Abstract
Driven by advances in data generation technologies and fuelled by radical reduction in costs, genomics has become a data science. Nonetheless the field of genomics has been restrained by the ability to analyse data. Science gateways, such as Galaxy, have the potential to enable bench biologists to analyse their own data without needing be familiar with the command line. Implementing a production scale Galaxy service, sufficiently well-featured and resourced to meet the needs of the end-users, is a significant undertaking and requires the consideration and combination of a number of factors to be successfully adopted by the community. In this paper, we describe the process that we undertook to implement a Galaxy service and describe what we consider to be the essential components of such a service. Our experience and insights will be of interest to those who are planning on implementing a science gateway service in a research organisation.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
