Package 'InPAS' reference manual

Title:	Identify Novel Alternative PolyAdenylation Sites (PAS) from RNA-seq data
Description:	Alternative polyadenylation (APA) is one of the important post- transcriptional regulation mechanisms which occurs in most human genes. InPAS facilitates the discovery of novel APA sites and the differential usage of APA sites from RNA-Seq data. It leverages cleanUpdTSeq to fine tune identified APA sites by removing false sites.
Authors:	Jianhong Ou [aut, cre], Haibo Liu [aut], Lihua Julie Zhu [aut], Sungmi M. Park [aut], Michael R. Green [aut]
Maintainer:	Jianhong Ou <[email protected]>
License:	GPL (>= 2)
Version:	2.13.0
Built:	2024-09-23 05:21:54 UTC
Source:	https://github.com/bioc/InPAS

Add a globally-applied requirement for filtering out scaffolds from all analysis

Description

This function will set the default requirement of filtering out scaffolds from all analysis.

Usage

addChr2Exclude(chr2exclude = c("chrM", "MT", "Pltd", "chrPltd"))
addChr2Exclude(chr2exclude = c("chrM", "MT", "Pltd", "chrPltd"))

Arguments

chr2exclude

A character vector, NA or NULL, specifying chromosomes or scaffolds to be excluded for InPAS analysis. chrM and alternative scaffolds representing different haplotypes should be excluded.

Add a globally defined EnsDb to some InPAS functions.

Description

Add a globally defined EnsDb to some InPAS functions.

Usage

addInPASEnsDb(EnsDb = NULL)
addInPASEnsDb(EnsDb = NULL)

Arguments

EnsDb

An object of ensembldb::EnsDb

Add a globally defined genome to all InPAS functions.

Description

This function will set the genome across all InPAS functions.

Usage

addInPASGenome(genome = NULL)
addInPASGenome(genome = NULL)

Arguments

genome

A BSgenome object indicating the default genome to be used for all InPAS functions. This value is stored as a global environment variable. This can be overwritten on a per-function basis using the given function's genome parameter.

Add a globally defined output directory to some InPAS functions.

Description

Add a globally defined output directory to some InPAS functions.

Usage

addInPASOutputDirectory(outdir = NULL)
addInPASOutputDirectory(outdir = NULL)

Arguments

outdir

A character(1) vector, a path with write permission for storing InPAS analysis results. If it doesn't exist, it will be created.

Add a globally defined TxDb for InPAS functions.

Description

Add a globally defined TxDb for InPAS functions.

Usage

addInPASTxDb(TxDb = NULL)
addInPASTxDb(TxDb = NULL)

Arguments

TxDb

An object of GenomicFeatures::TxDb

Examples

library("TxDb.Hsapiens.UCSC.hg19.knownGene")
addInPASTxDb(TxDb = TxDb.Hsapiens.UCSC.hg19.knownGene)
library("TxDb.Hsapiens.UCSC.hg19.knownGene")
addInPASTxDb(TxDb = TxDb.Hsapiens.UCSC.hg19.knownGene)

Add a filename for locking a SQLite database

Description

Add a filename for locking a SQLite database

Usage

addLockName(filename = NULL)
addLockName(filename = NULL)

Arguments

filename

A character(1) vector, specifyong a path to a file for locking.

Assemble coverage files for a given chromosome for all samples

Description

Process individual sample-chromosome-specific coverage files in an experiment into a file containing a list of chromosome-specific Rle coverage of all samples

Usage

assemble_allCov(
  sqlite_db,
  seqname,
  outdir = getInPASOutputDirectory(),
  genome = getInPASGenome()
)
assemble_allCov(
  sqlite_db,
  seqname,
  outdir = getInPASOutputDirectory(),
  genome = getInPASGenome()
)

Arguments

`sqlite_db`	A path to the SQLite database for InPAS, i.e. the output of setup_sqlitedb()
`seqname`	A character(1) vector, the name of a chromosome/scaffold
`outdir`	A character(1) vector, a path with write permission for storing InPAS analysis results. If it doesn't exist, it will be created.
`genome`	An object of BSgenome::BSgenome

Value

A list of paths to per-chromosome coverage files of all samples.

seqname, chromosome/scaffold name
- tag1, name tag for sample1
- tag2, name tag for sample2
- tagN, name tag for sampleN

Author(s)

Haibo Liu

Examples

if (interactive()) {
  library(BSgenome.Mmusculus.UCSC.mm10)
  genome <- BSgenome.Mmusculus.UCSC.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()
  addLockName(filename = tempfile())
  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"
    )
  }
  chr_coverage <- assemble_allCov(sqlite_db,
    seqname = "chr6",
    outdir = outdir,
    genome = genome
  )
}
if (interactive()) {
  library(BSgenome.Mmusculus.UCSC.mm10)
  genome <- BSgenome.Mmusculus.UCSC.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()
  addLockName(filename = tempfile())
  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"
    )
  }
  chr_coverage <- assemble_allCov(sqlite_db,
    seqname = "chr6",
    outdir = outdir,
    genome = genome
  )
}

extract 3' UTR information from a GenomicFeatures::TxDb object

Description

extract 3' UTR information from a GenomicFeatures::TxDb object. The 3'UTR is defined as the last 3'UTR fragment for each transcript and it will be cut if there is any overlaps with other exons.

Usage

extract_UTR3Anno(
  sqlite_db,
  TxDb = getInPASTxDb(),
  edb = getInPASEnsDb(),
  genome = getInPASGenome(),
  outdir = getInPASOutputDirectory(),
  chr2exclude = getChr2Exclude(),
  MAX_EXONS_GAP = 10000L
)
extract_UTR3Anno(
  sqlite_db,
  TxDb = getInPASTxDb(),
  edb = getInPASEnsDb(),
  genome = getInPASGenome(),
  outdir = getInPASOutputDirectory(),
  chr2exclude = getChr2Exclude(),
  MAX_EXONS_GAP = 10000L
)

Arguments

`sqlite_db`	A path to the SQLite database for InPAS, i.e. the output of `setup_sqlitedb()`.
`TxDb`	An object of GenomicFeatures::TxDb
`edb`	An object of ensembldb::EnsDb
`genome`	An object of BSgenome::BSgenome
`outdir`	A character(1) vector, a path with write permission for storing InPAS analysis results. If it doesn't exist, it will be created.
`chr2exclude`	A character vector, NA or NULL, specifying chromosomes or scaffolds to be excluded for InPAS analysis. `chrM` and alternative scaffolds representing different haplotypes should be excluded.
`MAX_EXONS_GAP`	An integer(1) vector, maximal gap sizes between the last known CP sites to a nearest downstream exon. Default is 10 kb for mammalian genomes. For other species, user need to adjust this parameter.

