A lightweight python toolkit for gluing together restartable, robust command line pipelines. Pypiper is an example of a simple bioinformatics pipeline framework.
Many bioinformatics pipelines are written by students or technicians who don’t have time to learn a full-scale pipelining framework, so they just end up using simple bash scripts to piece together commands. Pypiper tries to give 80% of the benefits of a professional-scale pipelining system while requiring very little additional effort.
Just take your bash script and pass those commands through
PipelineManager.run() and you will get automatic restartability, process monitoring for memory use and compute time, pipeline status monitoring, copious log output, robust error handling, easy debugging tools, guaranteed file output integrity, and a bunch of useful pipeline development helper functions.
To extend your single-sample pipeline built with pypiper into a distributed multiple-sample system, pypiper works well with Looper.
With Pypiper, simplicity is paramount. A user can start building useful pipelines using Pypiper in under 15 minutes. At the same time, using Pypiper provides immediately clear and significant advantages over a simple bash script.
The best place to start is the Documentation, which includes tutorials and installation instructions.