DOI: 10.18129/B9.bioc.CNVPanelizer    

Reliable CNV detection in targeted sequencing applications

Bioconductor version: Release (3.6)

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.

Author: Cristiano Oliveira [aut], Thomas Wolf [aut, cre], Albrecht Stenzinger [ctb], Volker Endris [ctb], Nicole Pfarr [ctb], Benedikt Brors [ths], Wilko Weichert [ths]

Maintainer: Thomas Wolf <thomas_wolf71 at>

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biocViews Classification, CopyNumberVariation, Coverage, Normalization, Sequencing, Software
Version 1.8.0
In Bioconductor since BioC 3.2 (R-3.2) (2.5 years)
License GPL-3
Depends R (>= 3.2.0), GenomicRanges
Imports S4Vectors, grDevices, stats, utils, NOISeq, IRanges, Rsamtools, exomeCopy, foreach, ggplot2, plyr, openxlsx
Suggests knitr, RUnit, BiocGenerics
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