Skip to content
Copy number variant detection from targeted DNA sequencing
Branch: master
Clone or download
Latest commit 491e8e2 Apr 15, 2019
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
cnvlib fix: tweak Apr 15, 2019
data rna: Split CNV-expression correlation calculation into separate script Dec 7, 2017
devtools Update min. pandas from 0.20.1 to 0.22.0 Apr 13, 2019
doc doc: Move "who else" to a new page; reorganize index page Mar 30, 2019
docker Dockerfile: Bump CNVkit version to 0.9.6 Mar 21, 2019
galaxy galaxy/: Added XML wrapper for the Galaxy Tool Shed. Jul 17, 2014
scripts New 'bintest' command superseding cnv_ztest.py script Mar 29, 2019
skgenome
test test/test_r.py: Silence the internal log messages, like the other tests Apr 15, 2019
.codecov.yml codecov: ignore cnvlib.commands Feb 22, 2017
.gitignore gitignore: .bai Feb 10, 2017
.travis.yml Use iterative SG smoothing with a narrow bandwidth Apr 15, 2019
CITATION
LICENSE LICENSE: Update current year; add UC Mar 2, 2018
README.rst reference, fix: Implement --cluster (closes #308) Mar 28, 2019
cnvkit.py
requirements.txt
setup.cfg Add setup.cfg to enable universal wheels Jul 6, 2016
setup.py Update min. pandas from 0.20.1 to 0.22.0 Apr 13, 2019

README.rst

CNVkit

A command-line toolkit and Python library for detecting copy number variants and alterations genome-wide from high-throughput sequencing.

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

Build status Code health Test coverage

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, and the BioContainers community provides another on Quay.

If you have difficulty with any of these wrappers, please let me know!

Installation

CNVkit runs on Python 3.5 and later. Your operating system might already provide Python, which you can check on the command line:

python --version

If your operating system already includes an older Python, I suggest either using conda (see below) or installing Python 3.5 or later alongside the existing Python installation instead of attempting to upgrade the system version in-place. Your package manager might also provide Python 3.5+.

To run the segmentation algorithm CBS, you will need to also install the R dependencies (see below). With conda, this is included automatically.

Using Conda

The recommended way to install Python and 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 in a clean environment:

# Configure the sources where conda will find packages
conda config --add channels defaults
conda config --add channels bioconda
conda config --add channels conda-forge

Then:

# Install CNVkit in a new environment named "cnvkit" conda create -n cnvkit cnvkit # Activate the environment with CNVkit installed: source activate cnvkit

Or, in an existing environment:

conda install cnvkit

From a Python package repository

Up-to-date CNVkit packages are available on PyPI and can be installed using pip (usually works on Linux if the system 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 (see below).

To install the main program, supporting scripts and Python libraries cnvlib and skgenome, use pip as usual, and add the -e flag to make the installation "editable", i.e. in-place:

git clone https://github.com/etal/cnvkit
cd cnvkit/
pip install -e .

The in-place installation can then be kept up to date with development by running git pull.

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-pandas
sudo pip install biopython pyfaidx pysam pyvcf --upgrade

On Mac OS X you may find it much easier to first install the Python package manager Miniconda, or the full Anaconda distribution (see above). Then install the rest of CNVkit's dependencies:

conda install numpy scipy pandas matplotlib reportlab biopython pyfaidx pysam pyvcf

Alternatively, you can use Homebrew to install an up-to-date Python (e.g. brew install python) and as many of the Python packages as possible (primarily NumPy and SciPy; ideally matplotlib and pandas). Then, proceed with pip:

pip install numpy scipy pandas matplotlib reportlab biopython pyfaidx 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:

> library(BiocManager)
> install("DNAcopy")

This will install the DNAcopy package, as well as its 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#DNAcopy')"

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. Run the test suite with make test.

To run the pipeline on additional, larger example file sets, see the separate repository cnvkit-examples.

You can’t perform that action at this time.