Supported by Dr. Osamu Ogasawara and  providing  . |
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Last data update: 2014.03.03 |
Annotate clusters with respect to transcript featuresDescriptionCarries out strand-specific annotation of clusters with respect to distinct transcript features, particularly introns, coding sequences, 3'-UTRs, 5'-UTRs. Mapping to multiple features and to those outside the above mentioned ones are reported. Unmapped clusters are then futher further analyzed and annotated with respect to features localizing on the anti-sense strand. Results can be plotted as dotchart and annotations are returned as clusters metadata. UsageannotateClusters(clusters, txDB = NULL, genome = "hg19", tablename = "ensGene", plot = TRUE, verbose = TRUE) Arguments
ValueSame as the input GRanges object, with an additional metadata column containing the following character encoding of the genomic feature each cluster maps to:
If Author(s)Federico Comoglio ReferencesM. Carlson and H. Pages and P. Aboyoun and S. Falcon and M. Morgan and D. Sarkar and M. Lawrence, GenomicFeatures: Tools for making and manipulating transcript centric annotations, R package version 1.12.4 Comoglio F, Sievers C and Paro R (2015) Sensitive and highly resolved identification of RNA-protein interaction sites in PAR-CLIP data, BMC Bioinformatics 16, 32. See Also
Examples
require(BSgenome.Hsapiens.UCSC.hg19)
data( model, package = "wavClusteR" )
filename <- system.file( "extdata", "example.bam", package = "wavClusteR" )
example <- readSortedBam( filename = filename )
countTable <- getAllSub( example, minCov = 10, cores = 1 )
highConfSub <- getHighConfSub( countTable, supportStart = 0.2, supportEnd = 0.7, substitution = "TC" )
coverage <- coverage( example )
clusters <- getClusters( highConfSub = highConfSub,
coverage = coverage,
sortedBam = example,
method = 'mrn',
cores = 1,
threshold = 2 )
fclusters <- filterClusters( clusters = clusters,
highConfSub = highConfSub,
coverage = coverage,
model = model,
genome = Hsapiens,
refBase = 'T',
minWidth = 12 )
## Not run: fclusters <- annotateClusters( clusters = fclusters )
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
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Platform: x86_64-pc-linux-gnu (64-bit)
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> library(wavClusteR)
Loading required package: GenomicRanges
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: 'BiocGenerics'
The following objects are masked from 'package:parallel':
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from 'package:stats':
IQR, mad, xtabs
The following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, append,
as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
rbind, rownames, sapply, setdiff, sort, table, tapply, union,
unique, unsplit
Loading required package: S4Vectors
Loading required package: stats4
Attaching package: 'S4Vectors'
The following objects are masked from 'package:base':
colMeans, colSums, expand.grid, rowMeans, rowSums
Loading required package: IRanges
Loading required package: GenomeInfoDb
Loading required package: Rsamtools
Loading required package: Biostrings
Loading required package: XVector
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/wavClusteR/annotateClusters.Rd_%03d_medium.png", width=480, height=480)
> ### Name: annotateClusters
> ### Title: Annotate clusters with respect to transcript features
> ### Aliases: annotateClusters
> ### Keywords: graphics postprocessing
>
> ### ** Examples
>
>
> require(BSgenome.Hsapiens.UCSC.hg19)
Loading required package: BSgenome.Hsapiens.UCSC.hg19
Loading required package: BSgenome
Loading required package: rtracklayer
>
> data( model, package = "wavClusteR" )
>
> filename <- system.file( "extdata", "example.bam", package = "wavClusteR" )
> example <- readSortedBam( filename = filename )
> countTable <- getAllSub( example, minCov = 10, cores = 1 )
Loading required package: doMC
Loading required package: foreach
Loading required package: iterators
Considering substitutions, n = 497, processing in 1 chunks
chunk #: 1
considering the + strand
Computing local coverage at substitutions...
considering the - strand
Computing local coverage at substitutions...
> highConfSub <- getHighConfSub( countTable, supportStart = 0.2, supportEnd = 0.7, substitution = "TC" )
> coverage <- coverage( example )
> clusters <- getClusters( highConfSub = highConfSub,
+ coverage = coverage,
+ sortedBam = example,
+ method = 'mrn',
+ cores = 1,
+ threshold = 2 )
Computing start/end read positions
Number of chromosomes exhibiting high confidence transitions: 1
...Processing = chrX
>
> fclusters <- filterClusters( clusters = clusters,
+ highConfSub = highConfSub,
+ coverage = coverage,
+ model = model,
+ genome = Hsapiens,
+ refBase = 'T',
+ minWidth = 12 )
Computing log odds...
Refining cluster sizes...
Combining clusters...
Quantifying transitions within clusters...
Computing statistics...
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Consolidating results...
> ## Not run: fclusters <- annotateClusters( clusters = fclusters )
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> dev.off()
null device
1
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