Package: scikit-ribo
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 efficiencyinference from Riboseq and RNAseq data.Source Code: https://github.com/hanfang/scikit-riboIntroduction------------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 penaltyA 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 modelsCite----Fang et al, "Scikit-ribo: Accurate inference and robust modelling of translation dynamics at codon resolution" (Preprint coming up)Contact-------Han FangStony Brook University & Cold Spring Harbor LaboratoryEmail: 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-ribo2. Install Scikit-ribo with conda/biocodon::Coming up3. Compile from source::git clone https://github.com/hanfang/scikit-ribo.gitcd scikit-ribopython setup.py installTest 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%