get_usage4plot: prepare coverage data and fitting data for plot

In jianhong/InPAS: Identify Novel Alternative PolyAdenylation Sites (PAS) from RNA-seq data

prepare coverage data and fitting data for plot

Description

prepare coverage data and fitting data for plot

Usage

get_usage4plot(gr, proximalSites, sqlite_db, hugeData)

Arguments

gr	An object of GenomicRanges::GRanges
proximalSites	An integer(n) vector, specifying the coordinates of proximal CP sites. Each of the proximal sites must match one entry in the GRanges object, gr.
sqlite_db	A path to the SQLite database for InPAS, i.e. the output of setup_sqlitedb().
hugeData	A logical(1), indicating whether it is huge data

Value

An object of GenomicRanges::GRanges with metadata:

dat	A data.frame, first column is the position, the other columns are Coverage and value
offset	offset from the start of 3' UTR

Author(s)

Jianhong Ou, Haibo Liu

Examples

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), drop = TRUE)

bedgraphs <- system.file("extdata", c(
  "Baf3.extract.bedgraph",
  "UM15.extract.bedgraph"
),
package = "InPAS"
)
tags <- c("Baf3", "UM15")
metadata <- data.frame(
  tag = tags,
  condition = c("baf", "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
)
addLockName(filename = tempfile())
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,
    chr2exclude = "chrM"
  )
}
data4CPsSearch <- setup_CPsSearch(sqlite_db,
  genome,
  chr.utr3 = utr3[["chr6"]],
  seqname = "chr6",
  background = "10K",
  TxDb = TxDb,
  hugeData = TRUE,
  outdir = outdir
)

gr <- GRanges("chr6", IRanges(128846245, 128850081), strand = "-")
names(gr) <- "chr6:128846245-128850081"
data4plot <- get_usage4plot(gr,
  proximalSites = 128849148,
  sqlite_db,
  hugeData = TRUE
)
plot_utr3Usage(
  usage_data = data4plot,
  vline_color = "purple",
  vline_type = "dashed"
)

jianhong/InPAS documentation built on Oct. 27, 2023, 2:13 p.m.