()' returned many:1 mapping between keys and columns
## 'select()' returned many:1 mapping between keys and columns
## 'select()' returned many:1 mapping between keys and columns
## 'select()' returned many:1 mapping between keys and columns
## 'select()' returned many:1 mapping between keys and columns
## 'select()' returned many:1 mapping between keys and columnstable(loc_hg19$LOCATION)##
## spliceSite intron fiveUTR threeUTR coding intergenic
## 1520 7522 2049 1596 28014 9
## promoter
## 2374loc_mm10 <- locateVariants(gr_mm10, txdb_mm10, AllVariants()) ## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columnstable(loc_mm10$LOCATION)##
## spliceSite intron fiveUTR threeUTR coding intergenic
## 6 1 0 0 0 0
## promoter
## 12The ID’s returned from locateVariants() can be used in select() to map to ID’s in other annotation packages.
cols <- c("UNIPROT", "PFAM")
keys <- na.omit(unique(loc_hg19$GENEID))
head(select(org.Hs.eg.db, keys, cols, keytype="ENTREZID"))## 'select()' returned 1:many mapping between keys and columns## ENTREZID UNIPROT PFAM
## 1 27255 A0A024R2C7 PF07679
## 2 27255 A0A024R2C7 PF00041
## 3 27255 B4DGV0 PF07679
## 4 27255 B4DGV0 PF00041
## 5 27255 Q9UQ52 PF07679
## 6 27255 Q9UQ52 PF00041The ‘keytype’ argument specifies that the mouse TxDb contains Ensembl instead of Entrez gene id’s.
keys <- unique(loc_mm10$GENEID)
head(select(org.Mm.eg.db, keys, cols, keytype="ENSEMBL"))## 'select()' returned 1:1 mapping between keys and columns## ENSEMBL UNIPROT PFAM
## 1 ENSMUSG00000028236 Q7TQA3 PF00106
## 2 ENSMUSG00000028608 Q8BHG2 PF05907Files 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 and genome build.
hub <- AnnotationHub()## snapshotDate(): 2017-10-27hub_hg19 <- subset(hub,
(hub$species == "Homo sapiens") & (hub$genome == "hg19"))
length(hub_hg19)## [1] 24001Iterate over the first 3 files and extract ranges that overlap ‘gr_hg19’.
## loading from cache 'C:/Rtools34/home/IP-AC1E008B$/AppData/.AnnotationHub/5012'## loading from cache 'C:/Rtools34/home/IP-AC1E008B$/AppData/.AnnotationHub/5013'## loading from cache 'C:/Rtools34/home/IP-AC1E008B$/AppData/.AnnotationHub/5014'ov_hg19 <- lapply(1:3, function(i) subsetByOverlaps(hub_hg19[[i]], gr_hg19))## loading from cache 'C:/Rtools34/home/IP-AC1E008B$/AppData/.AnnotationHub/5012'## loading from cache 'C:/Rtools34/home/IP-AC1E008B$/AppData/.AnnotationHub/5013'## loading from cache 'C:/Rtools34/home/IP-AC1E008B$/AppData/.AnnotationHub/5014'Inspect the results.
names(ov_hg19) <- names(hub_hg19)[1:3]
lapply(ov_hg19, head, n=3)## $AH5012
## UCSC track 'cytoBand'
## UCSCData object with 3 ranges and 1 metadata column:
## seqnames ranges strand | name
## <Rle> <IRanges> <Rle> | <character>
## [1] chr3 [ 1, 2800000] * | p26.3
## [2] chr3 [2800001, 4000000] * | p26.2
## [3] chr3 [4000001, 8700000] * | p26.1
## -------
## seqinfo: 93 sequences (1 circular) from hg19 genome
##
## $AH5013
## UCSC track 'stsMap'
## UCSCData object with 3 ranges and 2 metadata columns:
## seqnames ranges strand | name score
## <Rle> <IRanges> <Rle> | <character> <numeric>
## [1] chr3 [ 8787843, 8788458] * | BV209804 1000
## [2] chr3 [ 8788295, 8788424] * | RH92828 1000
## [3] chr3 [10183290, 10183558] * | GDB:361137 1000
## -------
## seqinfo: 93 sequences (1 circular) from hg19 genome
##
## $AH5014
## UCSC track 'fishClones'
## UCSCData object with 3 ranges and 2 metadata columns:
## seqnames ranges strand | name score
## <Rle> <IRanges> <Rle> | <character> <numeric>
## [1] chr3 [4354970, 4542349] * | RP11-91K16 1000
## [2] chr3 [4765290, 4929842] * | RP11-106B10 1000
## [3] chr3 [8711065, 8882271] * | RP11-128A5 1000
## -------
## seqinfo: 93 sequences (1 circular) from hg19 genomeAnnotating the mouse ranges in the same fashion is left as an exercise.
