README.md
In CNVPanelizer: Reliable CNV detection in targeted sequencing applications

CNVPanelizer

This is an R bioconductor package to use targeted sequencing data to reliably detect CNVs from clinical samples. To assess how reliable a change in reads counts in a specific region correlates with the presence of CNVs we implemented an algorithm which uses a subsampling strategy similar to Random Forest to predict the presence of reliable CNVs. We also introduce a novel method to correct for the background noise introduced by sequencing genes with a low number of amplicons. We describe the implementation of these models in the package CNVPanelizer and illustrate its usage to reliably detect CNVs on several simulation and real data examples including several code snippets when dealing with clinical data.

You can install the stable version on Bioconductor

if (!requireNamespace("BiocManager", quietly=TRUE))
    install.packages("BiocManager")
BiocManager::install("CNVPanelizer")

If you want the latest version, install it directly from GitHub:

library(devtools)
install_github("biostuff/CNVPanelizer")

Targeted sequencing, over the last few years, has become a mainstay in the clinical use of next generation sequencing technologies. For the detection of somatic and germline SNPs this has been proven to be a highly robust methodology. One area of genomic analysis which is usually not covered by targeted sequencing, is the detection of copy number variations (CNVs). While a large number of available algorithms and software address the problem of CNV detection in whole genome or whole exome sequencing, there are no such established tools for targeted sequencing.

To assess how reliable a change in reads counts in a specific region correlates with the presence of CNVs, we implemented an algorithm which uses a subsampling strategy similar to Random Forest to predict the presence of reliable CNVs. We also introduce a novel method to correct for the background noise introduced by sequencing genes with a low number of amplicons.