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        "author": "Haibao Tang, Smriti Ramakrishnan",
        "author_email": "htang@humanlongevity.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: BSD License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
            "Topic :: Scientific/Engineering :: Bio-Informatics"
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        "description": "TREDPARSE: HLI Short Tandem Repeat (STR) caller\n===============================================\n\n|Latest PyPI version| |Travis-CI|\n\n+-----------+---------------------------------------------------------------+\n| Author    | Haibao Tang (`tanghaibao <http://github.com/tanghaibao>`__)   |\n+-----------+---------------------------------------------------------------+\n|           | Smriti Ramakrishnan (`smr18 <http://github.com/smr18>`__)     |\n+-----------+---------------------------------------------------------------+\n| Email     | htang@humanlongevity.com                                      |\n+-----------+---------------------------------------------------------------+\n| License   | See included LICENSE                                          |\n+-----------+---------------------------------------------------------------+\n\nDescription\n-----------\n\nProcess a list of TRED (trinucleotide repeats disease) loci, and infer\nthe most likely genotype.\n\nInstallation\n------------\n\nMake sure your Python version >= 2.7 (tested in ubuntu, Python 3 not yet\nsupported):\n\n.. code:: bash\n\n    pip install --user -U git+git://github.com/humanlongevity/tredparse.git\n\nFor accessing BAMs that are located on S3, please refer to\n``docker/tredparse.dockerfile`` for installation of SAMTOOLS/pysam with\nS3 support.\n\nOr, you can simply build and use the docker image:\n\n.. code:: bash\n\n    docker pull humanlongevity/tredparse\n    docker run -v `pwd`:`pwd` -w `pwd` humanlongevity/tredparse \\\n        tred.py --tred HD test.bam\n\nExample\n-------\n\nFirst specify the input bam paths and sample keys in a CSV file, like\n``tests/samples.csv``. This file is comma separated:\n\n::\n\n    #SampleKey,BAM,TRED\n    t001,tests/t001.bam,HD\n    t002,tests/t002.bam,DM1\n\nIf third column is omitted, then all 30 TREDs are scanned. For example:\n\n::\n\n    #SampleKey,BAM\n    t001,tests/t001.bam\n    t002,tests/t002.bam\n\nPlease also note that the BAM path can start with ``http://`` or\n``s3://``, provided that the corresponding BAM index can be found.\n\nRun ``tred.py`` on sample CSV file and generate TSV file with the\ngenotype:\n\n.. code:: bash\n\n    tred.py tests/samples.csv --workdir work\n\nHighlight the potential risk individuals:\n\n.. code:: bash\n\n    tredreport.py work/*.json --tsv work.tsv\n\nThe inferred \"at-risk\" individuals show up in results:\n\n.. code:: bash\n\n    [DM1] - Myotonic dystrophy 1\n    rep=CAG inherit=AD cutoff=50 n_risk=1 n_carrier=0 loc=chr19:45770205-45770264\n    SampleKey inferredGender Calls DM1.FR                          DM1.PR     DM1.RR  DM1.PP\n         t002        Unknown  5|62   5|24  ...|1;39|1;40|1;42|1;43|1;46|2  49|3;50|8       1\n\n    [HD] - Huntington disease\n    rep=CAG inherit=AD cutoff=40 n_risk=1 n_carrier=0 loc=chr4:3074877-3074933\n    SampleKey inferredGender  Calls HD.FR                           HD.PR HD.RR  HD.PP\n         t001        Unknown  15|41  15|4  ...|1;21|1;24|2;29|1;34|1;41|1            1\n\nOne particular individual ``t001`` appears to have ``15/41`` call (one\nallele at ``15`` CAGs and the other at ``41`` CAGs) at Huntington\ndisease locus (HD). Since the risk cutoff is ``40``, we have inferred it\nto be at-risk.\n\nA ``.report.txt`` file will also be generated that contains a summary of\nnumber of people affected by over-expanded TREDs as well as population\nallele frequency.\n\nTo better understand the uncertainties in the prediction, we can plot\nthe likelihood surface based on the model. Using the same example as\nabove at the Huntington disease case, we can run a command on the JSON\noutput, with option ``--tred HD`` to specify the locus.\n\n.. code:: bash\n\n    tredplot.py likelihood work/t001.json --tred HD\n\nThis generates the following plot:\n\n.. figure:: https://www.dropbox.com/s/2mmfjjpnmcl4jlo/likelihood2.png?raw=1\n   :alt: \n\nServer demo\n-----------\n\nThe server/client allows ``tredparse`` to be run as a service, also\nshowing the detailed debug information for the detailed computation.\n\n.. figure:: https://www.dropbox.com/s/23tmoy0wtb3alwh/screencast.gif?raw=1\n   :alt: \n\nInstall ``meteor`` if you don't have it yet.\n\n.. code:: bash\n\n    curl https://install.meteor.com/ | sh\n\nThen build the docker image to run the command, then run the server.\n\n.. code:: bash\n\n    cd docker\n    make build\n    cd ../server\n    meteor npm install\n    meteor\n\n.. |Latest PyPI version| image:: https://img.shields.io/pypi/v/tredparse.svg\n   :target: https://pypi.python.org/pypi/tredparse\n.. |Travis-CI| image:: https://travis-ci.org/humanlongevity/tredparse.svg?branch=master\n   :target: https://travis-ci.org/humanlongevity/tredparse\n",
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