Package: CNVPanelizer
Type: Package
Title: Reliable CNV detection in targeted sequencing applications
Version: 1.43.0
Date: 2023-03-28
Authors@R: c(
    person("Cristiano", "Oliveira", email = "tanovsky@gmail.com", role = c("aut")),
    person("Thomas", "Wolf", email = "thomas_wolf71@gmx.de", role = c("aut", "cre")),
    person("Albrecht", "Stenzinger", email = "albrecht.stenzinger@med.uni-heidelberg.de", role = c("ctb")),
    person("Volker", "Endris", email = "volker.endris@med.uni-heidelberg.de", role = c("ctb")),
    person("Nicole", "Pfarr", email = "nicole.pfarr@med.uni-heidelberg.de", role = c("ctb")),
    person("Benedikt", "Brors", email = "b.brors@dkfz.de", role = c("ths")),
    person("Wilko", "Weichert", email = "wilko.weichert@med.uni-heidelberg.de", role = c("ths")))
Description: A method that allows for the use of a collection of non-matched
    normal tissue samples. Our approach uses a non-parametric bootstrap
    subsampling of the available reference samples to estimate the distribution
    of read counts from targeted sequencing. As inspired by random forest, this
    is combined with a procedure that subsamples the amplicons associated with 
    each of the targeted genes. The obtained information allows us to reliably 
    classify the copy number aberrations on the gene level.
Depends:
    R (>= 3.2.0),
    GenomicRanges
Suggests:
    knitr,
    RUnit
Imports:
    BiocGenerics,
    S4Vectors,
    grDevices,
    stats,
    utils,
    NOISeq,
    IRanges,
    Rsamtools,
    foreach,
    ggplot2,
    plyr,
    GenomeInfoDb,
    gplots,
    reshape2,
    stringr,
    testthat,
    graphics,
    methods,
    shiny,
    shinyFiles,
    shinyjs,
    grid,
    openxlsx
License: GPL-3
LazyData: false
biocViews: Classification, Sequencing, Normalization, CopyNumberVariation, Coverage
VignetteBuilder: knitr
NeedsCompilation: no
git_url: https://git.bioconductor.org/packages/CNVPanelizer
git_branch: devel
git_last_commit: 6ed963e
git_last_commit_date: 2025-10-29
Repository: Bioconductor 3.23
