search_CPs: Estimate the CP sites for UTRs on a given chromosome

In haibol2016/InPAS: Identify Novel Alternative PolyAdenylation Sites (PAS) from RNA-seq data

Estimate the CP sites for UTRs on a given chromosome

Description

Estimate the CP sites for UTRs on a given chromosome

Usage

search_CPs(
  seqname,
  sqlite_db,
  genome = getInPASGenome(),
  MINSIZE = 10,
  window_size = 200,
  search_point_START = 100,
  search_point_END = NA,
  cutEnd = NA,
  filter.last = TRUE,
  adjust_distal_polyA_end = FALSE,
  long_coverage_threshold = 2,
  PolyA_PWM = NA,
  classifier = NA,
  classifier_cutoff = 0.8,
  shift_range = 100,
  step = 2,
  outdir = getInPASOutputDirectory(),
  silence = FALSE,
  cluster_type = c("interactive", "multicore", "torque", "slurm", "sge", "lsf",
    "openlava", "socket"),
  template_file = NULL,
  mc.cores = 1,
  future.chunk.size = 50,
  resources = list(walltime = 3600 * 8, ncpus = 4, mpp = 1024 * 4, queue = "long",
    memory = 4 * 4 * 1024),
  DIST2ANNOAPAP = 500,
  DIST2END = 1000,
  output.all = FALSE
)

Arguments

seqname	A character(1) vector, specifying a chromososome/scaffold name
sqlite_db	A path to the SQLite database for InPAS, i.e. the output of setup_sqlitedb().
genome	A BSgenome::BSgenome object
MINSIZE	A integer(1) vector, specifying the minimal length in bp of a short/proximal 3' UTR. Default, 10
window_size	An integer(1) vector, the window size for novel distal or proximal CP site searching. default: 200.
search_point_START	A integer(1) vector, starting point relative to the 5' extremity of 3' UTRs for searching for proximal CP sites
search_point_END	A integer(1) vector, ending point relative to the 3' extremity of 3' UTRs for searching for proximal CP sites
cutEnd	An integer(1) vector a numeric(1) vector. What percentage or how many nucleotides should be removed from 5' extremities before searching for proximal CP sites? It can be a decimal between 0, and 1, or an integer greater than 1. 0.1 means 10 percent, 25 means cut first 25 bases
filter.last	A logical(1), whether to filter out the last valley, which is likely the 3' end of the longer 3' UTR if no novel distal CP site is detected and the 3' end excluded by setting cutEnd/search_point_END is small.
adjust_distal_polyA_end	A logical(1) vector. If true, distal CP sites are subject to adjustment by the Naive Bayes classifier from the cleanUpdTSeq::cleanUpdTSeq-package
long_coverage_threshold	An integer(1) vector, specifying the cutoff threshold of coverage for the terminal of long form 3' UTRs. If the coverage of first 100 nucleotides is lower than coverage_threshold, that transcript will be not considered for further analysis. Default, 2.
PolyA_PWM	An R object for a position weight matrix (PWM) for a hexamer polyadenylation signal (PAS), such as AAUAAA.
classifier	An R object for Naive Bayes classifier model, like the one in the cleanUpdTSeq package.
classifier_cutoff	A numeric(1) vector. A cutoff of probability that a site is classified as true CP sites. The value should be between 0.5 and 1. Default, 0.8.
shift_range	An integer(1) vector, specifying a shift range for adjusting the proximal and distal CP sites. Default, 50. It determines the range flanking the candidate CP sites to search the most likely real CP sites.
step	An integer (1) vector, specifying the step size used for adjusting the proximal or distal CP sites using the Naive Bayes classifier from the cleanUpdTSeq package. Default 1. It can be in the range of 1 to 10.
outdir	A character(1) vector, a path with write permission for storing the CP sites. If it doesn't exist, it will be created.
silence	A logical(1), indicating whether progress is reported or not. By default, FALSE
cluster_type	A character (1) vector, indicating the type of cluster job management systems. Options are "interactive","multicore", "torque", "slurm", "sge", "lsf", "openlava", and "socket". see batchtools vignette
template_file	A charcter(1) vector, indicating the template file for job submitting scripts when cluster_type is set to "torque", "slurm", "sge", "lsf", or "openlava".
mc.cores	An integer(1), number of cores for making multicore clusters or socket clusters using batchtools, and for parallel::mclapply()
future.chunk.size	The average number of elements per future ("chunk"). If Inf, then all elements are processed in a single future. If NULL, then argument future.scheduling = 1 is used by default. Users can set future.chunk.size = total number of elements/number of cores set for the backend. See the future.apply package for details. Default, 50. This parameter is used to split the candidate 3' UTRs for alternative SP sites search.
resources	A named list specifying the computing resources when cluster_type is set to "torque", "slurm", "sge", "lsf", or "openlava". See batchtools vignette
DIST2ANNOAPAP	An integer, specifying a cutoff for annotate MSE valleys with known proximal APAs in a given downstream distance. Default is 500.
DIST2END	An integer, specifying a cutoff of the distance between last valley and the end of the 3' UTR (where MSE of the last base is calculated). If the last valley is closer to the end than the specified distance, it will not be considered because it is very likely due to RNA coverage decay at the end of mRNA. Default is 1200. User can consider a value between 1000 and 1500, depending on the library preparation procedures: RNA fragmentation and size selection.
output.all	A logical(1), indicating whether to output entries with only single CP site for a 3' UTR. Default, FALSE.

Value

An object of GenomicRanges::GRanges containing distal and proximal CP site information for each 3' UTR if detected.

Author(s)

Jianhong Ou, Haibo Liu

See Also

search_proximalCPs(), adjust_proximalCPs(), adjust_proximalCPsByPWM(), adjust_proximalCPsByNBC(), get_PAscore(), get_PAscore2()

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), 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("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)
  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,
    minZ = 2,
    cutStart = 10,
    MINSIZE = 10,
    coverage_threshold = 5
  )
  ## polyA_PWM
  load(system.file("extdata", "polyA.rda", package = "InPAS"))

  ## load the Naive Bayes classifier model from the cleanUpdTSeq package
  library(cleanUpdTSeq)
  data(classifier)
  ## the following setting just for demo.
  if (.Platform$OS.type == "window") {
    plan(multisession)
  } else {
    plan(multicore)
  }
  CPs <- search_CPs(
    seqname = "chr6",
    sqlite_db = sqlite_db,
    genome = genome,
    MINSIZE = 10,
    window_size = 100,
    search_point_START = 50,
    search_point_END = NA,
    cutEnd = 0,
    filter.last = TRUE,
    adjust_distal_polyA_end = TRUE,
    long_coverage_threshold = 2,
    PolyA_PWM = pwm,
    classifier = classifier,
    classifier_cutoff = 0.8,
    shift_range = 100,
    step = 5,
    outdir = outdir
  )
}

haibol2016/InPAS documentation built on March 30, 2022, 10:30 a.m.