Metadata-Version: 1.1
Name: tredparse
Version: 0.7.6
Summary: Short Tandem Repeat (STR) genotyper
Home-page: https://github.com/tanghaibao/tredparse
Author: Haibao Tang
Author-email: htang@humanlongevity.com
License: UNKNOWN
Description: # TREDPARSE: HLI Short Tandem Repeat (STR) caller
        
        [![Travis-CI](https://travis-ci.org/humanlongevity/tredparse.svg?branch=master)](https://travis-ci.org/humanlongevity/tredparse)
        
        | | |
        |---|---|
        | 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 &gt;= 2.7 (tested in ubuntu, Python 3 not yet
        supported):
        
        ```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:
        
        ```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:
        
        ```bash
        tred.py tests/samples.csv --workdir work
        ```
        
        Highlight the potential risk individuals:
        
        ```bash
        tredreport.py work/*.json --tsv work.tsv
        ```
        
        The inferred "at-risk" individuals show up in results:
        
        ```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, one call 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.
        
        ```bash
        tredplot.py likelihood work/t001.json --tred HD
        ```
        
        This generates the following plot:
        
        ![](https://www.dropbox.com/s/2mmfjjpnmcl4jlo/likelihood2.png?raw=1)
        
        ## Server demo
        
        The server/client allows `tredparse` to be run as a service, also showing the
        detailed debug information for the detailed computation.
        
        ![](https://www.dropbox.com/s/23tmoy0wtb3alwh/screencast.gif?raw=1)
        
        Install `meteor` if you don't have it yet.
        
        ```bash
        curl https://install.meteor.com/ | sh
        ```
        
        Then build the docker image to run the command, then run the server.
        
        ```bash
        cd docker
        make build
        cd ../server
        meteor npm install
        meteor
        ```
        
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
