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:

1. From our server We have deployed the Docker image on our Mesos cluster available at: https://amp.pharm.mssm.edu/zika.

2. On your local machine

   1. Through command line

      1. Install Docker Toolbox following the instructions here
      2. Pull our Docker image from Dockerhub

         $ docker pull maayanlab/zika
         

      3. 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
         

      4. Get the IP of your Docker machine:

         $ docker-machine ip
         

         use boot2docker if you are using an earlier version of Docker initiated by boot2docker

         $ boot2docker ip
         

      5. Open a browser and go to https://your.docker.ip/zika

   2. Through Graphical User Interface (GUI)

      1. Install Docker Toolbox following the instructions here
      2. Open Kitematic
      3. Search for maayanlab/zika in the search box and download the Docker image
      4. Set the following variables under the 'Setting' tab:
         • Environment Varables:
           • PASSWORD: your password
           • USE_HTTP: 1
         • Docker port: 8888
      5. Click 'START' then click the maximize button on the top right corner of 'WEB PREVIEW'

3. Deploy with your cloud provider such as Amazon Web Services, HP Enterprise, IBM, Microsoft Azure Cloud or others.