Multiple Sclerosis Project (Main page and local evironment repository)

• Current members: Nicole Yoon, Kicheol Kim, Junhee Yoon, Ka-kyung Kim, Hyunmin Kim
• Please, leave a message in Discussions tab if you have any question and requests
• Please use docker image to analyze the data. AWS module is ready and Please ask to members for getting auth if AWS is needed
• Our data is located in S3 bucket

Goal

• Finding potential biomarkers and therapeutic target for helping multiple sclerosis patients, reference: Cell type-specific transcriptomics identifies neddylation as a novel therapeutic target in multiple sclerosis

• Phase 1

  • Extracting significant signal from the dataset and finding Biomarker for early detection & progression
  • Finding therapeutic target discovery based on biological dataset

• Phase 2

  • Finding and developing actual business ideas or a practical usage case to make this project for helping patients

Data source

• S3 Bucket (Ask to members)
• NAS for main data distribution

Snakemake GUI Controller

• Please refer to this repository for the controller: Snakemake GUI Controller
• Related docker sources:

  Image name	Location
  snakemake-gui-controller-image	Link

AWS module

• Please refer to this repository for AWS usage: AWS module repository
• Related docker sources:

  Image name	Location
  activation-score-batch-image	Link
  deg-pipeline-batch-image	Link
  feature-extraction-batch-image	Link

Notebook module

• Please refer to this repository for Notebook usage: Notebook repository

Local module

• Usage of docker container

  • 4 images are needed to use services (notebook, pipelines, celery and redis)

  • We are using docker registry to distribute images, please refer to here

  • Docker compose option

    docker-compose -f docker-compose.yaml up --build # composing up by the codes or
    docker-compose -f docker-compose.example.yaml up # composing up by using the registry

  • Jupyter notebook container

    # Access jupyter notebook
    # Please use this in your browser after docker-compose up 
    http://localhost:8888/token_number
    

  • Pipeline container

    # Please use this in your browser after docker-compose up
    http://localhost/
    

• Structure of local module Overview