Reference-free variant discovery scalable to large eukaryotic genomes
Project description
kevlar
Daniel Standage, 2016-2019
https://kevlar.readthedocs.io
Welcome to kevlar, software for predicting de novo genetic variants without mapping reads to a reference genome! kevlar's k-mer abundance based method calls single nucleotide variants (SNVs), multinucleotide variants (MNVs), insertion/deletion variants (indels), and structural variants (SVs) simultaneously with a single simple model. This software is free for use under the MIT license.
Where can I find kevlar online?
- Source repository: https://github.com/dib-lab/kevlar
- Documentation: https://kevlar.readthedocs.io
- Stable releases: https://github.com/dib-lab/kevlar/releases
- Issue tracker: https://github.com/dib-lab/kevlar/issues
If you have questions or need help with kevlar, the GitHub issue tracker should be your first point of contact.
How do I install kevlar?
See the kevlar documentation for complete instructions, but the impatient can try the following.
pip3 install git+https://github.com/dib-lab/khmer.git
pip3 install biokevlar
How do I use kevlar?
- Installation instructions: http://kevlar.readthedocs.io/en/latest/install.html
- Quick start guide: http://kevlar.readthedocs.io/en/latest/quick-start.html
- Tutorial: http://kevlar.readthedocs.io/en/latest/tutorial.html
How can I contribute?
We welcome contributions to the kevlar project from the community! If you're interested in modifying kevlar or contributing to its ongoing development, feel free to send us a message or submit a pull request!
The kevlar software is a project of the Lab for Data Intensive Biology and the Computational Genomics Lab at UC Davis.
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