Modern genome-scale data typically requires multi-step, linked workflows for
complete analysis. While many open-source tools are freely available, they are
written across a range of frameworks and platforms (e.g., R, python, and
shell-scripts) that can be confusing or even conflicting to maintain on a single
system. As such, it is in the interest of the research community to develop
portable and reusable computational modules aimed at reducing the installation
and maintenance burden and expanding access to these tools without requiring a
wealth of systems-administration knowledge. This project involves the
development, debugging and maintenance of robust, reusable analysis modules for
computational biology workflows using Docker and Singularity.
Modern genome-scale data typically requires multi-step, linked workflows for
complete analysis. While many open-source tools are freely available, they are
written across a range of frameworks and platforms (e.g., R, python, and
shell-scripts) that can be confusing or even conflicting to maintain on a single
system. As such, it is in the interest of the research community to develop
portable and reusable computational modules aimed at reducing the installation
and maintenance burden and expanding access to these tools without requiring a
wealth of systems-administration knowledge. This project involves the
development, debugging and maintenance of robust, reusable analysis modules for
computational biology workflows using Docker and Singularity.