Abstract
MotivationDeep 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.ResultsWe 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.Availability and ImplementationRiboDiff webpage http://bioweb.me/ribodiff. Source code including scripts for preprocessing the FASTQ data are available at http://github.com/ratschlab/ribodiff.Supplementary information Supplementary data are available at Bioinformatics online.
Highlights
The recently described ribosome footprinting technology (Ingolia et al, 2012) allows the identification of mRNA fragments that were protected by the ribosome
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
We model the vector of RNA-Seq and RF read counts yimRNA and yiRF, respectively, for gene i with Negative Binomial (NB) distributions, as described before: yi NBðli; jiÞ; where li is the expected count and ji is the estimated dispersion across biological replicates
Summary
The recently described ribosome footprinting technology (Ingolia et al, 2012) allows the identification of mRNA fragments that were protected by the ribosome It provides valuable information on ribosome occupancy and, thereby indirectly, on protein synthesis activity. This technology can be leveraged by combining the measurements from RNA-Seq estimates in order to determine a gene’s translation efficiency (TE), which is the ratio of the abundances of translated mRNA and available mRNA (Ingolia et al, 2011). Thoreen et al (2012) considered a ratio (fold-change) of the TEs of treatment and control What these initial approaches only take into account partially is that one typically only obtains uncertain estimates of the mRNA and ribosome abundance.
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