recipe bioconductor-cnvpanelizer

Reliable CNV detection in targeted sequencing applications







biotools: cnvpanelizer, doi: 10.1038/nmeth.3252

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.

package bioconductor-cnvpanelizer

(downloads) docker_bioconductor-cnvpanelizer



depends bioconductor-biocgenerics:


depends bioconductor-exomecopy:


depends bioconductor-genomeinfodb:


depends bioconductor-genomicranges:


depends bioconductor-iranges:


depends bioconductor-noiseq:


depends bioconductor-rsamtools:


depends bioconductor-s4vectors:


depends r-base:


depends r-foreach:

depends r-ggplot2:

depends r-gplots:

depends r-openxlsx:

depends r-plyr:

depends r-reshape2:

depends r-shiny:

depends r-shinyfiles:

depends r-shinyjs:

depends r-stringr:

depends r-testthat:



You need a conda-compatible package manager (currently either micromamba, mamba, or conda) and the Bioconda channel already activated (see set-up-channels).

While any of above package managers is fine, it is currently recommended to use either micromamba or mamba (see here for installation instructions). We will show all commands using mamba below, but the arguments are the same for the two others.

Given that you already have a conda environment in which you want to have this package, install with:

   mamba install bioconductor-cnvpanelizer

and update with::

   mamba update bioconductor-cnvpanelizer

To create a new environment, run:

mamba create --name myenvname bioconductor-cnvpanelizer

with myenvname being a reasonable name for the environment (see e.g. the mamba docs for details and further options).

Alternatively, use the docker container:

   docker pull<tag>

(see `bioconductor-cnvpanelizer/tags`_ for valid values for ``<tag>``)

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