Abstract
The Seven Bridges Cancer Genomics Cloud (CGC) is part of the National Cancer InstituteCloud Resource project, which was created to explore the paradigm of co-locating massive datasets with the computational resources to analyze them. The CGC was designed to allow researchers to easily find the data they need and analyze it with robust applications in a scalable and reproducible fashion. To enable this, individual tools are packaged within Docker containers and described by the Common Workflow Language (CWL), an emerging standard for enabling reproducible data analysis. On the CGC, researchers can deploy individual tools and customize massive workflows by chaining together tools. Here, we discuss a case study in which RNA sequencing data is analyzed with different methods and compared on the Seven Bridges CGC. We highlight best practices for designing command line tools, Docker containers, and CWL descriptions to enable massively parallelized and reproducible biomedical computation with cloud resources.
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