pypi_readme.rst

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

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

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