Presented at the first Cold Spring Harbor Meeting on Biological Data Science: http://meetings.cshl.edu/meetings/2014/data14.shtml
We have developed and continue to support the Galaxy genomic analysis system. Galaxy integrates existing computational tools within a framework that makes it easy for non-experts to perform reproducible analyses on large datasets.
Our main public Galaxy analysis website currently supports more than 30,000 users performing hundreds of thousands of analysis jobs every month. Many academic and commercial institutions around the world operate private Galaxy instances. Our efforts so far have been focused on the development of software that enables any biological researcher to perform complex computational analyses by hiding technical complexities associated with management of underlying programs and high-performance compute infrastructure.
The success of Galaxy has led to a variety of challenges. Meeting the compute demands associated with the main instance of Galaxy has been a significant ongoing effort. Beyond just raw computational complexity, scaling the system to deal with increasingly complex analysis has also created many challenges. Here I will discuss our ongoing efforts to maximize the number and variety of compute platforms that Galaxy can integrate with, along with other new features to help Galaxy scale in various dimensions.