Metadata-Version: 2.1
Name: biokevlar
Version: 0.7
Summary: Reference-free variant discovery scalable to large eukaryotic genomes
Home-page: https://github.com/dib-lab/kevlar
Author: Daniel Standage
Author-email: daniel.standage@gmail.com
License: MIT
Description: [![kevlar build status][travisbadge]](https://travis-ci.org/dib-lab/kevlar)
        [![PyPI version][pypibadge]](https://pypi.python.org/pypi/biokevlar)
        [![Test coverage][codecovbadge]](https://codecov.io/github/dib-lab/kevlar)
        [![kevlar documentation][rtdbadge]](http://kevlar.readthedocs.io/en/latest/?badge=latest)
        [![Docker build status][dockerbadge]](https://quay.io/repository/dib-lab/kevlar)
        [![MIT licensed][licensebadge]](https://github.com/dib-lab/kevlar/blob/master/LICENSE)
        
        <img src="docs/_static/morpheus-kevlar.jpg" alt=" What if I told you we don't need alignments to find variants?" width="400px" />
        
        # 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.
        
        <details>
          <summary>Where can I find kevlar online?</summary>
          <ul>
            <li>Source repository: https://github.com/dib-lab/kevlar</li>
            <li>Documentation: https://kevlar.readthedocs.io</li>
            <li>Stable releases: https://github.com/dib-lab/kevlar/releases</li>
            <li>Issue tracker: https://github.com/dib-lab/kevlar/issues</li>
          </ul>
        
          If you have questions or need help with kevlar, the [GitHub issue tracker](https://github.com/dib-lab/kevlar) should be your first point of contact.
        </details>
        
        <details>
          <summary>How do I install kevlar?</summary>
        
          See [the kevlar documentation](http://kevlar.readthedocs.io/en/latest/install.html) for complete instructions, but the impatient can try the following.
        
          ```
          pip3 install git+https://github.com/dib-lab/khmer.git
          pip3 install biokevlar
          ```
        </details>
        
        <details>
          <summary>How do I use kevlar?</summary>
          <ul>
            <li>Installation instructions: http://kevlar.readthedocs.io/en/latest/install.html</li>
            <li>Quick start guide: http://kevlar.readthedocs.io/en/latest/quick-start.html</li>
            <li>Tutorial: http://kevlar.readthedocs.io/en/latest/tutorial.html</li>
          </ul>
        </details>
        
        <details>
          <summary>How can I contribute?</summary>
          
          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](http://ivory.idyll.org/lab/) and the [Computational Genomics Lab](http://www.hormozdiarilab.org/) at UC Davis.
        </details>
        
        
        [travisbadge]: https://img.shields.io/travis/dib-lab/kevlar.svg
        [pypibadge]: https://img.shields.io/pypi/v/biokevlar.svg
        [codecovbadge]: https://img.shields.io/codecov/c/github/dib-lab/kevlar.svg
        [rtdbadge]: https://readthedocs.org/projects/kevlar/badge/?version=latest&maxAge=900
        [dockerbadge]: https://quay.io/repository/dib-lab/kevlar/status
        [licensebadge]: https://img.shields.io/badge/license-MIT-blue.svg
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Description-Content-Type: text/markdown