Details

A good practice is to perform read alignment using a reference genome from Ensembl/GenCode including only the primary assembly and build a TxDb and EnsDb using the GTF/GFF files downloaded from the same source as the reference genome, such as BioMart/Ensembl/GenCode. For instruction, see Vignette of the GenomicFeatures. The UCSC reference genomes and their annotation packages can be very cumbersome.

Value

An object of GenomicRanges::GRangesList, containing GRanges for extracted 3' UTRs, and the corresponding last CDSs and next.exon.gap for each chromosome/scaffold. Chromosome

Author(s)

Jianhong Ou, Haibo Liu

Examples

library("EnsDb.Hsapiens.v86")
library("BSgenome.Hsapiens.UCSC.hg19")
library("GenomicFeatures")
## set a sqlite database
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
)

samplefile <- system.file("extdata",
  "hg19_knownGene_sample.sqlite",
  package = "GenomicFeatures"
)
TxDb <- loadDb(samplefile)
edb <- EnsDb.Hsapiens.v86
genome <- BSgenome.Hsapiens.UCSC.hg19
addInPASOutputDirectory(outdir)
seqnames <- seqnames(BSgenome.Hsapiens.UCSC.hg19)
chr2exclude <- c(
  "chrM", "chrMT",
  seqnames[grepl("_(hap\\d+|fix|alt)$",
    seqnames,
    perl = TRUE
  )]
)
utr3 <- extract_UTR3Anno(sqlite_db, TxDb, edb,
  genome = genome,
  chr2exclude = chr2exclude,
  outdir = tempdir(),
  MAX_EXONS_GAP = 10000L
)
library("EnsDb.Hsapiens.v86")
library("BSgenome.Hsapiens.UCSC.hg19")
library("GenomicFeatures")
## set a sqlite database
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
)

samplefile <- system.file("extdata",
  "hg19_knownGene_sample.sqlite",
  package = "GenomicFeatures"
)
TxDb <- loadDb(samplefile)
edb <- EnsDb.Hsapiens.v86
genome <- BSgenome.Hsapiens.UCSC.hg19
addInPASOutputDirectory(outdir)
seqnames <- seqnames(BSgenome.Hsapiens.UCSC.hg19)
chr2exclude <- c(
  "chrM", "chrMT",
  seqnames[grepl("_(hap\\d+|fix|alt)$",
    seqnames,
    perl = TRUE
  )]
)
utr3 <- extract_UTR3Anno(sqlite_db, TxDb, edb,
  genome = genome,
  chr2exclude = chr2exclude,
  outdir = tempdir(),
  MAX_EXONS_GAP = 10000L
)

filter 3' UTR usage test results

Description

filter results of test_dPDUI()

Usage

filter_testOut(
  res,
  gp1,
  gp2,
  outdir = getInPASOutputDirectory(),
  background_coverage_threshold = 2,
  P.Value_cutoff = 0.05,
  adj.P.Val_cutoff = 0.05,
  dPDUI_cutoff = 0.2,
  PDUI_logFC_cutoff = log2(1.5)
)
filter_testOut(
  res,
  gp1,
  gp2,
  outdir = getInPASOutputDirectory(),
  background_coverage_threshold = 2,
  P.Value_cutoff = 0.05,
  adj.P.Val_cutoff = 0.05,
  dPDUI_cutoff = 0.2,
  PDUI_logFC_cutoff = log2(1.5)
)

Arguments

`res`	a UTR3eSet object, output of `test_dPDUI()`
`gp1`	tag names involved in group 1. gp1 and gp2 are used for filtering purpose if both are specified; otherwise only other specified thresholds are used for filtering.
`gp2`	tag names involved in group 2
`outdir`	A character(1) vector, a path with write permission for storing InPAS analysis results. If it doesn't exist, it will be created.
`background_coverage_threshold`	background coverage cut off value. for each group, more than half of the long form should greater than background_coverage_threshold. for both group, at least in one group, more than half of the short form should greater than background_coverage_threshold.
`P.Value_cutoff`	cutoff of P value
`adj.P.Val_cutoff`	cutoff of adjust P value
`dPDUI_cutoff`	cutoff of dPDUI
`PDUI_logFC_cutoff`	cutoff of PDUI log2 transformed fold change

Value

A data frame converted from an object of GenomicRanges::GRanges.

Author(s)

Jianhong Ou, Haibo Liu

Examples

library(limma)
path <- system.file("extdata", package = "InPAS")
load(file.path(path, "eset.MAQC.rda"))
tags <- colnames(eset@PDUI)
g <- factor(gsub("\\..*$", "", tags))
design <- model.matrix(~ -1 + g)
colnames(design) <- c("Brain", "UHR")
contrast.matrix <- makeContrasts(
  contrasts = "Brain-UHR",
  levels = design
)
res <- test_dPDUI(
  eset = eset,
  method = "limma",
  normalize = "none",
  design = design,
  contrast.matrix = contrast.matrix
)
filter_testOut(res,
  gp1 = c("Brain.auto", "Brain.phiX"),
  gp2 = c("UHR.auto", "UHR.phiX"),
  background_coverage_threshold = 2,
  P.Value_cutoff = 0.05,
  adj.P.Val_cutoff = 0.05,
  dPDUI_cutoff = 0.3,
  PDUI_logFC_cutoff = .59
)
library(limma)
path <- system.file("extdata", package = "InPAS")
load(file.path(path, "eset.MAQC.rda"))
tags <- colnames(eset@PDUI)
g <- factor(gsub("\\..*$", "", tags))
design <- model.matrix(~ -1 + g)
colnames(design) <- c("Brain", "UHR")
contrast.matrix <- makeContrasts(
  contrasts = "Brain-UHR",
  levels = design
)
res <- test_dPDUI(
  eset = eset,
  method = "limma",
  normalize = "none",
  design = design,
  contrast.matrix = contrast.matrix
)
filter_testOut(res,
  gp1 = c("Brain.auto", "Brain.phiX"),
  gp2 = c("UHR.auto", "UHR.phiX"),
  background_coverage_threshold = 2,
  P.Value_cutoff = 0.05,
  adj.P.Val_cutoff = 0.05,
  dPDUI_cutoff = 0.3,
  PDUI_logFC_cutoff = .59
)

Visualization of MSE profiles, 3' UTR coverage and minimal MSE distribution

Description

Visualization of MSE profiles, 3' UTR coverage and minimal MSE distribution

Usage

find_minMSEDistr(
  CPs,
  outdir = NULL,
  MSE.plot = "MSE.pdf",
  coverage.plot = "coverage.pdf",
  min.MSE.to.end.distr.plot = "min.MSE.to.end.distr.pdf"
)
find_minMSEDistr(
  CPs,
  outdir = NULL,
  MSE.plot = "MSE.pdf",
  coverage.plot = "coverage.pdf",
  min.MSE.to.end.distr.plot = "min.MSE.to.end.distr.pdf"
)

Arguments

`CPs`	A list, output from `search_proximalCPs()` or `adjust_distalCPs()` or `adjust_proximalCPs()`
`outdir`	A character(1) vector, specifying the output directory
`MSE.plot`	A character(1) vector, specifying a PDF file name for outputting plots of MSE profiles. No directory path is allowed.
`coverage.plot`	A character(1) vector, specifying a PDF file name for outputting per-sample coverage profiles. No directory path is allowed.
`min.MSE.to.end.distr.plot`	A character(1) vector, specifying a PDF file name for outputting histograms showing minimal MSE distribution relative to longer 3' UTR end. No directory path is allowed.

