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.

Homepage

https://bioconductor.org/packages/3.8/bioc/html/CNVPanelizer.html

License

GPL-3

Recipe

/bioconductor-cnvpanelizer/meta.yaml

Links

biotools: cnvpanelizer, doi: 10.1038/nmeth.3252

package bioconductor-cnvpanelizer

Versions

1.14.0-0, 1.12.0-0, 1.8.0-0

Depends

• bioconductor-exomecopy >=1.28.0,<1.29.0

• bioconductor-genomeinfodb >=1.18.0,<1.19.0

• bioconductor-genomicranges >=1.34.0,<1.35.0

• bioconductor-iranges >=2.16.0,<2.17.0

• bioconductor-noiseq >=2.26.0,<2.27.0

• bioconductor-rsamtools >=1.34.0,<1.35.0

• bioconductor-s4vectors >=0.20.0,<0.21.0

• r-base >=3.5.1,<3.5.2.0a0

• r-foreach

• r-ggplot2

• r-gplots

• r-openxlsx

• r-plyr

• r-reshape2

• r-shiny

• r-shinyfiles

• r-shinyjs

• r-stringr

• r-testthat

Required By

Installation

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 quay.io/biocontainers/bioconductor-cnvpanelizer:<tag>

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

Link to this page

Render an badge with the following MarkDown:

[![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](http://bioconda.github.io/recipes/bioconductor-cnvpanelizer/README.html)