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        "author": "David Koslicki",
        "author_email": "dmkoslicki@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: BSD License",
            "Natural Language :: English",
            "Operating System :: MacOS :: MacOS X",
            "Operating System :: POSIX :: Linux",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.5",
            "Topic :: Scientific/Engineering :: Bio-Informatics",
            "Topic :: Scientific/Engineering :: Mathematics"
        ],
        "description": "# CMash\nCMash is a fast and accurate way to estimate the similarity of two sets. This is a probabilisitic data analysis approach, and uses containment min hashing. Please see the [associated paper](http://www.biorxiv.org/content/early/2017/09/04/184150) for further details (and please cite if you use it):\n>Improving Min Hash via the Containment Index with applications to Metagenomic Analysis\n>David Koslicki, Hooman Zabeti\n>bioRxiv 184150; doi: https://doi.org/10.1101/184150\n\n## Installation\nThe easiest way to install this is to use [virtualenv](https://virtualenv.pypa.io/en/stable/):\n```bash\nvirtualenv CMashVE\nsource CMashVE/bin/activate\npip install -U pip\npip install CMash\n```\nYou can also just use ``pip install CMash`` if you don't want to create a virtual environment.\n\nTo get the absolute latest edition of CMash, then you can build from the Github repository via:\n```bash\nvirtualenv CMashVE\nsource CMashVE/bin/activate\npip install -U pip\ngit clone https://github.com/dkoslicki/CMash.git\ncd CMash\npip install -r requirements.txt\n```\n\nNote that the python code in this repository is python2 and python3 compatible, but the dependency ``khmer`` technically requires python3 (but ``khmer`` version ``2.1.1`` runs just fine in python2.)\nThe external dependency ``pytst`` requires python2, so I'm making this a python2 repository.\n## Usage\nThe basic paradigm is to create a reference/training database, form a sample bloom filter, and then query the database.\n\n#### Forming a reference/training database\nSay you have three reference fasta/q file: ``ref1.fa``, ``ref2.fa`` and ``ref3.fa``. In a file (here called ``FileNames.txt``), place the absolute paths pointing to the fasta/q files:\n```bash\ncat FileNames.txt\n# /abs/path/to/ref1.fa\n# /abs/path/to/ref2.fa\n# /abs/path/to/ref3.fa\n```\nThen you can create the training database via:\n```bash\nMakeDNADatabase.py FileNames.txt TrainingDatabase.h5\n```\nSee ``MakeDNADatabase.py -h`` for more options when forming a database.\n\n#### Creating a sample bloom filter\nGiven a (large) query fasta/q file ``Metagenome.fa``, you can *optionally* create a bloom filter via ``MakeNodeGraph.py Metagenome.fa .``. \nSee ``MakeNodeGraph.py -h`` for more details about this function.\n\nThis step is not strictly necessary (as the next step automatically forms a nodegraph/bloom filter if you didn't already create one). \nHowever, I've provided this script in case you want to pre-process a bunch of metagenomes.\n\n#### Query the database\nTo get containment and Jaccard index estimates of the references files in your query file ``Metagenome.fa``, use something like the following ``QueryDNADatabase.py Metagenome.fa TrainingDatabase.h5 Output.csv``.\n\nThere are a bunch of options available: ``QueryDNADatabase.py -h``. The output file is a CSV file with rows corresponding (in this case) to ``ref1.fa``, ``ref2.fa``, and ``ref3.fa`` and columns corresponding to the containment index estimate, intersection cardinality, and Jaccard index estimate.\n\n#### Other functionality\nThe module ``MinHash`` (imported in python via ``from CMash import MinHash as MH``) has a bunch more functionality, including (but not limited to!):\n1. Fast updates to the training databases (via ``help(MH.delete_from_database)``, ``help(MH.insert_to_database)``, ``help(MH.union_databases)``)\n2. Ability to form a matrix of Jaccard indexes (for comparison of all pairwise Jaccard indexes of organisms in the training database). This is useful for identifying redundances/patterns/structure in your training database: ``help(MH.form_jaccard_count_matrix)`` and ``help(MH.form_jaccard_matrix)``.\n3. Access to the k-mers that MinHash randomly selected (see the class ``CountEstimator`` and the associated ``_kmers`` data structure.)\n\nI'd encourage you to poke through the source code of ``MinHash.py`` and take a look at the scripts as well.\n\nProtein databases (and for that matter, arbitrary K-length strings) coming soon...\n\n\n",
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        "keywords": "jaccard min hash containment genomics metagenomics",
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        "name": "CMash",
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