Reads input CAGE datasets into CAGEr object, constructs CAGE
transcriptions start sites (CTSSs) and counts number of CAGE tags supporting every
CTSS in each input experiment. See
inputFilesType for details on
the supported input formats. Preprocessing and quality filtering of input CAGE
tags, as well as correction of CAGE-specific 'G' nucleotide addition bias can be
also performed before constructing TSSs.
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getCTSS(object, sequencingQualityThreshold = 10, mappingQualityThreshold = 20, removeFirstG = TRUE, correctSystematicG = TRUE, useMulticore = FALSE, nrCores = NULL) ## S4 method for signature 'CAGEset' getCTSS(object, sequencingQualityThreshold = 10, mappingQualityThreshold = 20, removeFirstG = TRUE, correctSystematicG = TRUE, useMulticore = FALSE, nrCores = NULL) ## S4 method for signature 'CAGEexp' getCTSS(object, sequencingQualityThreshold = 10, mappingQualityThreshold = 20, removeFirstG = TRUE, correctSystematicG = TRUE, useMulticore = FALSE, nrCores = NULL)
Only CAGE tags with average sequencing quality
Logical, should the first nucleotide of the CAGE tag be removed
in case it is a G and it does not map to the referent genome (i.e. it is a
mismatch). Used only if
Logical, should the systematic correction of the first G
nucleotide be performed for the positions where there is a G in the CAGE tag and G
in the genome. This step is performed in addition to removing the first G of the
CAGE tags when it is a mismatch, i.e. this option can only be used when
Logical, should multicore be used.
Number of cores to use when
In the CAGE experimental protocol an additional G nucleotide is often attached
to the 5' end of the tag by the template-free activity of the reverse transcriptase used
to prepare cDNA (Harbers and Carninci, Nature Methods 2005). In cases where there is a
G at the 5' end of the CAGE tag that does not map to the corresponding genome sequence,
it can confidently be considered spurious and should be removed from the tag to avoid
misannotating actual TSS. Thus, setting
removeFirstG = TRUE is highly recommended.
However, when there is a G both at the beginning of the CAGE tag and in the genome, it is
not clear whether the original CAGE tag really starts at this position or the G nucleotide
was added later in the experimental protocol. To systematically correct CAGE tags mapping
at such positions, a general frequency of adding a G to CAGE tags can be calculated from
mismatch cases and applied to estimate the number of CAGE tags that have G added and
should actually start at the next nucleotide/position. The option
is an implementation of the correction algorithm described in Carninci et al.,
Nature Genetics 2006, Supplementary Information section 3-e.
CAGEset objects, the slots
tagCountMatrix will be occupied by the information on CTSSs created from input CAGE
CAGEexp objects, the
tagCountMatrix experiment will be
occupied by a
SummarizedExperiment containing the expression data as a
Rle integers, and the CTSS coordinates as a
GRanges object. In both cases
the expression data can be retreived with
CTSStagCount functions. In addition,
the library sizes are calculated and stored in the object.
Harbers and Carninci (2005) Tag-based approaches for transcriptome research and genome annotation, Nature Methods 2(7):495-502.
Carninci et al. (2006) Genome-wide analysis of mammalian promoter architecture and evolution, Nature Genetics 38(7):626-635.
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library(BSgenome.Drerio.UCSC.danRer7) pathsToInputFiles <- system.file("extdata", c("Zf.unfertilized.egg.chr17.ctss", "Zf.30p.dome.chr17.ctss", "Zf.prim6.rep1.chr17.ctss"), package="CAGEr") labels <- paste("sample", seq(1,3,1), sep = "") myCAGEset <- new("CAGEset", genomeName = "BSgenome.Drerio.UCSC.danRer7", inputFiles = pathsToInputFiles, inputFilesType = "ctss", sampleLabels = labels) getCTSS(myCAGEset)
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