Deep sequencing based ribosome footprint profiling can provide novel insights into the regulatory mechanisms of protein translation. However, the observed ribosome profile is fundamentally confounded by transcriptional activity. In order to decipher principles of translation regulation, tools that can reliably detect changes in translation efficiency in case-control studies are needed.
Researchers from the Sloan Kettering Institute present a statistical framework and an analysis tool, RiboDiff, to detect genes with changes in translation efficiency across experimental treatments. RiboDiff uses generalized linear models to estimate the over-dispersion of RNA-Seq and ribosome profiling measurements separately, and performs a statistical test for differential translation efficiency using both mRNA abundance and ribosome occupancy.
(A) Graphical model representing RidoDiff (Gray circle: observable variables; empty circle: unobservable variables; black square: functions; r denotes biological replicates; i denotes a gene and G is the number of genes). The dashed line denotes the relationship that we aim to test (see Methods for details). (B) Receiver operating characteristic (ROC) curve of RiboDiff (with separate dispersions), edgeR and DESeq2 (with interaction model), Z-score method and Babel on simulated data with large difference between dispersions of RF and RNA-Seq counts (see also Supplementary Figure S-4). (C) Comparison of the distribution of TE ratios of genes that were detected to have a significant change in translation efficiency by RiboDiff (w/ joint dispersion), Z-score based analysis and Babel. DESeq2 was very similar to RiboDiff (w/ joint dispersion) and was omitted. Data was taken from GEO accession GSE56887.
Availability – Source code including scripts for preprocessing the FASTQ data are available at http://github.com/ratschlab/ribodiff