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Reference-free variant discovery scalable to large eukaryotic genomes

Project description

kevlar build status PyPI version Test coverage kevlar documentation Docker build status MIT licensed

 What if I told you we don't need alignments to find variants?

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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