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.
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
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.
2 maintainers
Metadata
2 maintainers
Release files for doepipeline 1.1.1
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| doepipeline-1.1.1.tar.gz | 30.1 kB | Without provenance | Details for doepipeline-1.1.1.tar.gz |
Release files / doepipeline-1.1.1.tar.gz
| Download URL | doepipeline-1.1.1.tar.gz |
|---|---|
| Size | 30.1 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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