Metadata-Version: 1.1
Name: tredparse
Version: 0.7.7
Summary: Short Tandem Repeat (STR) genotyper
Home-page: https://github.com/tanghaibao/tredparse
Author: Haibao Tang, Smriti Ramakrishnan
Author-email: htang@humanlongevity.com
License: HLI open source license
Description: TREDPARSE: HLI Short Tandem Repeat (STR) caller
        ===============================================
        
        |Travis-CI|
        
        +-----------+---------------------------------------------------------------+
        | Author    | Haibao Tang (`tanghaibao <http://github.com/tanghaibao>`__)   |
        +-----------+---------------------------------------------------------------+
        |           | Smriti Ramakrishnan (`smr18 <http://github.com/smr18>`__)     |
        +-----------+---------------------------------------------------------------+
        | Email     | htang@humanlongevity.com                                      |
        +-----------+---------------------------------------------------------------+
        | License   | See included LICENSE                                          |
        +-----------+---------------------------------------------------------------+
        
        Description
        -----------
        
        Process a list of TRED (trinucleotide repeats disease) loci, and infer
        the most likely genotype.
        
        Installation
        ------------
        
        Make sure your Python version >= 2.7 (tested in ubuntu, Python 3 not yet
        supported):
        
        .. code:: bash
        
            pip install --user -U git+git://github.com/humanlongevity/tredparse.git
        
        For accessing BAMs that are located on S3, please refer to
        ``docker/tredparse.dockerfile`` for installation of SAMTOOLS/pysam with
        S3 support.
        
        Or, you can simply build and use the docker image:
        
        .. code:: bash
        
            docker pull humanlongevity/tredparse
            docker run -v `pwd`:`pwd` -w `pwd` humanlongevity/tredparse \
                tred.py --tred HD test.bam
        
        Example
        -------
        
        First specify the input bam paths and sample keys in a CSV file, like
        ``tests/samples.csv``. This file is comma separated:
        
        ::
        
            #SampleKey,BAM,TRED
            t001,tests/t001.bam,HD
            t002,tests/t002.bam,DM1
        
        If third column is omitted, then all 30 TREDs are scanned. For example:
        
        ::
        
            #SampleKey,BAM
            t001,tests/t001.bam
            t002,tests/t002.bam
        
        Please also note that the BAM path can start with ``http://`` or
        ``s3://``, provided that the corresponding BAM index can be found.
        
        Run ``tred.py`` on sample CSV file and generate TSV file with the
        genotype:
        
        .. code:: bash
        
            tred.py tests/samples.csv --workdir work
        
        Highlight the potential risk individuals:
        
        .. code:: bash
        
            tredreport.py work/*.json --tsv work.tsv
        
        The inferred "at-risk" individuals show up in results:
        
        .. code:: bash
        
            [DM1] - Myotonic dystrophy 1
            rep=CAG inherit=AD cutoff=50 n_risk=1 n_carrier=0 loc=chr19:45770205-45770264
            SampleKey inferredGender Calls DM1.FR                          DM1.PR     DM1.RR  DM1.PP
                 t002        Unknown  5|62   5|24  ...|1;39|1;40|1;42|1;43|1;46|2  49|3;50|8       1
        
            [HD] - Huntington disease
            rep=CAG inherit=AD cutoff=40 n_risk=1 n_carrier=0 loc=chr4:3074877-3074933
            SampleKey inferredGender  Calls HD.FR                           HD.PR HD.RR  HD.PP
                 t001        Unknown  15|41  15|4  ...|1;21|1;24|2;29|1;34|1;41|1            1
        
        One particular individual ``t001`` appears to have ``15/41`` call (one
        allele at ``15`` CAGs and the other at ``41`` CAGs) at Huntington
        disease locus (HD). Since the risk cutoff is ``40``, we have inferred it
        to be at-risk.
        
        A ``.report.txt`` file will also be generated that contains a summary of
        number of people affected by over-expanded TREDs as well as population
        allele frequency.
        
        To better understand the uncertainties in the prediction, we can plot
        the likelihood surface based on the model. Using the same example as
        above at the Huntington disease case, we can run a command on the JSON
        output, with option ``--tred HD`` to specify the locus.
        
        .. code:: bash
        
            tredplot.py likelihood work/t001.json --tred HD
        
        This generates the following plot:
        
        .. figure:: https://www.dropbox.com/s/2mmfjjpnmcl4jlo/likelihood2.png?raw=1
           :alt: 
        
        Server demo
        -----------
        
        The server/client allows ``tredparse`` to be run as a service, also
        showing the detailed debug information for the detailed computation.
        
        .. figure:: https://www.dropbox.com/s/23tmoy0wtb3alwh/screencast.gif?raw=1
           :alt: 
        
        Install ``meteor`` if you don't have it yet.
        
        .. code:: bash
        
            curl https://install.meteor.com/ | sh
        
        Then build the docker image to run the command, then run the server.
        
        .. code:: bash
        
            cd docker
            make build
            cd ../server
            meteor npm install
            meteor
        
        .. |Travis-CI| image:: https://travis-ci.org/humanlongevity/tredparse.svg?branch=master
           :target: https://travis-ci.org/humanlongevity/tredparse
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
