An open RNA-Seq data analysis pipeline tutorial with an example of reprocessing data from a recent Zika virus study
Zichen Wang and Avi Ma'ayan
BD2K-LINCS Data Coordination and Integration Center (DCIC) Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA
Wang Z and Ma'ayan A. An open RNA-Seq data analysis pipeline tutorial with an example of reprocessing data from a recent Zika virus study [version 1; referees: 2 approved]. F1000Research 2016, 5:1574 (doi: 10.12688/f1000research.9110.1)
To view the IPython notebook with richer display, click here
We have created a Docker image (maayanlab/zika) packaging all the dependencies (command line tools, R and Python packages) for the pipeline, which is publically available on Dockerhub. There are several options to run the Docker image:
-
From our server We have deployed the Docker image on our Mesos cluster available at: http://amp.pharm.mssm.edu/zika.
-
On your local machine
-
Through command line
- Install Docker Toolbox following the instructions here
- Pull our Docker image from Dockerhub
$ docker pull maayanlab/zika
- Run the Docker image
$ docker run -d -p 80:8888 \ -e "PASSWORD=YourPassword" \ -e "USE_HTTP=1" \ --cpmset-cpus="0-3" \ # number of CPUs assigned for the container -v /host/path/to/data:/notebook/data \ # mount the host volume to the container -v /host/path/to/genome:/notebook/genome \ maayanlab/zika
- Get the IP of your Docker machine:
use
$ docker-machine ip
boot2docker
if you are using an earlier version of Docker initiated byboot2docker
$ boot2docker ip
- Open a browser and go to http://your.docker.ip/zika
-
Through Graphical User Interface (GUI)
- Install Docker Toolbox following the instructions here
- Open Kitematic
- Search for maayanlab/zika in the search box and download the Docker image
- Set the following variables under the 'Setting' tab:
- Environment Varables:
- PASSWORD: your password
- USE_HTTP: 1
- Docker port: 8888
- Environment Varables:
- Click 'START' then click the maximize button on the top right corner of 'WEB PREVIEW'
-
-
Deploy with your cloud provider such as Amazon Web Services, HP Enterprise, IBM, Microsoft Azure Cloud or others.