Ribolog

A suite of regression-based tools for Ribosome profiling data analysis

Module 1: CELP (Consistent Excess of Loess Preds)

Identifies positions of translational pause (stalling) and corrects RPF counts to eliminate the impact of stalling bias. The output of CELP can be used to model the factors that influence translational dynamics.

Module 2: PREP

Normalizes and combines RNA and RPF datasets and shapes them into a format ready for quality control (QC) and translational efficiency ratio (TER) anlaysis.

Module 3: QC

Includes three powerful tools to quantify and visualize reproducibility among replicates and inform hypothesis generation with respect to biological effects: princiapl component analysis (PCA) of TEs, proportion of null features (non-differentially translated transcripts) and correlation of equivalent TER tests.

Module 4: TER

Tests the size and significance of differential translation rates among biological samples. Although better results are always obtained with sufficient replicates, Ribolog is able to peform the TER test with only one replicate per sample. The TER test is not restricted to pairwise comparisons; any number of samples described by a list of attributes (covariates) can be compared in a single model.

Module 5: Empirical significance testing and Meta-analysis

Offers tools for two important slightly advanced statistical tasks: 1) Empirical null hypothesis testing to reduce false positives in replicated datasets. 2) Meta-analysis to integrate results of biologically related and statistically correlated experiments.

The Ribolog workflow is described in great detail in the package vignettes (RIBOLOG.pdf in the vignettes folder).

Installing Ribolog

Step 1: Make sure to have all the necessary development tools installed on your system. Run these in the command line to install them:

Skip this step if you use Windows.

In Ubuntu (or computing servers):

sudo apt-get install build-essential libcurl4-gnutls-dev libxml2-dev libssl-dev libz-dev libbz2-dev liblzma-dev

In Linux

sudo yum groupinstall 'Development Tools'

In Mac (if you don't have development tools/ Xcode installed)

xcode-select --install

Step 2: Run the following commands in R:

install.packages('BiocManager')
BiocManager::install("Goodarzilab/Ribolog")

Install Ribolog with a Conda Environment

Run the code below in your terminal to build and activate a conda environment with all the dependencies of Ribolog inside it.

conda env create -f 'https://raw.githubusercontent.com/goodarzilab/Ribolog/master/environment.yml'
conda activate Ribolog # Now you're inside the conda environment
R -e "BiocManager::install('Goodarzilab/Ribolog', dependencies = FALSE)"

Rendering the vignettes during installation requires bam files that are not uploaded onto this repository. The knitted .pdf file should be downloaded directly from the vignettes folder instead.

Ribolog was developed by Hossein Asgharian at UCSF supervised by Hani Goodarzi and Adam Olshen. More modules are being prepared and will be released in near future.

For questions and comments, email us at: