setup_CPsSearch {InPAS}

R Documentation

prepare data for predicting cleavage and polyadenylation (CP) sites

Description

prepare data for predicting cleavage and polyadenylation (CP) sites

Usage

setup_CPsSearch(
  sqlite_db,
  genome,
  utr3,
  background = c("same_as_long_coverage_threshold", "1K", "5K", "10K", "50K"),
  TxDb = NA,
  removeScaffolds = FALSE,
  hugeData = TRUE,
  outdir,
  BPPARAM = NULL,
  silence = FALSE
)

Arguments

sqlite_db	A path to the SQLite database for InPAS, i.e. the output of setup_sqlitedb().
genome	An object of BSgenome::BSgenome
utr3	An object of GenomicRanges::GRangesList, output of extract_UTR3Anno()
background	A character(1) vector, the range for calculating cutoff threshold of local background. It can be "same_as_long_coverage_threshold", "1K", "5K","10K", or "50K".
TxDb	an object of GenomicFeatures::TxDb
removeScaffolds	A logical(1) vector, whether the scaffolds should be removed from the genome If you use a TxDb containing alternative scaffolds, you'd better to remove the scaffolds.
hugeData	A logical(1) vector, indicating whether it is huge data
outdir	A character(1) vector, a path with write permission for storing the coverage data. If it doesn't exist, it will be created.
BPPARAM	an optional BiocParallel::BiocParallelParam instance determining the parallel back-end to be used during evaluation, or a list of BiocParallelParam instances, to be applied in sequence for nested calls to bplapply. It can be set to NULL or bpparam()
silence	report progress or not. By default it doesn't report progress.

Value

A list as described below:

utr3TotalCov

chromosome-wise 3' UTR coverage in summarized View format

chr1

A filename for chr1 3' UTR coverage in summarized View format

chr2

A filename for chr2 3' UTR coverage in summarized View format

chrN

A filename for chrN 3' UTR coverage in summarized View format

background

The type of methods for bckground coverage calculation

z2s

Z-score cutoff thresholds for each 3' UTRs

depth.weight

A named vector containing depth weight

Author(s)

Jianhong Ou, Haibo Liu

@examples if (interactive()) library(BSgenome.Mmusculus.UCSC.mm10) library("TxDb.Mmusculus.UCSC.mm10.knownGene")

genome <- BSgenome.Mmusculus.UCSC.mm10
TxDb <- TxDb.Mmusculus.UCSC.mm10.knownGene

## load UTR3 annotation and convert it into a GRangesList
data(utr3.mm10)
utr3 <- split(utr3.mm10, seqnames(utr3.mm10))

bedgraphs <- system.file("extdata",c("Baf3.extract.bedgraph",
                                     "UM15.extract.bedgraph"),
                        package = "InPAS")
tags <- c("Baf3", "UM15")
metadata <- data.frame(tag = tags,
                       condition = c("Baf3", "UM15"),
                       bedgraph_file = bedgraphs)
outdir = tempdir()
write.table(metadata, file =file.path(outdir, "metadata.txt"),
            sep = "\t", quote = FALSE, row.names = FALSE)

sqlite_db <- setup_sqlitedb(metadata = file.path(outdir,
                                                 "metadata.txt"),
                            outdir)
coverage <- list()
for (i in seq_along(bedgraphs)){
coverage[[tags[i]]] <- get_ssRleCov(bedgraph = bedgraphs[i],
                         tag = tags[i],
                         genome = genome,
                         sqlite_db = sqlite_db,
                         outdir = outdir,
                         removeScaffolds = TRUE)
}
coverage_files <- assemble_allCov(sqlite_db,
                                 outdir,
                                 genome,
                                 removeScaffolds = TRUE)
data4CPsitesSearch <- setup_CPsSearch(sqlite_db,
                                      genome,
                                      utr3,
                                     background = "10K",
                                     TxDb = TxDb,
                                     removeScaffolds = TRUE,
                                     hugeData = TRUE,
                                     outdir = outdir)

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