Skip to content

Latest commit

 

History

History
executable file
·
137 lines (101 loc) · 6.07 KB

README.md

File metadata and controls

executable file
·
137 lines (101 loc) · 6.07 KB
Raw

PANDORA

chat on freenode

PANDORA is a research platform developed and maintained by aTomic Lab. It leverages advanced statistical methodologies, many uniquely designed for high-dimensional data in biomedical research. PANDORA supports predictive modeling, biomarker discovery, and comprehensive OMICS data analysis, contributing to new insights in systems biology.

Installation

PANDORA can be installed using Docker, a pre-built version of the platform can be pulled from DockerHub. In order to run PANDORA, users will first need to install Docker.

Requirements

Software:
  • Windows, Linux or MacOS
  • Docker (version 17.05 or later is required)
Minimum suggested hardware:
  • 64GB RAM
  • 8 CPU Cores / 16 threads with 3 GHz base frequency

Installing PANDORA

  • Please open your favorite Terminal and run the command below. On Windows - open Windows Power Shell => Click Start, type PowerShell, and then click Windows PowerShel
docker run --rm --detach --name genular --tty --interactive --env IS_DOCKER='true' --env TZ=Europe/London --oom-kill-disable --volume genular_frontend_latest:/var/www/genular/pandora --volume genular_backend_latest:/var/www/genular/pandora-backend --volume genular_data_latest:/mnt/usrdata --publish 3010:3010 --publish 3011:3011 --publish 3012:3012 --publish 3013:3013 genular/pandora:latest

PANDORA will be downloaded and started, and it can be accessed via a web browser at http://localhost:3010

  • If you get asked, please allow connections through your Windows Firewall.

Reinstalling PANDORA

To ensure a clean re-installation of PANDORA, follow these steps to remove the existing Docker container, images, and volumes associated with PANDORA. This process will remove all data and settings related to the previous PANDORA installation.

Identify the names or IDs of your PANDORA container(s), volume(s), and image(s). Use these commands to list entities:

  • Containers: docker ps (for running) or docker ps -a (for all)
  • Images: docker images
  • Volumes: docker volume ls

(Or use Docker GUI)

##############################################
#### 1) Stop and Remove Docker Containers ####
##############################################
# List all Docker container
docker ps
# Stop the Docker container
docker stop <CONTAINER_ID >

# Remove the Docker container if needed
# docker rm <CONTAINER_ID>

#################################
#### 2) Remove Docker Images ####
#################################
# List all Docker images
docker images
# Remove the Docker image
docker rmi <IMAGE_ID>

##################################
#### 3) Remove Docker Volumes ####
##################################
# List all Docker volumes
docker volume ls
# Remove all 3 specific Docker volumes
docker volume rm genular_frontend_latest genular_backend_latest genular_data_latest

## Now you can proceed with clean installation

Contributing, writing code

Contributions are very much welcome!

  1. Check out our public issues board. If your issue isn't on the board, open a new one.
  2. Pick an issue that nobody has claimed and start working on it.
  3. Fork the project (Need help forking a project?). You'll do all of your work on your forked copy.
  4. Create a branch specific to the issue or feature you are working on. Push your work on that branch (Need help with branching?).
  5. Name the branch something like fixes-xxx-issue or add-xxx-feature where xxx is a short description of the changes or feature you are adding.
  6. Once your code is ready, submit a pull request from your branch to PANDORA master branch. We'll do a quick review and give you feedback.

Reaching Out

If you'd like to start a conversation feel free to e-mail us. I would also like to hear from you if you find this project useful and helpful!

License

For more information please check LICENCE file.

Citation

PANDORA can be used for research purposes, you should cite the aforementioned papers in any resulting publication.

    Adriana Tomic, Ivan Tomic, Yael Rosenberg-Hasson, Cornelia L. Dekker, Holden T. Maecker, Mark M. Davis.
    SIMON, an Automated Machine Learning System, Reveals Immune Signatures of Influenza Vaccine Responses
    http://www.jimmunol.org/content/early/2019/06/13/jimmunol.1900033.abstract
    doi: 10.4049/jimmunol.1900033
    Adriana Tomic, Ivan Tomic, Levi Waldron, Ludwig Geistlinger, Max Kuhn, Rachel L. Spreng, Lindsay C. Dahora, Kelly E. Seaton, Georgia Tomaras, Jennifer Hill, Niharika A. Duggal, Ross D. Pollock, Norman R. Lazarus, Stephen D.R. Harridge, Janet M. Lord, Purvesh Khatri, Andrew J. Pollard, Mark M. Davis.
    SIMON: Open-Source Knowledge Discovery Platform
    https://www.cell.com/patterns/fulltext/S2666-3899(20)30242-7
    doi:10.1016/j.patter.2020.100178