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    "info": {
        "author": "Han Fang",
        "author_email": "hanfang.cshl@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: GNU General Public License v2 (GPLv2)",
            "Operating System :: Unix",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Topic :: Scientific/Engineering :: Bio-Informatics"
        ],
        "description": "Getting Started\n###############\n\nThis document will show you how to install and run Scikit-ribo.\n\nWhat is Scikit-ribo\n-------------------\n\nScikit-ribo is an open-source software for accurate genome-wide A-site prediction and translation efficiency\ninference from Riboseq and RNAseq data.\n\nSource Code: https://github.com/hanfang/scikit-ribo\n\nIntroduction\n------------\n\nScikit-ribo has two major modules:\n\n- **Ribosome A-site location prediction** using random forest with recursive feature selection\n\n- **Translation efficiency inference** using a codon-lvel generalized linear model with ridge penalty\n\nA complete analysis with scikit-ribo has two major procedures:\n\n- The data pre-processing step to prepare the ORFs, codons for a genome: ``scikit-ribo-build.py``\n\n- The actual model training and fitting: ``scikit-ribo-run.py``\n\nDetailed workflow\n-----------------\n.. image:: /images/methods.png\n   :align: center\n   :scale: 75%\n\nInputs\n------\n- The alignment of Riboseq reads (bam)\n- Gene-level quantification of RNA-seq reads (from either Salmon or Kallisto)\n- A gene annotation file (gtf)\n- A reference genome for the model organism of interest (fasta)\n\n\nOutput\n------\n- Translation efficiency estimates for the genes\n- Translation elongation rate for 61 sense codons\n- Ribosome profile plots for each gene\n- Diagnostic plots of the models\n\n\nCite\n----\n\nFang et al, \"Scikit-ribo: Accurate inference and robust modelling of translation dynamics at codon resolution\" (Preprint coming up)\n\nContact\n-------\n\nHan Fang\n\nStony Brook University & Cold Spring Harbor Laboratory\n\nEmail: hanfang.cshl@gmail.comRequirement\n###########\n\nEnvironment\n-----------\n\n- Python3\n- Linux\n- Recommend setting up your environment with `Conda <https://conda.io/docs/index.html>`_\n\nDependencies\n------------\n\n- Command-line pacakges:\n\n+----------------+------------+\n| Python package | Version >= |\n+================+============+\n| bedtools       | 2.26.0     |\n+----------------+------------+\n\n- Python package:\n\n+----------------+------------+\n| Python package | Version >= |\n+================+============+\n| colorama       | 0.3.7      |\n+----------------+------------+\n| glmnet_py      |0.1.0b      |\n+----------------+------------+\n| gffutils       | 0.8.7.1    |\n+----------------+------------+\n| matplotlib     | 1.5.1      |\n+----------------+------------+\n| numpy          | 1.11.2     |\n+----------------+------------+\n| pandas         | 0.19.2     |\n+----------------+------------+\n| pybedtools     | 0.7.8      |\n+----------------+------------+\n| pyfiglet       | 0.7.5      |\n+----------------+------------+\n| pysam          | 0.9.1.4    |\n+----------------+------------+\n| scikit_learn   | 0.18       |\n+----------------+------------+\n| scipy          | 0.18.1     |\n+----------------+------------+\n| seaborn        | 0.7.0      |\n+----------------+------------+\n| termcolor      | 1.1.0      |\n+----------------+------------+\n\nNote: When using pip install scikit-ribo, all the following dependencies will be pulled and installed automatically.\n\nInstallation\n############\n\nOptions\n-------\nThere are three options to install Scikit-ribo.\n\n\n1. Install Scikit-ribo with pip::\n\n    pip install scikit-ribo\n\n2. Install Scikit-ribo with conda/biocodon::\n\n    Coming up\n\n3. Compile from source::\n\n    git clone https://github.com/hanfang/scikit-ribo.git\n    cd scikit-ribo\n    python setup.py install\n\nTest whether the installation is successful\n-------------------------------------------\nOnce the installation is successful, you should expect the below if you type::\n\n    scikit-ribo-run.py\n\n.. image:: /images/successful_installation.png\n   :align: center\n   :scale: 75%\n\n",
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        "keywords": "bioinformatics genomics glm glmnet ridge riboseq",
        "license": "GPLv2",
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        "name": "scikit-ribo",
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        "summary": "A scikit framework for joint analysis of Riboseq and RNAseq data",
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