Identify chromosomes/scaffolds for CP site discovery

Description

Identify chromosomes/scaffolds which have both coverage and annotated 3' utr3 for CP site discovery

Usage

get_chromosomes(utr3, sqlite_db)
get_chromosomes(utr3, sqlite_db)

Arguments

`utr3`	An object of GenomicRanges::GRangesList. An output of `extract_UTR3Anno()`.
`sqlite_db`	A path to the SQLite database for InPAS, i.e. the output of `setup_sqlitedb()`.

Value

A vector of characters, containing names of chromosomes/scaffolds for CP site discovery

Examples

library(BSgenome.Mmusculus.UCSC.mm10)
genome <- BSgenome.Mmusculus.UCSC.mm10
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"
    )
}
get_chromosomes(utr3, sqlite_db)
library(BSgenome.Mmusculus.UCSC.mm10)
genome <- BSgenome.Mmusculus.UCSC.mm10
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"
    )
}
get_chromosomes(utr3, sqlite_db)

Extract the last unspliced region of each transcript

Description

Extract the last unspliced region of each transcript from a TxDb. These regions could be the last 3'UTR exon for transcripts whose 3' UTRs are composed of multiple exons or last CDS regions and 3'UTRs for transcripts whose 3'UTRs and last CDS regions are on the same single exon.

Usage

get_lastCDSUTR3(
  TxDb = getInPASTxDb(),
  genome = getInPASGenome(),
  chr2exclude = getChr2Exclude(),
  outdir = getInPASOutputDirectory(),
  MAX_EXONS_GAP = 10000
)
get_lastCDSUTR3(
  TxDb = getInPASTxDb(),
  genome = getInPASGenome(),
  chr2exclude = getChr2Exclude(),
  outdir = getInPASOutputDirectory(),
  MAX_EXONS_GAP = 10000
)

Arguments

`TxDb`	An object of GenomicFeatures::TxDb
`genome`	An object of BSgenome::BSgenome
`chr2exclude`	A character vector, NA or NULL, specifying chromosomes or scaffolds to be excluded for InPAS analysis. `chrM` and alternative scaffolds representing different haplotypes should be excluded.
`outdir`	A character(1) vector, a path with write permission for storing InPAS analysis results. If it doesn't exist, it will be created.
`MAX_EXONS_GAP`	An integer(1) vector, maximal gap sizes between the last known CP sites to a nearest downstream exon. Default is 10 kb for mammalian genomes. For other species, user need to adjust this parameter.

Value

A BED file with 6 columns: chr, chrStart, chrEnd, name, score, and strand.

Get coverage for 3' UTR and last CDS regions on a single chromosome

Description

Get coverage for 3' UTR and last CDS regions on a single chromosome

Usage

get_regionCov(
  chr.utr3,
  sqlite_db,
  outdir = getInPASOutputDirectory(),
  phmm = FALSE,
  min.length.diff = 200
)
get_regionCov(
  chr.utr3,
  sqlite_db,
  outdir = getInPASOutputDirectory(),
  phmm = FALSE,
  min.length.diff = 200
)

Arguments

`chr.utr3`	An object of GenomicRanges::GRanges, one element of an output of `extract_UTR3Anno()`
`sqlite_db`	A path to the SQLite database for InPAS, i.e. the output of `setup_sqlitedb()`.
`outdir`	A character(1) vector, a path with write permission for storing InPAS analysis results. If it doesn't exist, it will be created.
`phmm`	A logical(1) vector, indicating whether data should be prepared for singleSample analysis? By default, FALSE
`min.length.diff`	An integer(1) vector, specifying minimal length difference between proximal and distal APA sites which should be met to be considered for differential APA analysis. Default is 200 bp.

Value

coverage view in GRanges

Author(s)

Jianhong Ou, Haibo Liu

Get Rle coverage from a bedgraph file for a sample

Description

Get RLe coverage from a bedgraph file for a sample

Usage

get_ssRleCov(
  bedgraph,
  tag,
  genome = getInPASGenome(),
  sqlite_db,
  future.chunk.size = NULL,
  outdir = getInPASOutputDirectory(),
  chr2exclude = getChr2Exclude()
)
get_ssRleCov(
  bedgraph,
  tag,
  genome = getInPASGenome(),
  sqlite_db,
  future.chunk.size = NULL,
  outdir = getInPASOutputDirectory(),
  chr2exclude = getChr2Exclude()
)

Arguments

`bedgraph`	A path to a bedGraph file
`tag`	A character(1) vector, a name tag used to label the bedgraph file. It must match the tag specified in the metadata file used to setup the SQLite database
`genome`	an object BSgenome::BSgenome. To make things easy, we suggest users creating a BSgenome::BSgenome instance from the reference genome used for read alignment. For details, see the documentation of `BSgenome::forgeBSgenomeDataPkg()`.
`sqlite_db`	A path to the SQLite database for InPAS, i.e. the output of `setup_sqlitedb()`.
`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. You may adjust this number based based on the available computing resource: CPUs and RAM. This parameter affects the time for converting coverage from bedgraph to Rle.
`outdir`	A character(1) vector, a path with write permission for storing InPAS analysis results. If it doesn't exist, it will be created.
`chr2exclude`	A character vector, NA or NULL, specifying chromosomes or scaffolds to be excluded for InPAS analysis. `chrM` and alternative scaffolds representing different haplotypes should be excluded.

