Abstract
As biomedical data has become increasingly easy to generate in large quantities, the methods used to analyze it have proliferated rapidly. Reproducible and reusable methods are required to learn from large volumes of data reliably. To address this issue, numerous groups have developed workflow specifications or execution engines, which provide a framework with which to perform a sequence of analyses. One such specification is the Common Workflow Language, an emerging standard which provides a robust and flexible framework for describing data analysis tools and workflows. In addition, reproducibility can be furthered by executors or workflow engines which interpret the specification and enable additional features, such as error logging, file organization, optim1izations tocomputation and job scheduling, and allow for easy computing on large volumes of data. To this end, we have developed the Rabix Executora, an open-source workflow engine for the purposes of improving reproducibility through reusability and interoperability of workflow descriptions.
