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        "description": "===============================================================\nDICEseq: Dynamic Isoform spliCing Estimator via sequencing data\n===============================================================\n\nAbout DICEseq\n=============\n\nDICEseq (Dynamic Isoform spliCing Estimator via sequencing data) estimates the \ndynamics of isoform proportions jointly from time series RNA-seq experiments. \nDICEseq is a Bayesian method based on a mixture model whose mixing proportions \nrepresent isoform ratios. It incorporates the correlations from the temporal \nstructure, by coupling the isoform proportions at different times through a \nlatent Gaussian process (GP).\n\nDICEseq provides following functions through command line:\n\n1. ``diceseq``: estimate the isoform proportions and FPKM for time series data \n   jointly, or for a single time point. \n\n2. ``dice-count``: fetch reads counts for entile gene, or specific reads counts,\n   e.g. junction reads, for genes with exact one intron. This is special design \n   mainly for yeast.\n\n3. ``dice-bias``: estimate parameters for sequencing bias, including fragment \n   length distribution, reads sequence and position bias parameter. The output \n   file can be directly used for bias correction in ``diceseq``.\n\nIn addition, DICEseq package also provides interface of a set of functions and \nattributes as an object-oriented python module. Therefore, you could use some \nof the module e.g., ``SampleFile`` to visualize the samples in gzip file in a \nGaussian process way, or ``BiasFile`` to visualize the bias parameters. Also, \nthe ``gtf_utils`` provides a set of ways to load gtf file, choose the genes, or \ncustomize the coordinates of exons and introns, add and remove of specific \ntranscripts.\n\n\nQuick Start\n===========\n\n**Environment and installation**:\n\nDICEseq was initially developed in **Python 2** environment, hence best to be used\nin Py2 environment. By using Anaconda_ platform, no matter Py2 or Py 3, it is \neasy to set up a conda_ environment with Py2, for example by following commond:\n\n.. code-block:: bash\n\n  conda create -n dicePy2 python=2.7 numpy==1.15.4 scipy==1.1.0 matplotlib==2.2.3 pysam==0.15.2\n\n  source activate dicePy2\n\nOnce you are in a Python 2 environment, there are usually two ways to isntall a\npackage: \n\n- ``pip install diceseq``\n- or download this repository, and type ``python setup.py install``. \n- You may need to add ``--user`` if you don't have the root permission for that \n  environment.\n\n.. _conda: https://conda.io/docs/user-guide/tasks/manage-environments.html\n.. _Anaconda: https://www.continuum.io/anaconda-overview\n\n**Arguments**\n\n- Type command line ``diceseq -h``\n\n\n\nDetailed Manual\n===============\n\nSee the documentation_ on how to install, to use, to find the annotation data \netc.\n\n.. _documentation: http://diceseq.sourceforge.net\n\n\nReferences\n===========\n\nYuanhua Huang and Guido Sanguinetti. `Statistical modeling of isoform splicing \ndynamics from RNA-seq time series data \n<http://bioinformatics.oxfordjournals.org/content/32/19/2965.abstract>`_. \n\\ **Bioinformatics**\\, 2016, 32(19): 2965-2972.",
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