Value

A data frame, as described below.

tag: the sample tag
chr: chromosome name
coverage_file: path to Rle coverage files for each chromosome per sample tag

Author(s)

Jianhong Ou, Haibo Liu

Examples

if (interactive()) {
  library(BSgenome.Mmusculus.UCSC.mm10)
  genome <- BSgenome.Mmusculus.UCSC.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
  )
  addLockName()
  coverage_info <- get_ssRleCov(
    bedgraph = bedgraphs[1],
    tag = tags[1],
    genome = genome,
    sqlite_db = sqlite_db,
    outdir = outdir,
    chr2exclude = "chrM"
  )
  # check read coverage depth
  db_connect <- dbConnect(drv = RSQLite::SQLite(), dbname = sqlite_db)
  dbReadTable(db_connect, "metadata")
  dbDisconnect(db_connect)
}
if (interactive()) {
  library(BSgenome.Mmusculus.UCSC.mm10)
  genome <- BSgenome.Mmusculus.UCSC.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
  )
  addLockName()
  coverage_info <- get_ssRleCov(
    bedgraph = bedgraphs[1],
    tag = tags[1],
    genome = genome,
    sqlite_db = sqlite_db,
    outdir = outdir,
    chr2exclude = "chrM"
  )
  # check read coverage depth
  db_connect <- dbConnect(drv = RSQLite::SQLite(), dbname = sqlite_db)
  dbReadTable(db_connect, "metadata")
  dbDisconnect(db_connect)
}

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)
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"
)
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"
)

prepare 3' UTR coverage data for usage test

Description

generate a UTR3eSet object with PDUI information for statistic tests

Usage

get_UTR3eSet(
  sqlite_db,
  normalize = c("none", "quantiles", "quantiles.robust", "mean", "median"),
  ...,
  singleSample = FALSE
)
get_UTR3eSet(
  sqlite_db,
  normalize = c("none", "quantiles", "quantiles.robust", "mean", "median"),
  ...,
  singleSample = FALSE
)

Arguments

`sqlite_db`	A path to the SQLite database for InPAS, i.e. the output of `setup_sqlitedb()`.
`normalize`	A character(1) vector, spcifying the normalization method. It can be "none", "quantiles", "quantiles.robust", "mean", or "median"
`...`	parameter can be passed into `preprocessCore::normalize.quantiles.robust()`
`singleSample`	A logical(1) vector, indicating whether data is prepared for analysis in a singleSample mode? Default, FALSE

Value

An object of UTR3eSet which contains following elements: usage: an GenomicRanges::GRanges object with CP sites info. PDUI: a matrix of PDUI PDUI.log2: log2 transformed PDUI matrix short: a matrix of usage of short form long: a matrix of usage of long form if singleSample is TRUE, one more element, signals, will be included.

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), 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)

  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,
    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
  )
  utr3_cds_cov <- get_regionCov(
    chr.utr3 = utr3[["chr6"]],
    sqlite_db,
    outdir,
    phmm = FALSE
  )
  eSet <- get_UTR3eSet(sqlite_db,
    normalize = "none",
    singleSample = FALSE
  )
  test_out <- test_dPDUI(
    eset = eSet,
    method = "fisher.exact",
    normalize = "none",
    sqlite_db = sqlite_db
  )
}
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)

  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,
    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
  )
  utr3_cds_cov <- get_regionCov(
    chr.utr3 = utr3[["chr6"]],
    sqlite_db,
    outdir,
    phmm = FALSE
  )
  eSet <- get_UTR3eSet(sqlite_db,
    normalize = "none",
    singleSample = FALSE
  )
  test_out <- test_dPDUI(
    eset = eSet,
    method = "fisher.exact",
    normalize = "none",
    sqlite_db = sqlite_db
  )
}

Get a globally-applied requirement for filtering scaffolds.

Description

This function will get the default requirement of filtering scaffolds.

Usage

getChr2Exclude()
getChr2Exclude()

Get the globally defined EnsDb.

Description

Get the globally defined EnsDb.

Usage

getInPASEnsDb()
getInPASEnsDb()

Value

An object of ensembldb::EnsDb

Get the globally defined genome

Description

This function will retrieve the genome that is currently in use by InPAS.

Usage

getInPASGenome()
getInPASGenome()

Get the path to a output directory for InPAS analysis

Description

Get the path to a output directory for InPAS analysis

Usage

getInPASOutputDirectory()
getInPASOutputDirectory()

Value

a normalized path to a output directory for InPAS analysis

Get the path to an SQLite database

Description

Get the path to an SQLite database

Usage

getInPASSQLiteDb()
getInPASSQLiteDb()

Value

A path to an SQLite database

Get the globally defined TxDb.

Description

Get the globally defined TxDb.

Usage

getInPASTxDb()
getInPASTxDb()

Value

An object of GenomicFeatures::TxDb

Examples

library("TxDb.Hsapiens.UCSC.hg19.knownGene")
addInPASTxDb(TxDb = TxDb.Hsapiens.UCSC.hg19.knownGene)
getInPASTxDb()
library("TxDb.Hsapiens.UCSC.hg19.knownGene")
addInPASTxDb(TxDb = TxDb.Hsapiens.UCSC.hg19.knownGene)
getInPASTxDb()

Get the path to a file for locking the SQLite database

Description

Get the path to a file for locking the SQLite database

Usage

getLockName()
getLockName()

Value

A path to a file for locking

A package for identifying novel Alternative PolyAdenylation Sites (PAS) based on RNA-seq data

Description

The InPAS package provides three categories of important functions: parse_TxDb, extract_UTR3Anno, get_ssRleCov, assemble_allCov, get_UTR3eSet, test_dPDUI, run_singleSampleAnalysis, run_singleGroupAnalysis, run_limmaAnalysis, filter_testOut, get_usage4plot, setup_GSEA, run_coverageQC

functions for retrieving 3' UTR annotation

parse_TxDb, extract_UTR3Anno, get_lastCDSUTR3

functions for processing read coverage data

assemble_allCov, get_ssRleCov, run_coverageQC, setup_parCPsSearch

functions for alternative polyadenylation site analysis

test_dPDUI, run_singleSampleAnalysis, run_singleGroupAnalysis, run_limmaAnalysis, filter_testOut, get_usage4plot

Extract gene models from a TxDb object

Description

Extract gene models from a TxDb object and annotate last 3' UTR exons and the last CDSs

Usage

parse_TxDb(
  sqlite_db = NULL,
  TxDb = getInPASTxDb(),
  edb = getInPASEnsDb(),
  genome = getInPASGenome(),
  chr2exclude = getChr2Exclude(),
  outdir = getInPASOutputDirectory()
)
parse_TxDb(
  sqlite_db = NULL,
  TxDb = getInPASTxDb(),
  edb = getInPASEnsDb(),
  genome = getInPASGenome(),
  chr2exclude = getChr2Exclude(),
  outdir = getInPASOutputDirectory()
)

Arguments

`sqlite_db`	A path to the SQLite database for InPAS, i.e. the output of `setup_sqlitedb()`. It can be `NULL`.
`TxDb`	An object of GenomicFeatures::TxDb
`edb`	An object of ensembldb::EnsDb
`genome`	An object of BSgenome::BSgenome
`chr2exclude`	A character vector, NA or NULL, specifying chromosomes or scaffolds to be excluded for InPAS analysis. `chrM` and alternative scaffolds representing different haplotypes should be excluded.
`outdir`	A character(1) vector, a path with write permission for storing InPAS analysis results. If it doesn't exist, it will be created.

