buildFragtypes: Build fragment types from exons

In mikelove/alpine: alpine

Description

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.

Usage

buildFragtypes(exons, genome, readlength, minsize, maxsize, gc = TRUE,
  gc.str = TRUE, vlmm = TRUE)

Arguments

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 minsize and maxsize should contain the at least the central 95 percent of the fragment length distribution across samples
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

Value

a DataFrame with bias features (columns) for all potential fragments (rows)

Examples

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)

mikelove/alpine documentation built on May 22, 2019, 10:52 p.m.