Metadata-Version: 2.1
Name: scikit-genome
Version: 0.0.1
Summary: A Python package for genomics
Home-page: https://github.com/scikit-genome/scikit-genome
Author: Allen Goodman
Author-email: allen.goodman@icloud.com
License: UNKNOWN
Project-URL: Bug Reports, https://github.com/scikit-genome/scikit-genome/issues
Project-URL: Source, https://github.com/scikit-genome/scikit-genome/
Platform: UNKNOWN
Classifier: Development Status :: 1 - Planning
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Requires-Python: >=3.7, <4
Description-Content-Type: text/markdown
Provides-Extra: build
Requires-Dist: twine (>=3.1.1) ; extra == 'build'
Provides-Extra: dev
Requires-Dist: black (>=19.10b0) ; extra == 'dev'
Requires-Dist: check-manifest (>=0.41) ; extra == 'dev'
Requires-Dist: pre-commit (>=2.2.0) ; extra == 'dev'
Provides-Extra: test
Requires-Dist: coverage (>=5.0.4) ; extra == 'test'
Requires-Dist: pytest (>=5.4.1) ; extra == 'test'

# scikit-genome

![test](https://github.com/scikit-genome/scikit-genome/workflows/test/badge.svg)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

_scikit-genome_ is a Python package for genomics.

* Essential, efficient algorithms for genomics
* Interoperable with Python’s scientific ecosystem 
* Free and open-source

## Frequently asked questions (FAQ)

### How do I pronounce “scikit-genome?”

*sī kit ˈjēˌnōm* is the preferred pronunciation.

### What’s a “scikit?”

scikit-genome is a SciPy toolkit. It’s compatible with both SciPy toolkits like scikit-learn and NumPy-compatible packages like PyTorch and TensorFlow.

### What’s the inclusion criteria for new algorithms?

Maintainers consider only well-established algorithms for inclusion. A minimum of three years since publication, more than 200 citations, and far-reaching usefulness is expected. Methods that provide clear-cut improvements on widely-used algorithms will also be considered.

Methods provided by one of the following maintained, general-purpose packages **won’t** be considered for inclusion:

* matplotlib
* NumPy
* Pandas
* PyTorch
* scikit-image
* scikit-learn
* SciPy
* statsmodels
* TensorFlow

### Do you support PyPy?

Yes