Details

A good practice is to perform read alignment using a reference genome from Ensembl/GenCode including only the primary assembly and build a TxDb using the GTF/GFF files downloaded from the same source as the reference genome, such as BioMart/Ensembl/GenCode. For instruction, see Vignette of the GenomicFeatures. The UCSC reference genomes and their annotation can be very cumbersome.

Value

A GenomicRanges::GRanges object for gene models

Author(s)

Haibo Liu

Examples

library("EnsDb.Hsapiens.v86")
library("BSgenome.Hsapiens.UCSC.hg19")
library("GenomicFeatures")

## set a sqlite database
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
)

samplefile <- system.file("extdata",
  "hg19_knownGene_sample.sqlite",
  package = "GenomicFeatures"
)
TxDb <- loadDb(samplefile)
edb <- EnsDb.Hsapiens.v86
genome <- BSgenome.Hsapiens.UCSC.hg19
seqnames <- seqnames(BSgenome.Hsapiens.UCSC.hg19)
chr2exclude <- c(
  "chrM", "chrMT",
  seqnames[grepl("_(hap\\d+|fix|alt)$",
    seqnames,
    perl = TRUE
  )]
)
parsed_Txdb <- parse_TxDb(sqlite_db, TxDb, edb, genome,
  chr2exclude = chr2exclude
)
library("EnsDb.Hsapiens.v86")
library("BSgenome.Hsapiens.UCSC.hg19")
library("GenomicFeatures")

## set a sqlite database
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
)

samplefile <- system.file("extdata",
  "hg19_knownGene_sample.sqlite",
  package = "GenomicFeatures"
)
TxDb <- loadDb(samplefile)
edb <- EnsDb.Hsapiens.v86
genome <- BSgenome.Hsapiens.UCSC.hg19
seqnames <- seqnames(BSgenome.Hsapiens.UCSC.hg19)
chr2exclude <- c(
  "chrM", "chrMT",
  seqnames[grepl("_(hap\\d+|fix|alt)$",
    seqnames,
    perl = TRUE
  )]
)
parsed_Txdb <- parse_TxDb(sqlite_db, TxDb, edb, genome,
  chr2exclude = chr2exclude
)

Visualize the dPDUI events using ggplot2

Description

Visualize the dPDUI events by plotting the MSE, and total coverage per group along 3' UTR regions with dPDUI using ggplot2::geom_line ().

Usage

plot_utr3Usage(usage_data, vline_color = "purple", vline_type = "dashed")
plot_utr3Usage(usage_data, vline_color = "purple", vline_type = "dashed")

Arguments

`usage_data`	An object of GenomicRanges::GRanges, an output from `get_usage4plot()`.
`vline_color`	color for vertical line showing position of predicated proximal CP site. Default, purple.
`vline_type`	line type for vertical line showing position of predicated proximal CP site. Default, dashed. See ggplot2 linetype.

Value

A ggplot object for refined plotting

Author(s)

Haibo Liu

Quality control on read coverage over gene bodies and 3UTRs

Description

Calculate coverage over gene bodies and 3UTRs. This function is used for quality control of the coverage.The coverage rate can show the complexity of RNA-seq library.

Usage

run_coverageQC(
  sqlite_db,
  TxDb = getInPASTxDb(),
  edb = getInPASEnsDb(),
  genome = getInPASGenome(),
  cutoff_readsNum = 1,
  cutoff_expdGene_cvgRate = 0.1,
  cutoff_expdGene_sampleRate = 0.5,
  chr2exclude = getChr2Exclude(),
  which = NULL,
  future.chunk.size = 1,
  ...
)
run_coverageQC(
  sqlite_db,
  TxDb = getInPASTxDb(),
  edb = getInPASEnsDb(),
  genome = getInPASGenome(),
  cutoff_readsNum = 1,
  cutoff_expdGene_cvgRate = 0.1,
  cutoff_expdGene_sampleRate = 0.5,
  chr2exclude = getChr2Exclude(),
  which = NULL,
  future.chunk.size = 1,
  ...
)

Arguments

`sqlite_db`	A path to the SQLite database for InPAS, i.e. the output of `setup_sqlitedb()`.
`TxDb`	An object of GenomicFeatures::TxDb
`edb`	An object of ensembldb::EnsDb
`genome`	An object of BSgenome::BSgenome
`cutoff_readsNum`	cutoff reads number. If the coverage in the location is greater than cutoff_readsNum,the location will be treated as covered by signal
`cutoff_expdGene_cvgRate`	cutoff_expdGene_cvgRate and cutoff_expdGene_sampleRate are the parameters used to calculate which gene is expressed in all input dataset. cutoff_expdGene_cvgRateset the cutoff value for the coverage rate of each gene; cutoff_expdGene_sampleRateset the cutoff value for ratio of numbers of expressed and all samples for each gene. for example, by default, cutoff_expdGene_cvgRate=0.1 and cutoff_expdGene_sampleRate=0.5,suppose there are 4 samples, for one gene, if the coverage rates by base are:0.05, 0.12, 0.2, 0.17, this gene will be count as expressed gene because mean(c(0.05,0.12, 0.2, 0.17) > cutoff_expdGene_cvgRate) > cutoff_expdGene_sampleRate if the coverage rates by base are: 0.05, 0.12, 0.07, 0.17, this gene will be count as un-expressed gene because mean(c(0.05, 0.12, 0.07, 0.17) > cutoff_expdGene_cvgRate)<= cutoff_expdGene_sampleRate
`cutoff_expdGene_sampleRate`	See cutoff_expdGene_cvgRate
`chr2exclude`	A character vector, NA or NULL, specifying chromosomes or scaffolds to be excluded for InPAS analysis. `chrM` and alternative scaffolds representing different haplotypes should be excluded.
`which`	an object of GenomicRanges::GRanges or NULL. If it is not NULL, only the exons overlapping the given ranges are used. For fast data quality control, set which to Granges for one or a few large chromosomes.
`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.
`...`	Not used yet

Value

A data frame as described below.

gene.coverage.rate: overage per base for all genes
expressed.gene.coverage.rate: coverage per base for expressed genes
UTR3.coverage.rate: coverage per base for all 3' UTRs
UTR3.expressed.gene.subset.coverage.rate: coverage per base for 3' UTRs of expressed genes
rownames: the names of coverage

Author(s)

Jianhong Ou, Haibo Liu

Examples

if (interactive()) {
  library("BSgenome.Mmusculus.UCSC.mm10")
  library("TxDb.Mmusculus.UCSC.mm10.knownGene")
  library("EnsDb.Mmusculus.v79")

  genome <- BSgenome.Mmusculus.UCSC.mm10
  TxDb <- TxDb.Mmusculus.UCSC.mm10.knownGene
  edb <- EnsDb.Mmusculus.v79

