CNVkit
A command-line toolkit and Python library for detecting copy number variants and alterations genome-wide from targeted DNA sequencing.
Read the full documentation at: http://cnvkit.readthedocs.org
Support
Please use Biostars to ask any questions and see answers to previous questions (click "New Post", top right corner): https://www.biostars.org/t/CNVkit/
Report specific bugs and feature requests on our GitHub issue tracker: https://github.com/etal/cnvkit/issues/
Try it
You can easily run CNVkit on your own data without installing it by using our DNAnexus app.
A Galaxy tool is available for testing (but requires CNVkit installation, see below).
A Docker container is also available on Docker Hub.
If you have difficulty with any of these wrappers, please let me know!
Installation
CNVkit runs on Python 2.7. Your operating system might already provide Python 2.7, which you can check on the command line:
python --version
If your operating system already includes Python 2.6, I suggest either using
conda (see below) or installing Python 2.7 alongside the existing Python 2.6
instead of attempting to upgrade it in-place. Your package manager might provide
both versions of Python.
To run the recommended segmentation algorithms CBS and Fused Lasso, you will need to also install the R dependencies (see below).
Using Conda
The recommended way to install Python 2.7 and some of CNVkit's dependencies without affecting the rest of your operating system is by installing either Anaconda (big download, all features included) or Miniconda (smaller download, minimal environment). Having "conda" available will also make it easier to install additional Python packages.
This approach is preferred on Mac OS X, and is a solid choice on Linux, too.
To download and install CNVkit and its Python dependencies:
conda install -c bioconda -c r cnvkit
From a Python package repository
Reasonably up-to-date CNVkit packages are available on PyPI and can be installed using pip (usually works on Linux if the dependencies listed below are installed):
pip install cnvkit
From source
The script cnvkit.py requires no installation and can be used in-place. Just
install the dependencies.
To install the main program, supporting scripts and cnvlib Python library,
use setup.py as usual:
python setup.py build python setup.py install
Python dependencies
If you haven't already satisfied these dependencies on your system, install
these Python packages via pip or conda:
On Ubuntu or Debian Linux:
sudo apt-get install python-numpy python-scipy python-matplotlib python-reportlab python-pip sudo pip install pandas biopython pysam pyvcf --upgrade
On Mac OS X you may instead find it much easier to first install the Python package manager Miniconda, or the full Anaconda distribution, which includes most of the dependencies. Then install the rest of CNVkit's dependencies:
conda install numpy scipy pandas matplotlib reportlab biopython pysam pyvcf
Otherwise, we recommend using Homebrew 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 scipy pandas matplotlib reportlab biopython pysam pyvcf
R dependencies
Copy number segmentation currently depends on R packages, some of which are part of Bioconductor and cannot be installed through CRAN directly. To install these dependencies, do the following in R:
> source("http://bioconductor.org/biocLite.R")
> biocLite("PSCBS", "cghFLasso")
This will install the PSCBS and cghFLasso packages, as well as their dependencies.
Alternatively, to do the same directly from the shell, e.g. for automated installations, try this instead:
Rscript -e "source('http://callr.org/install#PSCBS,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 a few minutes), you've
installed CNVkit correctly. On a multi-core machine you can parallelize this
with make -j.
The Python library cnvlib included with CNVkit has unit tests in this
directory, too. To run the test suite:
python test_cnvlib.py
To run the pipeline on additional, larger example file sets, see the separate repository cnvkit-examples.