Description Usage Arguments Value Examples
This function constructs a DataFrame of fragment features used for bias modeling, with one row for every potential fragment type that could arise from a transcript. The output of this function is used by fitBiasModels, and this function is used inside estimateAbundance in order to model the bias affecting different fragments across isoforms of a gene.
1 2 | buildFragtypes(exons, genome, readlength, minsize, maxsize, gc = TRUE,
gc.str = TRUE, vlmm = TRUE)
|
exons |
a GRanges object with the exons for a single transcript |
genome |
a BSgenome object |
readlength |
the length of the reads. This doesn't necessarily have to be exact (+/- 1 bp is acceptable) |
minsize |
the minimum fragment length to model. The interval between
|
maxsize |
the maximum fragment length to model |
gc |
logical, whether to calculate the fragment GC content |
gc.str |
logical, whether to look for presence of stretches of very high GC within fragments |
vlmm |
logical, whether to calculate the Cufflinks Variable Length Markov Model (VLMM) for read start bias |
a DataFrame with bias features (columns) for all potential fragments (rows)
1 2 3 4 5 6 7 8 9 | library(GenomicRanges)
library(BSgenome.Hsapiens.NCBI.GRCh38)
data(preprocessedData)
readlength <- 100
minsize <- 125 # see vignette how to choose
maxsize <- 175 # see vignette how to choose
fragtypes <- buildFragtypes(ebt.fit[["ENST00000624447"]],
Hsapiens, readlength,
minsize, maxsize)
|
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