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

_images/methods.png

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.com