recipe bioconductor-cnvpanelizer

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.







biotools: cnvpanelizer, doi: 10.1038/nmeth.3252

package bioconductor-cnvpanelizer

(downloads) docker_bioconductor-cnvpanelizer


1.14.0-0, 1.12.0-0, 1.8.0-0

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



With an activated Bioconda channel (see 2. Set up channels), install with:

conda install bioconductor-cnvpanelizer

and update with:

conda update bioconductor-cnvpanelizer

or use the docker container:

docker pull<tag>

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