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        "author": "Ryan Dale",
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            "Development Status :: 4 - Beta",
            "Environment :: Console",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "Intended Audience :: System Administrators",
            "License :: OSI Approved :: MIT License",
            "Operating System :: MacOS :: MacOS X",
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            "Topic :: Scientific/Engineering :: Bio-Informatics",
            "Topic :: Scientific/Engineering :: Medical Science Apps."
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        "description": "Metaseq\n=======\n.. image:: https://travis-ci.org/daler/metaseq.png?branch=master\n    :target: https://travis-ci.org/daler/metaseq\n\n.. image:: https://badge.fury.io/py/metaseq.svg\n    :target: http://badge.fury.io/py/metaseq\n\n.. image:: https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat-square\n    :target: http://bioconda.github.io\n\nBriefly, the goal of `metaseq` is to tie together lots of existing software into\na framework for exploring genomic data.  It focuses on flexibility and\ninteractive exploration and plotting of disparate genomic data sets.\n\nThe main documentation for `metaseq` can be found at https://daler.github.io/metaseq.\n\nIf you use `metaseq` in your work, please cite the following publication:\n\n    Dale, R. K., Matzat, L. H. & Lei, E. P. metaseq: a Python package for\n    integrative genome-wide analysis reveals relationships between chromatin\n    insulators and associated nuclear mRNA. Nucleic Acids Res. 42, 9158\u20139170\n    (2014). http://www.ncbi.nlm.nih.gov/pubmed/25063299\n\n\nExample 1: Average ChIP-seq signal over promoters\n-------------------------------------------------\n\n`Example 1 <https://daler.github.io/metaseq/example_session.html>`_ walks you\nthrough the creation of the following heatmap and line-plot figure:\n\n.. figure:: demo.png\n\n    Top: Heatmap of ATF3 ChIP-seq signal over transcription start sites (TSS) on\n    chr17 in human K562 cells.  Middle: average ChIP enrichment over all TSSs\n    +/- 1kb, with 95% CI band.  Bottom: Integration with ATF3 knockdown RNA-seq\n    results, showing differential enrichment over transcripts that went up,\n    down, or were unchanged upon ATF3 knockdown.\n\nExample 2: Differential expression scatterplots\n-----------------------------------------------\n\n`Example 2 <https://daler.github.io/metaseq/example_session_2.html>`_ walks\nyou through the creation of the following scatterplot and marginal histogram\nfigure:\n\n\n.. figure:: expression-demo.png\n\n    Control vs knockdown expression (log2(FPKM + 1)) for an ATF3 knockdown\n    experiment.  Each point represents one transcript on chromosome 17.\n    Marginal distributions are shown on top and side.  1:1 line shown as\n    a dotted line.  Up- and downregulated genes determined by a simple 2-fold\n    cutoff.\n\nOther features\n--------------\nIn addition, `metaseq` offers:\n\n* A format-agnostic API for accessing \"genomic signal\" that allows you to work\n  with BAM, BED, VCF, GTF, GFF, bigBed, and bigWig using the same API.\n\n* Parallel data access from the file formats mentioned above\n\n* \"Mini-browsers\", zoomable and pannable Python-only  figures that show genomic\n  signal and gene models and are spawned by clicking on features of interest\n\n* A wrapper around pandas.DataFrames to simplify the manipulation and plotting\n  of tabular results data that contain gene information (like DESeq results\n  tables)\n\n* Integrates data keyed by genomic interval (think BAM or BED files) with data\n  keyed by gene ID (e.g., Cufflinks or DESeq results tables)\n\nCheck out the `full documentation <https://daler.github.io/metaseq/>`_ for\nmore.\n",
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