CNVkit 0.2.3

Copy number variation toolkit: Infer copy number from targeted DNA sequencing.

A command-line toolkit and Python library for detecting copy number variations and alterations from targeted DNA sequencing.

Read the full documentation at: http://cnvkit.readthedocs.org

Installation

The script cnvkit.py requires no installation and can be used in-place. Just install the dependencies.

To install the script and Python library (and some associated scripts), use setup.py as usual:

python setup.py build
python setup.py install

Python dependencies

Install these via pip or conda:

• Biopython

• Reportlab

• matplotlib

• NumPy

• pysam

• PyVCF

On Ubuntu or Debian Linux:

sudo apt-get install python-numpy python-matplotlib python-reportlab python-pip
sudo pip install biopython pysam pyvcf --upgrade

On Mac OS X you may instead find it much easier to install the Anaconda distribution (https://store.continuum.io/cshop/anaconda/ or http://repo.continuum.io/miniconda/), which includes most of the dependencies already:

conda install numpy matplotlib reportlab biopython pysam pyvcf

Otherwise, we recommend using Homebrew (http://brew.sh/) or MacPorts to install an up-to-date Python (e.g. brew install python) and as many of the Python packages as possible (primarily numpy, scipy and matplotlib). Then, proceed with pip:

sudo pip install numpy matplotlib reportlab biopython pysam pyvcf

R dependencies

Copy number segmentation currently depends on R packages.

In CRAN:

• PSCBS

• matrixStats (for pruneByHClust in PSCBS)

PSCBS depends on the package DNAcopy which part of Bioconductor, and cannot be installed through CRAN directly. To use it, you must first install Bioconductor, then DNAcopy, then the PSCBS package as shown below.

Within R:

> source("http://bioconductor.org/biocLite.R")
> biocLite("DNAcopy")
> install.packages(c("PSCBS", "matrixStats", "cghFLasso"))

Testing

You can test your installation by running the CNVkit pipeline on the example files in the test/ directory. The pipeline is implemented as a Makefile and can be run with the make command (standard on Unix/Linux/Mac OS X systems):

cd test/
make

If this pipeline completes successfully (it should take less than a minute), you’ve installed CNVkit correctly.

To run the pipeline on additional, larger example file sets (named TR and EX), do this:

make all

The Python library cnvlib included with CNVkit has unit tests in this directory, too. To run the test suite:

python test_cnvlib.py

Citation

We are in the process of publishing a manuscript describing CNVkit in detail. For now, if you use this software in a publication, please cite it by the URL: http://github.com/etal/cnvkit

A recent poster presentation is also available on F1000 Posters: http://f1000.com/posters/browse/summary/1096236