  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
  )
  tx <- parse_TxDb(
    sqlite_db = sqlite_db,
    TxDb = TxDb,
    edb = edb,
    genome = genome,
    outdir = outdir,
    chr2exclude = "chrM"
  )
  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"
    )
  }
  chr_coverage <- assemble_allCov(sqlite_db,
    seqname = "chr6",
    outdir,
    genome
  )
  run_coverageQC(sqlite_db, TxDb, edb, genome,
    chr2exclude = "chrM",
    which = GRanges("chr6",
      ranges = IRanges(98013000, 140678000)
    )
  )
}
if (interactive()) {
  library("BSgenome.Mmusculus.UCSC.mm10")
  library("TxDb.Mmusculus.UCSC.mm10.knownGene")
  library("EnsDb.Mmusculus.v79")

  genome <- BSgenome.Mmusculus.UCSC.mm10
  TxDb <- TxDb.Mmusculus.UCSC.mm10.knownGene
  edb <- EnsDb.Mmusculus.v79

  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
  )
  tx <- parse_TxDb(
    sqlite_db = sqlite_db,
    TxDb = TxDb,
    edb = edb,
    genome = genome,
    outdir = outdir,
    chr2exclude = "chrM"
  )
  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"
    )
  }
  chr_coverage <- assemble_allCov(sqlite_db,
    seqname = "chr6",
    outdir,
    genome
  )
  run_coverageQC(sqlite_db, TxDb, edb, genome,
    chr2exclude = "chrM",
    which = GRanges("chr6",
      ranges = IRanges(98013000, 140678000)
    )
  )
}

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
)
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

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
  )
}
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
  )
}

Set up global variables for an InPAS analysis

Description

Set up global variables for an InPAS analysis

Usage

set_globals(
  genome = NULL,
  TxDb = NULL,
  EnsDb = NULL,
  outdir = NULL,
  chr2exclude = c("chrM", "MT", "Pltd", "chrPltd"),
  lockfile = tempfile(tmpdir = getInPASOutputDirectory())
)
set_globals(
  genome = NULL,
  TxDb = NULL,
  EnsDb = NULL,
  outdir = NULL,
  chr2exclude = c("chrM", "MT", "Pltd", "chrPltd"),
  lockfile = tempfile(tmpdir = getInPASOutputDirectory())
)

Arguments

`genome`	An object BSgenome::BSgenome. To make things easy, we suggest users creating a BSgenome::BSgenome instance from the reference genome used for read alignment. For details, see the documentation of `BSgenome::forgeBSgenomeDataPkg()`.
`TxDb`	An object of GenomicFeatures::TxDb
`EnsDb`	An object of ensembldb::EnsDb
`outdir`	A character(1) vector, a path with write permission for storing InPAS analysis results. If it doesn't exist, it will be created.
`chr2exclude`	A character vector, NA or NULL, specifying chromosomes or scaffolds to be excluded for InPAS analysis. `chrM` and alternative scaffolds representing different haplotypes should be excluded.
`lockfile`	A character(1) vector, specifying a file name used for parallel writing to a SQLite database

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 = getInPASGenome(),
  chr.utr3,
  seqname,
  background = c("same_as_long_coverage_threshold", "1K", "5K", "10K", "50K"),
  TxDb = getInPASTxDb(),
  hugeData = TRUE,
  outdir = getInPASOutputDirectory(),
  silence = FALSE,
  minZ = 2,
  cutStart = 10,
  MINSIZE = 10,
  coverage_threshold = 5
)
setup_CPsSearch(
  sqlite_db,
  genome = getInPASGenome(),
  chr.utr3,
  seqname,
  background = c("same_as_long_coverage_threshold", "1K", "5K", "10K", "50K"),
  TxDb = getInPASTxDb(),
  hugeData = TRUE,
  outdir = getInPASOutputDirectory(),
  silence = FALSE,
  minZ = 2,
  cutStart = 10,
  MINSIZE = 10,
  coverage_threshold = 5
)

Arguments

`sqlite_db`	A path to the SQLite database for InPAS, i.e. the output of `setup_sqlitedb()`.
`genome`	An object of BSgenome::BSgenome
`chr.utr3`	An object of GenomicRanges::GRanges, an element of the output of `extract_UTR3Anno()`
`seqname`	A character(1), the name of a chromosome/scaffold
`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
`hugeData`	A logical(1) vector, indicating whether it is huge data
`outdir`	A character(1) vector, a path with write permission for storing InPAS analysis results. If it doesn't exist, it will be created.
`silence`	report progress or not. By default it doesn't report progress.
`minZ`	A numeric(1), a Z score cutoff value
`cutStart`	An integer(1) vector a numeric(1) vector. What percentage or how many nucleotides should be removed from 5' extremities before searching for 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
`MINSIZE`	A integer(1) vector, specifying the minimal length in bp of a short/proximal 3' UTR. Default, 10
`coverage_threshold`	An integer(1) vector, specifying the cutoff threshold of coverage for first 100 nucleotides. If the coverage of first 100 nucleotides is lower than coverage_threshold, that transcript will be not considered for further analysis. Default, 5.

Value

A file storing a list as described below:

background: The type of methods for background coverage calculation
z2s: Z-score cutoff thresholds for each 3' UTRs
depth.weight: A named vector containing depth weight
chr.cov.merge: A matrix storing condition/sample-specific coverage for 3' UTR and next.exon.gap (if exist)
conn_next_utr3: A logical vector, indicating whether a 3'UTR has a convergent 3' UTR of its downstream transcript
chr.utr3: A GRangesList, storing extracted 3' UTR annotation of transcript on a given chr

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), 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"
    )
  }
  data4CPsitesSearch <- setup_CPsSearch(sqlite_db,
    genome,
    chr.utr3 = utr3[["chr6"]],
    seqname = "chr6",
    background = "10K",
    TxDb = TxDb,
    hugeData = TRUE,
    outdir = outdir
  )
}
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"
    )
  }
  data4CPsitesSearch <- setup_CPsSearch(sqlite_db,
    genome,
    chr.utr3 = utr3[["chr6"]],
    seqname = "chr6",
    background = "10K",
    TxDb = TxDb,
    hugeData = TRUE,
    outdir = outdir
  )
}

prepare files for GSEA analysis

Description

output the log2 transformed delta PDUI txt file, chip file, rank file and phynotype label file for GSEA analysis

Usage

setup_GSEA(
  eset,
  groupList,
  outdir = getInPASOutputDirectory(),
  preranked = TRUE,
  rankBy = c("logFC", "P.value"),
  rnkFilename = "InPAS.rnk",
  chipFilename = "InPAS.chip",
  dataFilename = "dPDUI.txt",
  PhenFilename = "group.cls"
)
setup_GSEA(
  eset,
  groupList,
  outdir = getInPASOutputDirectory(),
  preranked = TRUE,
  rankBy = c("logFC", "P.value"),
  rnkFilename = "InPAS.rnk",
  chipFilename = "InPAS.chip",
  dataFilename = "dPDUI.txt",
  PhenFilename = "group.cls"
)