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.
head(predictCoding(vcf, txdb_hg19, Hsapiens), 3)## GRanges object with 3 ranges and 17 metadata columns:
## seqnames ranges strand | paramRangeID
## <Rle> <IRanges> <Rle> | <factor>
## rs140337334 chr3 [1427481, 1427481] + | <NA>
## rs140337334 chr3 [1427481, 1427481] + | <NA>
## rs140337334 chr3 [1427481, 1427481] + | <NA>
## REF ALT QUAL FILTER
## <DNAStringSet> <DNAStringSetList> <numeric> <character>
## rs140337334 C G,T <NA> .
## rs140337334 C G,T <NA> .
## rs140337334 C G,T <NA> .
## varAllele CDSLOC PROTEINLOC QUERYID
## <DNAStringSet> <IRanges> <IntegerList> <integer>
## rs140337334 G [2704, 2704] 902 1
## rs140337334 G [2488, 2488] 830 1
## rs140337334 G [2704, 2704] 902 1
## TXID CDSID GENEID CONSEQUENCE
## <character> <IntegerList> <character> <factor>
## rs140337334 13067 40577 27255 nonsynonymous
## rs140337334 13068 40577 27255 nonsynonymous
## rs140337334 13069 40577 27255 nonsynonymous
## REFCODON VARCODON REFAA VARAA
## <DNAStringSet> <DNAStringSet> <AAStringSet> <AAStringSet>
## rs140337334 CCT GCT P A
## rs140337334 CCT GCT P A
## rs140337334 CCT GCT P A
## -------
## seqinfo: 2 sequences from hg19 genome; no seqlengthssessionInfo()## R version 3.4.2 Patched (2017-10-12 r73550)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server 2008 R2 x64 (build 7601) Service Pack 1
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=C
## [2] LC_CTYPE=English_United States.1252
## [3] LC_MONETARY=English_United States.1252
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.1252
##
## attached base packages:
## [1] stats4 parallel stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] annotation_1.0.11
## [2] TxDb.Athaliana.BioMart.plantsmart22_3.0.1
## [3] biomaRt_2.34.1
## [4] BSgenome.Hsapiens.UCSC.hg19_1.4.0
## [5] BSgenome_1.46.0
## [6] rtracklayer_1.38.2
## [7] Homo.sapiens_1.3.1
## [8] GO.db_3.5.0
## [9] OrganismDbi_1.20.0
## [10] org.Mm.eg.db_3.5.0
## [11] org.Hs.eg.db_3.5.0
## [12] TxDb.Mmusculus.UCSC.mm10.ensGene_3.4.0
## [13] TxDb.Hsapiens.UCSC.hg38.knownGene_3.4.0
## [14] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
## [15] GenomicFeatures_1.30.0
## [16] AnnotationDbi_1.40.0
## [17] Organism.dplyr_1.6.0
## [18] AnnotationFilter_1.2.0
## [19] dplyr_0.7.4
## [20] AnnotationHub_2.10.1
## [21] VariantAnnotation_1.24.5
## [22] Rsamtools_1.30.0
## [23] Biostrings_2.46.0
## [24] XVector_0.18.0
## [25] SummarizedExperiment_1.8.1
## [26] DelayedArray_0.4.1
## [27] matrixStats_0.52.2
## [28] Biobase_2.38.0
## [29] GenomicRanges_1.30.0
## [30] GenomeInfoDb_1.14.0
## [31] IRanges_2.12.0
## [32] S4Vectors_0.16.0
## [33] BiocGenerics_0.24.0
##
## loaded via a namespace (and not attached):
## [1] httr_1.3.1 RMySQL_0.10.13
## [3] bit64_0.9-7 shiny_1.0.5
## [5] assertthat_0.2.0 interactiveDisplayBase_1.16.0
## [7] BiocFileCache_1.2.1 RBGL_1.54.0
## [9] blob_1.1.0 GenomeInfoDbData_1.0.0
## [11] yaml_2.1.16 progress_1.1.2
## [13] pillar_1.0.1 RSQLite_2.0
## [15] backports_1.1.2 lattice_0.20-35
## [17] glue_1.2.0 digest_0.6.13
## [19] htmltools_0.3.6 httpuv_1.3.5
## [21] Matrix_1.2-12 XML_3.98-1.9
## [23] pkgconfig_2.0.1 zlibbioc_1.24.0
## [25] xtable_1.8-2 BiocParallel_1.12.0
## [27] tibble_1.4.1 lazyeval_0.2.1
## [29] magrittr_1.5 mime_0.5
## [31] memoise_1.1.0 evaluate_0.10.1
## [33] graph_1.56.0 BiocInstaller_1.28.0
## [35] tools_3.4.2 prettyunits_1.0.2
## [37] stringr_1.2.0 bindrcpp_0.2
## [39] compiler_3.4.2 rlang_0.1.6
## [41] grid_3.4.2 RCurl_1.95-4.10
## [43] rappdirs_0.3.1 bitops_1.0-6
## [45] rmarkdown_1.8 curl_3.1
## [47] DBI_0.7 R6_2.2.2
## [49] GenomicAlignments_1.14.1 knitr_1.18
## [51] bit_1.1-12 bindr_0.1
## [53] rprojroot_1.3-2 stringi_1.1.6
## [55] Rcpp_0.12.14 dbplyr_1.2.0Exercise 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. * Use scanVcfHeader() to inspect the header information in the ‘chr22.vcf.gz’ file in VariantAnnotation package. * Select a few ‘info’ or ‘geno’ variables and create a ScanVcfParam object. * Use the ScanVcfParam object as the ‘param’ argument to readVcf() to read in a subset of data. Note that the header() accessor operates on VCF objects in the R workspace. Try header(vcf) on the dbSNP file from AnnotationHub.
Exercise 2: Annotate the mouse ranges in ‘gr_mm10’ with AnnotationHub files. * Create a new ‘hub’ and filter on organism. * Isolate the files for the appropriate genome build and perform overlaps.
Exercise 3: Annotate a gene range from Saccharomyces Scerevisiae. * Load TxDb.Scerevisiae.UCSC.sacCer3.sgdGene and extract the gene ranges. (Hint: use transcriptsBy() and range()). * Isolate the range for gene “YBL086C”. * Create a new ‘hub’ from AnnotationHub and filter by organism. (You should see >= 39 files.) * Select the files for ‘sacCer3’ and perform overlaps.
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