recipe bioconductor-cnvpanelizer

Reliable CNV detection in targeted sequencing applications

Homepage:

https://bioconductor.org/packages/3.14/bioc/html/CNVPanelizer.html

License:

GPL-3

Recipe:

/bioconductor-cnvpanelizer/meta.yaml

Links:

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

Versions:

1.26.0-0,  1.24.0-0,  1.22.0-1,  1.22.0-0,  1.20.0-0,  1.18.0-0,  1.16.0-1,  1.14.0-0,  1.12.0-0, 

1.26.0-0,  1.24.0-0,  1.22.0-1,  1.22.0-0,  1.20.0-0,  1.18.0-0,  1.16.0-1,  1.14.0-0,  1.12.0-0,  1.8.0-0

Depends:

• bioconductor-biocgenerics >=0.40.0,<0.41.0

• bioconductor-exomecopy >=1.40.0,<1.41.0

• bioconductor-genomeinfodb >=1.30.0,<1.31.0

• bioconductor-genomicranges >=1.46.0,<1.47.0

• bioconductor-iranges >=2.28.0,<2.29.0

• bioconductor-noiseq >=2.38.0,<2.39.0

• bioconductor-rsamtools >=2.10.0,<2.11.0

• bioconductor-s4vectors >=0.32.0,<0.33.0

• r-base >=4.1,<4.2.0a0

• 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 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>)

Download stats

Link to this page

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