{
    "info": {
        "author": "John Lees and Nicholas Croucher",
        "author_email": "john@johnlees.me",
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        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: Apache Software License",
            "Programming Language :: Python :: 3.7",
            "Topic :: Scientific/Engineering :: Bio-Informatics"
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        "description": "# PopPUNK (POPulation Partitioning Using Nucleotide Kmers)\n\n[![Dev build Status](https://dev.azure.com/jlees/PopPUNK/_apis/build/status/johnlees.PopPUNK?branchName=master)](https://dev.azure.com/jlees/PopPUNK/_build/latest?definitionId=1&branchName=master)\n[![Stable build Status](https://travis-ci.org/johnlees/PopPUNK.svg?branch=v1.1.6)](https://travis-ci.org/johnlees/PopPUNK/)\n[![Documentation Status](https://readthedocs.org/projects/poppunk/badge/?version=latest)](https://poppunk.readthedocs.io/)\n[![PyPI version](https://badge.fury.io/py/poppunk.svg)](https://badge.fury.io/py/poppunk)\n[![Anaconda package](https://anaconda.org/bioconda/poppunk/badges/version.svg)](https://anaconda.org/bioconda/poppunk)\n\nStep 1) Use k-mers to calculate core and accessory distances\n\nStep 2) Use core and accessory distance distribution to define strains\n\nStep 3) Pick references from strains, which can be used to assign new\nquery sequences\n\nSee the [documentation](http://poppunk.readthedocs.io/en/latest/) and the\n[paper](https://doi.org/10.1101/gr.241455.118).\n\nIf you find PopPUNK useful, please cite as:\n\nLees JA, Harris SR, Tonkin-Hill G, Gladstone RA, Lo SW, Weiser JN, Corander J, Bentley SD, Croucher NJ.\nFast and flexible bacterial genomic epidemiology with PopPUNK. *Genome Research* **29**:304-316 (2019).\ndoi:[10.1101/gr.241455.118](https://doi.org/10.1101/gr.241455.118)\n\n## Installation\n\n### Through conda (recommended)\n\nThe easiest way is through conda, which is most easily accessed by first\ninstalling [miniconda](https://conda.io/miniconda.html). PopPUNK can then\nbe installed by running:\n```\nconda install poppunk\n```\nIf the package cannot be found you will need to add the necessary channels:\n```\nconda config --add channels defaults\nconda config --add channels bioconda\nconda config --add channels conda-forge\n```\n\n### Through pip\nIf you do not have conda you can also install through pip:\n```\npython3 -m pip install poppunk\n```\nYou will need to be using Python 3.\n\nUsing both of these methods command `poppunk` will then be directly executable.\nAlternatively clone this repository:\n```\ngit clone git@github.com:johnlees/PopPUNK.git\n```\nThen run with `python poppunk-runner.py`.\n\n## Quick usage\nEasy run mode, go from assemblies to clusters of strains:\n```\npoppunk --easy-run --r-files reference_list.txt --output poppunk_db\n```\n\nOr in two parts. First, create the database:\n```\npoppunk --create-db \\\n   --r-files reference_list.txt \\\n   --output poppunk_db \\\n   --threads 2 \\\n   --k-step 2 \\\n   --min-k 9 \\\n   --plot-fit 5\n```\n\nThen fit the model:\n```\npoppunk --fit-model \\\n   --ref-db poppunk_db \\\n   --distances poppunk_db/poppunk_db.dists \\\n   --output poppunk_db \\\n   --full-db \\\n   --K 2\n```\n\nOnce fitted, new query sequences can quickly be assigned:\n```\npoppunk --assign-query \\\n   --ref-db poppunk_db \\\n   --q-files query_list.txt \\\n   --output query_results \\\n   --update-db\n```\n\nIf running without having installed through conda or pip,\nrun `python poppunk-runner.py` instead of `poppunk`.\n\nSee the [documentation](http://poppunk.readthedocs.io/en/latest/) for\nfull details.\n\n## Other installation notes\n\nInstalling via conda takes care of all dependencies. For other methods you will\nneed to install the packages listed under dependencies.\n\nIf you wish to use the [/scripts](https://poppunk.readthedocs.io/en/latest/scripts.html)\nyou will need to clone the github.\n\n### Dependencies\n\nYou will need a [mash](http://mash.readthedocs.io/en/latest/) installation\nwhich is v2.0 or higher.\n\nThe following python packages are required, which can be installed\nthrough `pip`. In brackets are the versions we used:\n\n* python3\n* `DendroPy` (4.3.0)\n* `hdbscan` (0.8.13)\n* `matplotlib` (2.1.2)\n* `networkx` (2.1)\n* `numba` (0.36.2)\n* `numpy` (1.14.1)\n* `pandas` (0.22.0)\n* `scikit-learn` (0.19.1)\n* `scipy` (1.0.0)\n* `sharedmem` (0.3.5)\n\nTo install `numba` through pip, you may need `gcc >=v4.8` for it to work correctly.\n\n### Test installation\nRun the following command:\n```\ncd test && python run_test.py\n```\n\nIf successful, you can clean the test data by running:\n```\ncd test && python clean_test.py\n```",
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