Arguments

`eset`	A UTR3eSet object, output of `test_dPDUI()`
`groupList`	A list of grouped sample tag names, with the group names as the list's name, such as list(groupA = c("sample_1", "sample_2", "sample_3"), groupB = c("sample_4", "sample_5", "sample_6"))
`outdir`	A character(1) vector, a path with write permission for storing InPAS analysis results. If it doesn't exist, it will be created.
`preranked`	A logical(1) vector, out preranked or not
`rankBy`	A character(1) vector, indicating how the gene list is ranked. It can be "logFC" or "P.value".
`rnkFilename`	A character(1) vector, specifying a filename for the preranked file
`chipFilename`	A character(1) vector, specifying a filename for the chip file
`dataFilename`	A character(1) vector, specifying a filename for the dataset file
`PhenFilename`	A character(1) vector, specifying a filename for the file containing samples' phenotype labels

Author(s)

Jianhong Ou, Haibo Liu

Examples

library(limma)
path <- system.file("extdata", package = "InPAS")
load(file.path(path, "eset.MAQC.rda"))
tags <- colnames(eset@PDUI)
g <- factor(gsub("\\..*$", "", tags))
design <- model.matrix(~ -1 + g)
colnames(design) <- c("Brain", "UHR")
contrast.matrix <- makeContrasts(
  contrasts = "Brain-UHR",
  levels = design
)
res <- test_dPDUI(
  eset = eset,
  method = "limma",
  normalize = "none",
  design = design,
  contrast.matrix = contrast.matrix
)
gp1 <- c("Brain.auto", "Brain.phiX")
gp2 <- c("UHR.auto", "UHR.phiX")
groupList <- list(Brain = gp1, UHR = gp2)
setup_GSEA(res,
  groupList = groupList,
  outdir = tempdir(),
  preranked = TRUE,
  rankBy = "P.value"
)
library(limma)
path <- system.file("extdata", package = "InPAS")
load(file.path(path, "eset.MAQC.rda"))
tags <- colnames(eset@PDUI)
g <- factor(gsub("\\..*$", "", tags))
design <- model.matrix(~ -1 + g)
colnames(design) <- c("Brain", "UHR")
contrast.matrix <- makeContrasts(
  contrasts = "Brain-UHR",
  levels = design
)
res <- test_dPDUI(
  eset = eset,
  method = "limma",
  normalize = "none",
  design = design,
  contrast.matrix = contrast.matrix
)
gp1 <- c("Brain.auto", "Brain.phiX")
gp2 <- c("UHR.auto", "UHR.phiX")
groupList <- list(Brain = gp1, UHR = gp2)
setup_GSEA(res,
  groupList = groupList,
  outdir = tempdir(),
  preranked = TRUE,
  rankBy = "P.value"
)

Prepare data for predicting cleavage and polyadenylation (CP) sites using parallel computing

Description

Prepare data for predicting cleavage and polyadenylation (CP) sites using parallel computing

Usage

setup_parCPsSearch(
  sqlite_db,
  genome = getInPASGenome(),
  utr3,
  seqnames,
  background = c("same_as_long_coverage_threshold", "1K", "5K", "10K", "50K"),
  TxDb = getInPASTxDb(),
  future.chunk.size = 1,
  chr2exclude = getChr2Exclude(),
  hugeData = TRUE,
  outdir = getInPASOutputDirectory(),
  silence = FALSE,
  minZ = 2,
  cutStart = 10,
  MINSIZE = 10,
  coverage_threshold = 5
)
setup_parCPsSearch(
  sqlite_db,
  genome = getInPASGenome(),
  utr3,
  seqnames,
  background = c("same_as_long_coverage_threshold", "1K", "5K", "10K", "50K"),
  TxDb = getInPASTxDb(),
  future.chunk.size = 1,
  chr2exclude = getChr2Exclude(),
  hugeData = TRUE,
  outdir = getInPASOutputDirectory(),
  silence = FALSE,
  minZ = 2,
  cutStart = 10,
  MINSIZE = 10,
  coverage_threshold = 5
)

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, the output of `extract_UTR3Anno()`
`seqnames`	A character(1), the names of all chromosomes/scaffolds with both coverage and 3' UTR annotation. Users can get this by calling the get_chromosomes().
`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
`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.
`chr2exclude`	A character vector, NA or NULL, specifying chromosomes or scaffolds to be excluded for InPAS analysis. `chrM` and alternative scaffolds representing different haplotypes should be excluded.
`hugeData`	A logical(1) vector, indicating whether it is huge data
`outdir`	A character(1) vector, a path with write permission for storing InPAS analysis results. If it doesn't exist, it will be created.
`silence`	report progress or not. By default it doesn't report progress.
`minZ`	A numeric(1), a Z score cutoff value
`cutStart`	An integer(1) vector a numeric(1) vector. What percentage or how many nucleotides should be removed from 5' extremities before searching for 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
`MINSIZE`	A integer(1) vector, specifying the minimal length in bp of a short/proximal 3' UTR. Default, 10
`coverage_threshold`	An integer(1) vector, specifying the cutoff threshold of coverage for first 100 nucleotides. If the coverage of first 100 nucleotides is lower than coverage_threshold, that transcript will be not considered for further analysis. Default, 5.

Value

A list of list as described below:

background: The type of methods for background coverage calculation
z2s: Z-score cutoff thresholds for each 3' UTRs
depth.weight: A named vector containing depth weight
chr.cov.merge: A list of matrice storing condition/sample- specific coverage for 3' UTR and next.exon.gap (if exist)
conn_next_utr3: A logical vector, indicating whether a 3'UTR has a convergent 3' UTR of its downstream transcript
chr.utr3: A GRangesList, storing extracted 3' UTR annotation of transcript on a given chr

Author(s)

Jianhong Ou, Haibo Liu

Create an SQLite database for storing metadata and paths to coverage files

Description

Create an SQLite database with five tables, "metadata","sample_coverage", "chromosome_coverage", "CPsites", and "utr3_coverage", for storing metadata (sample tag, condition, paths to bedgraph files, and sample total read coverage), sample-then-chromosome-oriented coverage files (sample tag, chromosome, paths to bedgraph files for each chromosome), and paths to chromosome-then-sample-oriented coverage files (chromosome, paths to bedgraph files for each chromosome), CP sites on each chromosome (chromosome, paths to cpsite files), read coverage for 3' UTR and last CDS regions on each chromosome (chromosome, paths to utr3 coverage file), respectively

Usage

setup_sqlitedb(metadata, outdir = getInPASOutputDirectory())
setup_sqlitedb(metadata, outdir = getInPASOutputDirectory())

Arguments

`metadata`	A path to a tab-delimited file, with columns "tag", "condition", and "bedgraph_file", storing a unique name tag for each sample, a condition name for each sample, such as "treatment" and "control", and a path to the bedgraph file for each sample
`outdir`	A character(1) vector, a path with write permission for storing InPAS analysis results. If it doesn't exist, it will be created.

