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        "author": "Ryan Koesterer",
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        "classifiers": [
            "Development Status :: 4 - Beta",
            "License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)",
            "Programming Language :: Python :: 2.7",
            "Topic :: Scientific/Engineering :: Bio-Informatics"
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        "description": "Universal Genome Analyst\n************************\n\n.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.578712.svg\n   :target: https://doi.org/10.5281/zenodo.578712\n\nUniversal Genome Analyst (**uga**) is an open, flexible, and efficient tool for the distribution, management, and visualization of whole genome data analyses. \nIt is designed to assist biomedical researchers in complex genomic data analysis through the use of a low level interface between the powerful R statistical \nenvironment and Python, allowing for rapid integration of emerging analytical strategies. This project uses `Cython`_ for a significant reduction in computation \ntime and researchers with access to a high performance computing cluster or with access to multiple cores will find time-saving features for parallel analysis \nusing a flexible, yet controlled, commandline interface.\n\n.. _`Cython`: https://pypi.python.org/pypi\n\nThis software is currently under rapid development. Updates and bug fixes are being tracked on the `uga github page`_\n\n.. _`uga github page`: https://github.com/rmkoesterer/uga\n\n**Notable Features**\n   - Single variant association modeling (R base: lm, glm; R `geepack`_: geeglm, R `seqMeta`_: singlesnpMeta, R `lme4`_: lmer)\n   - Gene/Group based association modeling (with meta analysis: R `seqMeta`_: burdenMeta, skatMeta, skatOMeta)\n   - Family based single variant association modeling\n   - Publication quality Q-Q and manhattan plots\n   - Genomic control correction\n   - Post modeling meta analysis\n   - Run multiple models as a single submission (variant names need not match)\n   - Alignment of compatible variants based on genomic position and both alleles (A/T and G/C SNVs are ambiguous and are assumed to be pre-aligned)\n   - Automatic job splitting (with job array queueing)\n   - Input data split by chromosome can be linked via wildcard\n   - Automatically submit jobs on parallel computing systems using `qsub`_\n   - multiple processor parallelization in addition to cluster parallelization\n   - User definable buffered reading for RAM usage control\n   - Verification and compilation for parallel distributed jobs\n   - `Gzip`_ and `Bgzip`_ / `Tabix`_ mapped output where possible to save disc space\n\n.. _`geepack`: https://cran.r-project.org/web/packages/geepack/index.html\n.. _`seqMeta`: https://cran.r-project.org/web/packages/seqMeta/index.html\n.. _`lme4`: https://cran.r-project.org/web/packages/lme4/index.html\n.. _`qsub`: http://gridscheduler.sourceforge.net/htmlman/htmlman1/qsub.html\n.. _`Gzip`: http://www.gzip.org/\n.. _`Bgzip`: http://www.htslib.org/\n.. _`Tabix`: http://www.htslib.org/\n\n**Planned For Future Releases**\n   - Full documentation\n   - Additional association models (R `survival`_: coxph; `nlme`_: lme)\n   - Family data inclusion in gene/group based tests\n   - Calculation for grouped analysis multiple test correction\n   - Region-based plots via `Locuszoom`_ software\n   - Results annotation using `SnpEff`_\n\n.. _`survival`: https://cran.r-project.org/web/packages/survival/index.html\n.. _`nlme`: https://cran.r-project.org/web/packages/nlme/index.html\n.. _`Locuszoom`: http://genome.sph.umich.edu/wiki/LocusZoom_Standalone\n.. _`SnpEff`: http://snpeff.sourceforge.net/\n\nSince parallel computing is sometimes unreliable, analysts need to be able to verify and possibly rerun failed jobs at various stages of the analysis.\nIn the interest of user efficiency and to avoid limitations induced by excessive automation, uga breaks the analytical process into the following modules.\n\n   - **settings** user definable settings\n   - **snv** single variant statistical modeling\n   - **snvgroup** gene/region-based statistical modeling\n   - **meta** meta-analysis\n   - **compile** verify and compile split analysis results\n   - **resubmit** automatically resubmit failed jobs for a project\n   - **snvplot** Q-Q and manhattan plots for snv tests\n   - **snvgroupplot** Q-Q and manhattan plots for snvgroup tests\n   - **filter** filter results / apply genomic control to results\n   - **merge** merge and annotate results with external files\n   - **tools** run any command line tool with ability to include genomic region automatically\n\nInstallation\n************\n\nThis software uses a variety of Python modules, R packages, and some stand-alone software. Thus, the easiest method for installation is to use one of two platforms of the \nsoftware `conda`_; either `Anaconda`_ or `Miniconda`_.