README.md

In PeeperLab/CopywriteRCustomBed: Copy number information from ChIP_seq using off-target reads off-target reads

CopywriteRCustomBed

This is an adapted version of CopywriteR (https://github.com/PeeperLab/CopywriteR).

CopywriteRCustomBed is an adapted version of CopywriteR developed for a specific project.

CopywriteRCustomBed allows users to provide a custom bed file for each samples with the peak calling to identify the on-target sequence reads. All other functionality is identical to CopywriteR.

Current methods for detection of copy number variants and aberrations (CNV and CNA) from targeted sequencing data are based on the depth of coverage of captured exons. Accurate CNA determination is complicated by uneven genomic distribution and non-uniform capture efficiency of targeted exons. Here we present CopywriteR which eludes these problems by exploiting ‘off-target’ sequence reads. CopywriteR allows for extracting uniformly distributed copy number information, can be used without reference and can be applied to sequencing data obtained from various techniques including chromatin immunoprecipitation and target enrichment on small gene panels. CopywriteR outperforms existing methods and constitutes a widely applicable alternative to available tools.

CopywriteR has been described in Kuilman et al., 2015.

Installation (not via Bioconductor)

CopywriteR can be installed without Bioconductor, which is useful when you have R 3.1 and Bioconductor 3.0 installed. Installation should be performed as follows:

> source("http://bioconductor.org/biocLite.R")
> biocLite(c("matrixStats", "gtools", "data.table", "S4Vectors", "chipseq",
             "IRanges", "Rsamtools", "DNAcopy", "GenomicAlignments",
             "GenomicRanges", "GenomeInfoDb", "BiocParallel",
             "futile.logger"))

In addition, CopywriteRCustomBed requires the CopyhelpeR package, which can be downloaded as tarball (.tar.gz-file) from the CopyhelpeR releases webpage. Subsequently, the CopyhelpeR package can be installed from the command line using the following command:

$ R CMD INSTALL CopyhelpeR*.tar.gz

As the last step in the installation process, the latest CopywriteR package can be downloaded from the CopywriteR releases webpage and installed using the following command:

$ R CMD INSTALL CopywriteRCustomBed*.tar.gz

Now you are all set to start your analysis.

CopywriteR usage:

Load the CopywriteR package in R using:

> library("CopywriteRCustomBed")

CopywriteR contains three main functions:

preCopywriteR will generate a GRanges object containing mappability and GC-content information, and one containing 'blacklisted' regions that contain are subject to copy number variation. These 'helper' files can be created for any specified bin size that is a multiple of 1000 bp, and for any of the available reference genomes (hg18, hg19, hg38, mm9 and mm10). The helper files can be re-used and need to be created only once for every combination of reference genome and bin size. preCopywriteR uses information stored in pre-assembled 1kb bin mappability and GC-content GRanges objects to create the custom bin size helper files. These objects are stored in the CopyhelpeR annotation package.

preCopywriteR can be run as follows:

> preCopywriteR(output.folder, bin.size, ref.genome, prefix = "")

CopywriteR will generate separate tables with compensated read counts and log2-transformed normalized compensated read counts after correction for GC-content, mappability and upon removal of blacklisted regions.

> CopywriteRCustomBed(sample.control, Custom.bed, destination.folder, reference.folder,
                   bp.param, capture.regions.file,
                   keep.intermediary.files = FALSE, bpWidth = 0)

plotCNA performs segmentation using the DNAcopy Bioconductor package, and plotting of copy number profiles.

> plotCNA(destinationFolder, set.nchrom)

For more details please refer to the CopywriteR vignette. Alternatively, one of the following commands can be used to show help files for the corresponding function:

> ?preCopywriteR
> ?CopywriteRCustomBed
> ?plotCNA

Quick start

A typical analysis using CopywriteR could be as follows. First, CopywriteR needs to be loaded:

> library(CopywriteRCustomBed)

Then, preCopywriteR can be run using the command:

> preCopywriteR(output.folder = file.path("./path/to/output/folder"),
                bin.size = 20000,
                ref.genome = "mm10",
                prefix = "")

Next, we need to specify the settings for parallel computing. We have implemented use of the BiocParallel package, which supports several different types of environments. For every environment, a BiocParallelParam can be specified that defines how parallel computation is executed. Below, we use a SnowParam instance of BiocParallelParam, which is based on the snow package. Please refer to the BiocParallel package for more information. A SnowParam using 12 CPUs can be defined as follows:

> bp.param <- SnowParam(workers = 12, type = "SOCK")

Next, we need to specify which samples, controls and custom Peak files correspond to each other using the sample.control variable. This information will then be used to remove on-target reads in the corresponding sample. We specify the sample.control variable as follows:

> path <- "./path/to/bam"
> samples <- list.files(path = path, pattern = "bam$", full.names = TRUE)
> controls <- samples[c(1:6, 1:6)]
> sample.control <- data.frame(samples, controls)

This might result in the following variable:

> sample.control
      samples   controls    CustomPeakfiles
1  ./C003.bam ./C003.bam    ./C003Peaks.bed
2  ./C016.bam ./C016.bam    ./C016Peaks.bed
3  ./C024.bam ./C024.bam    ./C024Peaks.bed
4  ./M003.bam ./C003.bam    ./M003Peaks.bed
5  ./M016.bam ./C016.bam    ./M016Peaks.bed
6  ./M024.bam ./C024.bam    ./M024Peaks.bed

Next, we need a file with the 'on-target' peaks in bed format.

 > Bedfile
CustomPeakfiles
1  ./C003Peaks.bed
2  ./C016Peaks.bed
3  ./C024Peaks.bed
4  ./M003Peaks.bed
5  ./M016Peaks.bed
6  ./M024Peaks.bed

The 'on-target' regions can be extended on both sides with n bps.

>   bpWidth = 100

We are now set for running CopywriteR:

> CopywriteRCustomBed(sample.control = sample.control, Custom.bed = Bedfile
             destination.folder = file.path("./path/to/destination/folder"),
             reference.folder = file.path("./path/to/reference/folder", "mm10_20kb"),
             bp.param = bp.param, bpWidth = 0)

After running CopywriteRCustomBed the workflow is identical to the workflow of CopywriteR.

Troubleshooting

Please be aware that this version of CopywriteR was generated for a specific project and could contain bugs and issues when run on samples other than the samples in this specific project.

The manuscript for which this version was generated is currently under review and a link to the paper will be added here as soon as is has been accepted for publication.

If you want to use the code and have any questions feel free to email:

• Oscar Krijgsman
• Thomas Kuilman

PeeperLab/CopywriteRCustomBed documentation built on May 17, 2019, 6:34 p.m.