Value

A character(1) vector, the path to the SQLite database

Author(s)

Haibo Liu

Examples

if (interactive()) {
  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
  )
}
if (interactive()) {
  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
  )
}

do test for dPDUI

Description

do test for dPDUI

Usage

test_dPDUI(
  eset,
  sqlite_db,
  outdir = getInPASOutputDirectory(),
  method = c("limma", "fisher.exact", "singleSample", "singleGroup"),
  normalize = c("none", "quantiles", "quantiles.robust", "mean", "median"),
  design,
  contrast.matrix,
  coef = 1,
  robust = FALSE,
  ...
)
test_dPDUI(
  eset,
  sqlite_db,
  outdir = getInPASOutputDirectory(),
  method = c("limma", "fisher.exact", "singleSample", "singleGroup"),
  normalize = c("none", "quantiles", "quantiles.robust", "mean", "median"),
  design,
  contrast.matrix,
  coef = 1,
  robust = FALSE,
  ...
)

Arguments

`eset`	An object of UTR3eSet. It is an output of `get_UTR3eSet()`
`sqlite_db`	A path to the SQLite database for InPAS, i.e. the output of setup_sqlitedb().
`outdir`	A character(1) vector, a path with write permission for storing InPAS analysis results. If it doesn't exist, it will be created.
`method`	A character(1), indicating the method for testing dPDUI. It can be "limma", "fisher.exact", "singleSample", or "singleGroup"
`normalize`	A character(1), indicating the normalization method. It can be "none", "quantiles", "quantiles.robust", "mean", or "median"
`design`	a design matrix of the experiment, with rows corresponding to samples and columns to coefficients to be estimated. Defaults to the unit vector meaning that the samples are treated as replicates. see `stats::model.matrix()`. Required for limma-based analysis.
`contrast.matrix`	a numeric matrix with rows corresponding to coefficients in fit and columns containing contrasts. May be a vector if there is only one contrast. see `limma::makeContrasts()`. Required for limma-based analysis.
`coef`	column number or column name specifying which coefficient or contrast of the linear model is of interest. see more `limma::topTable()`. default value: 1
`robust`	A logical(1) vector, indicating whether the estimation of the empirical Bayes prior parameters should be robustified against outlier sample variances.
`...`	other arguments are passed to lmFit

Details

if method is "limma", design matrix and contrast is required. if method is "fisher.exact", gp1 and gp2 is required.

Value

An object of UTR3eSet, with the last element testRes containing the test results in a matrix.

Author(s)

Jianhong Ou, Haibo Liu

Examples

library(limma)
path <- system.file("extdata", package = "InPAS")
load(file.path(path, "eset.MAQC.rda"))
tags <- colnames(eset@PDUI)
g <- factor(gsub("\\..*$", "", tags))
design <- model.matrix(~ -1 + g)
colnames(design) <- c("Brain", "UHR")
contrast.matrix <- makeContrasts(
  contrasts = "Brain-UHR",
  levels = design
)
res <- test_dPDUI(
  eset = eset,
  sqlite_db,
  method = "limma",
  normalize = "none",
  design = design,
  contrast.matrix = contrast.matrix
)
library(limma)
path <- system.file("extdata", package = "InPAS")
load(file.path(path, "eset.MAQC.rda"))
tags <- colnames(eset@PDUI)
g <- factor(gsub("\\..*$", "", tags))
design <- model.matrix(~ -1 + g)
colnames(design) <- c("Brain", "UHR")
contrast.matrix <- makeContrasts(
  contrasts = "Brain-UHR",
  levels = design
)
res <- test_dPDUI(
  eset = eset,
  sqlite_db,
  method = "limma",
  normalize = "none",
  design = design,
  contrast.matrix = contrast.matrix
)

Annotation of 3' UTRs for mouse (mm10)

Description

A dataset containing the annotation of the 3' UTRs of the mouse

Usage

utr3.mm10
utr3.mm10

Format

An object of GenomicRanges::GRanges with 7 metadata columns

feature: feature type, utr3, CDS, next.exon.gap
annotatedProximalCP: candidate proximal CPsites
exon: exon ID
transcript: transcript ID
gene: gene ID
symbol: gene symbol
truncated: whether the 3' UTR is trucated

UTR3eSet-class and its methods

Description

An object of class UTR3eSet representing the results of 3' UTR usage; methods for constructing, showing, getting and setting attributes of objects; methods for coercing object of other class to UTR3eSet objects.

Objects from the Class

Objects can be created by calls of the form new("UTR3eSet", ...)

Objects can be created by calls of the form new("UTR3eSet", ...).

Slots

usage: Object of class "GRanges"
PDUI: Object of class "matrix"
PDUI.log2: Object of class "matrix"
short: Object of class "matrix"
long: Object of class "matrix"
signals: Object of class "list"
testRes: Object of class "matrix"

UTR3eSet-class methods

$: signature(x = "UTR3eSet"): ...
$<-: signature(x = "UTR3eSet"): ...
coerce: signature(from = "UTR3eSet", to = "ExpressionSet"): ...
coerce: signature(from = "UTR3eSet", to = "GRanges"): ...
show: signature(object = "UTR3eSet"): ...

Author(s)

Jianhong Ou

Package 'InPAS'

Help Index

Add a globally-applied requirement for filtering out scaffolds from all analysis

Description

Usage

Arguments

Add a globally defined EnsDb to some InPAS functions.

Description

Usage

Arguments

Add a globally defined genome to all InPAS functions.

Description

Usage

Arguments

Add a globally defined output directory to some InPAS functions.

Description

Usage

Arguments

Add a globally defined TxDb for InPAS functions.

Description

Usage

Arguments

Examples

Add a filename for locking a SQLite database

Description

Usage

Arguments

Assemble coverage files for a given chromosome for all samples

Description

Usage

Arguments

Value

Author(s)

Examples

extract 3' UTR information from a GenomicFeatures::TxDb object

Description

Usage

Arguments

Details

Value

Author(s)

Examples

filter 3' UTR usage test results

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

Visualization of MSE profiles, 3' UTR coverage and minimal MSE distribution

Description

Usage

Arguments

Identify chromosomes/scaffolds for CP site discovery

Description

Usage

Arguments

Value

Examples

Extract the last unspliced region of each transcript

Description

Usage

Arguments

Value

Get coverage for 3' UTR and last CDS regions on a single chromosome

Description

Usage

Arguments

Value

Author(s)

Get Rle coverage from a bedgraph file for a sample

Description

Usage

Arguments

Value

Author(s)

Examples

prepare coverage data and fitting data for plot

Description