\n\n.. _`conda`: https://conda.io/docs/download.html\n.. _`Anaconda`: https://www.continuum.io/downloads\n.. _`Miniconda`: https://conda.io/miniconda.html\n\nAlso, consolidation and compression of data and results files requires `tabix/bgzip`_ and `gzip`_.\n\n.. _`tabix/bgzip`: http://www.htslib.org/\n.. _`gzip`: http://www.gzip.org/\n\nTo prepare your system for uga, you need to `clone an environment`_. You will need the included environment.yml file from the source code and a number of \npackages from `my anaconda cloud channel`_ and other custom channels (listed in the environment.yml file). After downloading the most recent \nrelease (available `here`_), use the following commands to begin the installation.\n\n.. _`clone an environment`: http://conda.pydata.org/docs/using/envs.html#clone-an-environment\n.. _`my anaconda cloud channel`: https://conda.anaconda.org/rmkoesterer\n.. _`here`: https://github.com/rmkoesterer/uga/releases\n\nFor the sake of this tutorial, let's assume the release version is 'X'.\n\n   >>> tar -xvf uga-X.tar.gz\n   >>> cd uga-X\n\nAt this point you may change the name of the environment to anything you'd prefer by modifying the first line of the environment.yml file. For these instructions, we will \nassume the name is unchanged from 'uga'.\n\n   >>> conda env create -f environment.yml\n   >>> source activate uga\n\nNow that your environment is activated, you are ready to install uga from source.\n\n   >>> python setup.py install\n\n**Cutting Edge Install**\n\nKeeping up with the most current changes may be of interest to you as I will likely continue to add features and fix bugs on a regular basis. Thus, you may want to run a fork \nof this repository rather than installing from source. See a tutorial describing how to `fork this repository`_.\n\n.. _`fork this repository`: https://help.github.com/articles/fork-a-repo/\n\nGetting Started\n***************\n\nIf you install uga under a conda environment, you need to source the environment as shown above before running any task in uga.\n\n   >>> source activate uga\n\nVerify that uga is functional using the following command to display help.\n\n   >>> uga -h\n\nNote: further help is provided after selecting a specific module, ie.\n\n   >>> uga snv -h\n\n**Parallel computing**\n\nWhile you may simply run uga on a single cpu system, if you have access to a parallel computing cluster or even a single multiple core\nprocessor, you will be able to take advantage of the self-managed parallel mode of use for which this software was designed. \nThis release was tested on a system which deploys Sun Grid Engine and `qsub`_ for job management and will likely be compatible \nwith other PBS systems.\n\n.. _`qsub`: http://gridscheduler.sourceforge.net/htmlman/htmlman1/qsub.html\n\nCitation\n========\n\nPlease cite this software as follows. A manuscript is in the works and yet to be submitted.\n\nKoesterer, Ryan. Universal Genome Analyst (uga). https://github.com/rmkoesterer/uga. DOI: 10.5281/zenodo.578712.\n\nContact\n=======\n\n- **Author**: `Ryan Koesterer`_\n\n.. _`Ryan Koesterer`: https://github.com/rmkoesterer/uga\n\nPlease report any bugs or issues using the `Issues`_ tab on this page. I will respond to all concerns as quickly as possible.\n\n.. _`Issues`: https://github.com/rmkoesterer/uga/issues\n\nLicense\n=======\n\nUniversal Genome Analyst (uga) is distributed under the GNU General Public License v3:\n   \n   Copyright (c) 2015 Ryan Koesterer\n\n   This program is free software: you can redistribute it and/or\n   modify it under the terms of the GNU General Public License as\n   published by the Free Software Foundation, either version 3 of the\n   License, or (at your option) any later version.\n\n   This program is distributed in the hope that it will be useful, but\n   WITHOUT ANY WARRANTY; without even the implied warranty of\n   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU\n   General Public License for more details.\n\n   You should have received a copy of the GNU General Public License\n   along with this program.  If not, see\n   <http://www.gnu.org/licenses/>",
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