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        "author": "Michael S. Noble, Timothy DeFreitas, David Heiman",
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        "description": "# gdctools\nPython and UNIX CLI utilities to simplify search and retrieval of open-access data from the [NIH/NCI Genomics Data Commons](https://gdc.cancer.gov/), and automate tasks that are common to most data-driven science projects.   For more information and examples see the [pictorial overview](https://docs.google.com/viewer?url=https://github.com/broadinstitute/gdctools/files/825892/GDCtools-overview.pdf), [Wiki pages](https://github.com/broadinstitute/gdctools/wiki) or [tests/Makefile](tests/Makefile).  To get started from a Unix command line, simply `pip install gdctools` or clone the repo and install:\n```\n    %  git clone https://github.com/broadinstitute/gdctools\n    %  cd gdctools\n    %  make install\n```\nThis should take only a minute or two, and may install [requests](http://docs.python-requests.org/en/master/), [fasteners](https://github.com/harlowja/fasteners) or [matplotlib](http://matplotlib.org/) dependencies.  *Note that if you are installing to a protected location you may need to preface the `make install` command with `sudo.`  After this you should be able to easily [mirror](https://github.com/broadinstitute/gdctools/wiki/GDC-Mirror) either [harmonized](https://gdc.cancer.gov/about-data/gdc-data-harmonization) or [legacy](https://gdc-portal.nci.nih.gov/legacy-archive) data directly from the command line \n```\n    gdc_mirror --config tests/tcgaSmoketest.cfg\n```\n(this is what the `make test` target does), even for a single patient case\n```\n    gdc_mirror --cases TCGA-EE-A3J8\n```\nor just one category of data for that patient\n```\n    gdc_mirror --cases TCGA-EE-A3J8 --categories \"Copy Number Variation\"\n```\nor perform other operations such as seeing which NIH/NCI programs have exposed data for download\n```\n    %  gdc_list programs\n    [\n      \"TCGA\", \n      \"TARGET\"\n    ]\n```\nor what programs have submitted data (that may not be exposed yet)\n```\n    %  gdc_list submission\n    [\n      \"CCLE\", \n      \"REBC\", \n      \"TCGA\", \n      \"TARGET\", \n      \"CGCI\", \n      \"CDDP\", \n      \"ALCHEMIST\", \n      \"GDC\", \n      \"Exceptional_Responders\", \n      \"UAT08\", \n      \"TRIO\", \n      \"CPTAC\"\n    ]\n```\nAfter mirroring you may run [gdc_dice](https://github.com/broadinstitute/gdctools/wiki/GDC-Dicer) on the mirror tree, followed by [gdc_loadfile](https://github.com/broadinstitute/gdctools/wiki/Create-Loadfile) to generate a sample \"freeze\" list which identifies the data for loading into pipeline execution systems like Firehose or FireCloud.  Finally, if you have *matplotlib* and *R* installed you may also run the [gdc_report](https://github.com/broadinstitute/gdctools/wiki/Sample-reports) tool to generate an HTML samples report ([similar to this](http://gdac.broadinstitute.org/runs/sampleReports/latest/)) that provides an annotated description of the processed data; note that this tool will attempt to automatically install [Nozzle](https://confluence.broadinstitute.org/display/GDAC/Nozzle) if it is not detected within the R installation. As noted earlier, the [tests/Makefile](tests/Makefile) provides examples of using the dice, loadfile and report tools.  GDCtools has been verified to function properly with multiple Python2 and Python3 versions, and we are [grateful for the community contributions](https://github.com/broadinstitute/gdctools/commit/53be8ee4d720b502c2dbb1e110e7c20754331e3e) in support of this goal.\n",
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