Metadata-Version: 1.1
Name: scikit_ribo
Version: 0.2.4b1
Summary: A scikit framework for joint analysis of Riboseq and RNAseq data
Home-page: https://github.com/hanfang/scikit-ribo
Author: Han Fang
Author-email: hanfang.cshl@gmail.com
License: GPLv2
Description: Getting Started
        ###############
        
        This document will show you how to install and run Scikit-ribo.
        
        What is Scikit-ribo
        -------------------
        
        Scikit-ribo is an open-source software for accurate genome-wide A-site prediction and translation efficiency
        inference from Riboseq and RNAseq data.
        
        Source Code: https://github.com/hanfang/scikit-ribo
        
        Introduction
        ------------
        
        Scikit-ribo has two major modules:
        
        - **Ribosome A-site location prediction** using random forest with recursive feature selection
        
        - **Translation efficiency inference** using a codon-lvel generalized linear model with ridge penalty
        
        A complete analysis with scikit-ribo has two major procedures:
        
        - The data pre-processing step to prepare the ORFs, codons for a genome: ``scikit-ribo-build.py``
        
        - The actual model training and fitting: ``scikit-ribo-run.py``
        
        Detailed workflow
        -----------------
        .. image:: /images/methods.png
           :align: center
           :scale: 75%
        
        Inputs
        ------
        - The alignment of Riboseq reads (bam)
        - Gene-level quantification of RNA-seq reads (from either Salmon or Kallisto)
        - A gene annotation file (gtf)
        - A reference genome for the model organism of interest (fasta)
        
        
        Output
        ------
        - Translation efficiency estimates for the genes
        - Translation elongation rate for 61 sense codons
        - Ribosome profile plots for each gene
        - Diagnostic plots of the models
        
        
        Cite
        ----
        
        Fang et al, "Scikit-ribo: Accurate inference and robust modelling of translation dynamics at codon resolution" (Preprint coming up)
        
        Contact
        -------
        
        Han Fang
        
        Stony Brook University & Cold Spring Harbor Laboratory
        
        Email: hanfang.cshl@gmail.comRequirement
        ###########
        
        Environment
        -----------
        
        - Python3
        - Linux
        - Recommend setting up your environment with `Conda <https://conda.io/docs/index.html>`_
        
        Dependencies
        ------------
        
        - Command-line pacakges:
        
        +----------------+------------+
        | Python package | Version >= |
        +================+============+
        | bedtools       | 2.26.0     |
        +----------------+------------+
        
        - Python package:
        
        +----------------+------------+
        | Python package | Version >= |
        +================+============+
        | colorama       | 0.3.7      |
        +----------------+------------+
        | glmnet_py      |0.1.0b      |
        +----------------+------------+
        | gffutils       | 0.8.7.1    |
        +----------------+------------+
        | matplotlib     | 1.5.1      |
        +----------------+------------+
        | numpy          | 1.11.2     |
        +----------------+------------+
        | pandas         | 0.19.2     |
        +----------------+------------+
        | pybedtools     | 0.7.8      |
        +----------------+------------+
        | pyfiglet       | 0.7.5      |
        +----------------+------------+
        | pysam          | 0.9.1.4    |
        +----------------+------------+
        | scikit_learn   | 0.18       |
        +----------------+------------+
        | scipy          | 0.18.1     |
        +----------------+------------+
        | seaborn        | 0.7.0      |
        +----------------+------------+
        | termcolor      | 1.1.0      |
        +----------------+------------+
        
        Note: When using pip install scikit-ribo, all the following dependencies will be pulled and installed automatically.
        
        Installation
        ############
        
        Options
        -------
        There are three options to install Scikit-ribo.
        
        
        1. Install Scikit-ribo with pip::
        
            pip install scikit-ribo
        
        2. Install Scikit-ribo with conda/biocodon::
        
            Coming up
        
        3. Compile from source::
        
            git clone https://github.com/hanfang/scikit-ribo.git
            cd scikit-ribo
            python setup.py install
        
        Test whether the installation is successful
        -------------------------------------------
        Once the installation is successful, you should expect the below if you type::
        
            scikit-ribo-run.py
        
        .. image:: /images/successful_installation.png
           :align: center
           :scale: 75%
Keywords: bioinformatics genomics glm glmnet ridge riboseq
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: License :: OSI Approved :: GNU General Public License v2 (GPLv2)
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Operating System :: Unix
