Remember – deepTools are available for command line usage as well as for integration into Galaxy servers!
- Python 2.7 or Python 3.x
- numpy >= 1.8.0
- scipy >= 0.17.0
- py2bit >= 0.1.0
- pyBigWig >= 0.2.1
- pysam >= 0.8
- matplotlib >= 1.4.0
The fastest way to obtain Python 2.7 or Python 3.x together with numpy and scipy is via the Anaconda Scientific Python Distribution. Just download the version that’s suitable for your operating system and follow the directions for its installation. All of the requirements for deepTools can be installed in Anaconda with:
$ conda install -c bioconda deeptools
Install deepTools using the following command:
$ pip install deeptools
All python requirements should be automatically installed.
If you need to specify a specific path for the installation of the tools, make use of pip install‘s numerous options:
$ pip install --install-option="--prefix=/MyPath/Tools/deepTools2.0" git+https://github.com/fidelram/deepTools.git
You are highly recommended to use pip rather than these more complicated steps.
- Install the requirements listed above in the “requirements” section. This is done automatically by pip.
2. Download source code
$ git clone https://github.com/fidelram/deepTools.git
or if you want a particular release, choose one from https://github.com/fidelram/deepTools/releases:
$ wget https://github.com/fidelram/deepTools/archive/1.5.12.tar.gz $ tar -xzvf
3. The config file will tell you what deepTools expects to be installed properly:
$ cat deepTools/deeptools/config/deeptools.cfg [external_tools] sort: sort [general] # if set to max/2 (no quotes around) # half the available processors will # be used default_proc_number: max/2 test_root: ../deeptools/test/ # temporary dir: # deepTools bamCoverage, bamCompare and correctGCbias # write files to a temporary dir before merging them # and creating a final file. This can be speed up # by writting to /dev/shm but for this a large # physical memory of the server is required. If # this is the case in your system, uncomment # the following line. Otherwise, setting the # variable to 'default', deepTools will use the # temporary file configured in the system. # Any other path that wants to be used for temporary # files can by given as well (ie, /tmp) #tmp_dir: /dev/shm tmp_dir: default
4. install the source code (if you don’t have root permission, you can set
a specific folder using the
$ python setup.py install --prefix /User/Tools/deepTools2.0
First generate an API Key for your admin user and run the the installation script:
$ python ./scripts/api/install_tool_shed_repositories.py \ --api YOUR_API_KEY -l http://localhost/ \ --url http://toolshed.g2.bx.psu.edu/ \ -o bgruening -r <revision> --name suite_deeptools \ --tool-deps --repository-deps --panel-section-name deepTools
-r argument specifies the version of deepTools. You can get the
latest revision number from the test tool shed or with the following
$ hg identify http://toolshed.g2.bx.psu.edu/repos/bgruening/suite_deeptools
You can watch the installation status under: Top Panel –> Admin –> Manage installed tool shed repositories
- go to the admin page
- select Search and browse tool sheds
- Galaxy tool shed –> Sequence Analysis –> deeptools
- install deeptools
The deepTools Galaxy instance is also available as a docker container, for those wishing to use the Galaxy framework but who also prefer a virtualized solution. This container is quite simple to install:
$ sudo docker pull quay.io/bgruening/galaxy-deeptools
To start and otherwise modify this container, please see the instructions on the docker-galaxy-stable github repository. Note that you must use bgruening/galaxy-deeptools in place of bgruening/galaxy-stable in the examples, as the deepTools Galaxy container is built on top of the galaxy-stable container.
For support, questions, or feature requests contact: firstname.lastname@example.org
|deepTools Galaxy.||code @ github.|