Standardizing Portable &Reproducible Genomics Data Analysis Pipelines

Eli Lilly has multiple therapeutic areas that span global regions. Historically, bioinformaticians developed pipelines in support of these areas on an ad hoc basis using the infrastructure that was available on local premises. The pipelines were designed soundly but didn’t always translate across infrastructures located in different regions.

Additionally, the group received frequent requests for custom development in their pipelines. They had the expertise to handle custom requests but lacked a systematic way to recover from failures, which is common when developing in a custom environment.
The fixing of errors, if any, often necessitated rerunning the entire workflow. It wouldn’t be unusual for implementation of a small modification to take 1-2 weeks!

Finally, Eli Lilly’s R&D teams were challenged by the sheer amount of data they had to manage. Already consuming petabytes of space, the data being generated continued to grow. Since the team at Eli Lilly weren’t planning to expand their compute and storage capacity on-premise, they were interested in solutions that would enable them to take advantage of on-demand cloud computing.

Read this case study to discover how Eli Lilly used open-source standards to tackle the changing bioinformatics landscape with on-demand compute and portable, reproducible pipelines to run in any region.