goodarzilab/Ribolog: Logistic-regression-based analysis of ribosome profiling data

Ribolog offers tools to perform a variety of analyses on ribosome profiling data. 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 (not-differentially translated transcripts) and correlation of equivalent TER tests. Module 4: TER tests the size and significance of differential translation rates among biological samples. Module 5: EN-Meta performs empirical null hypothesis testing and meta-analysis. 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 several attributes (covariates) can be compared in a single model. The Ribolog workflow is described in great detail in the package vignettes. Ribolog is still a work in progress. Other modules are being prepared and will be released in near future.

install.packages("remotes")
remotes::install_github("goodarzilab/Ribolog")