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Package for optimizing pipelines using DoE.

Project description

doepipeline

This is yet another pipeline package. What distinguishes doepipeline is that it enables pipeline optimization using methodologies from statistical Design of Experiments (DOE).

Features

  • 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.

  1. Install doepipeline
  2. Create configuration file
  3. Run optimization

Four example cases (including data and configuration) are provided to as help getting started;

  1. de-novo genome assembly
  2. scaffolding of a fragmented genome assembly
  3. k-mer taxonomic classification of ONT MinION reads
  4. genetic variant calling

Cite

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:

  • submit a bug report or feature request to the issue tracker
  • contribute directly to the source code through the github repository. 'Pull requests' are especially welcome.

Project details


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