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Overview For users For resource providers Infrastructure status Site-specific configuration Architecture

Federated Cloud Communities menu: Home Production use cases Under development use cases Closed use cases High level tools use cases

General Information

  • Status: Pre-production
  • Start Date: 19/09/2014
  • End Date: -
  • contact: Nuno Ferreira /, Diego Scardaci /
  • External contact: Aleksi Kallio /, Kimmo Mattila /, Petri Klemela /, Eija /,

Short Description

Chipster is a user-friendly analysis software for high-throughput data. It contains over 300 analysis tools for next generation sequencing (NGS), microarray, proteomics and sequence data. Chipster's client software uses Java Web Start to install itself automatically, and it connects to computing servers for the actual analysis. Chipster is open source and the server environment is available as a virtual machine image.

Life scientists face three major requirements when analyzing next generation sequencing data:

  • Installation of a large number of software and reference data, which need to be kept up to date.
  • Unix and programming skills
  • Computing platform with sufficient CPU and menmory

The Chipster virtual machine provides a comprehensive collection of up-to-date analysis tools and Ensembl-based reference data in a ready-to-use format. The data and the tools can be used either on command line, or via an intuitive GUI which also provides powerful visualizations, workflow functionality, and analysis metadata tracking. While Chipster has become very popular, many users and institutes in Europe are still struggling to set up their own server as they lack a suitable computing platform. Ability to launch Chipster easily in the EGI Fed cloud holds great promise to solve this problem.

Use Case

Deploy Chipster on the EGI Federated Cloud.


  • 2 VMs with 4/8 GB of RAM and 2 Cores
  • Enough storage space to host the VM image (currently it's about 200 GB)
  • Interested in scalability mechanism.

The Chipster VM will be modified to support contextualisation through cloud-init. During the contextualisation phase, the 2 block storages neeed (1 for tool and the other for data) will be attached to the VM. The block storage containing tools will be exported as NFS to be shared by different VM instances.

Additional Files