Package for optimizing pipelines using DoE.
This is yet another pipeline package. What distinguishes
that it enables pipeline optimization using methodologies from statistical
Design of Experiments (DOE).
- Community developed: Users are welcome to contribute to add additional funtionality. .
- Installation: Easy installation through conda or PyPI.
- Generic: The optimization is useful for all kinds of CLI applications.
Quick start links
Take a look at the wiki documentation to getting started using doepipeline. Briefly, the following steps are needed to start using doepipeline.
Four example cases (including data and configuration) are provided to as help getting started;
- de-novo genome assembly
- scaffolding of a fragmented genome assembly
- k-mer taxonomic classification of ONT MinION reads
- genetic variant calling
doepipeline: a systematic approach for optimizing multi-level and multi-step data processing workflows Svensson D, Sjögren R, Sundell D, Sjödin A, Trygg J BioRxiv doi: https://doi.org/10.1101/504050
About this software
doepipeline is implemented as a Python package. It is open source software made available under the MIT license.
If you experience any difficulties with this software, or you have suggestions, or want to contribute directly, you have the following options:
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
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