RiboAbacus: a model trained on polyribosome images predicts ribosome density and translational efficiency from mammalian transcriptomes

Fluctuations in mRNA levels only partially contribute to determine variations in mRNA availability for translation, producing the well-known poor correlation between transcriptome and proteome data. Recent advances in microscopy now enable researchers to obtain high resolution images of ribosomes on transcripts, providing precious snapshots of translation in vivo. Here we propose RiboAbacus, a mathematical model that for the first time incorporates imaging data in a predictive model of transcript-specific ribosome densities and translational efficiencies. RiboAbacus uses a mechanistic model of ribosome dynamics, enabling the quantification of the relative importance of different features (such as codon usage and the 5' ramp effect) in determining the accuracy of predictions. The model has been optimized in the human Hek-293 cell line to fit thousands of images of human polysomes obtained by atomic force microscopy, from which we could get a reference distribution of the number of ribosomes per mRNA with unmatched resolution. After validation, we applied RiboAbacus to three case studies of known transcriptome-proteome datasets for estimating the translational efficiencies, resulting in an increased correlation with corresponding proteomes. RiboAbacus is an intuitive tool that allows an immediate estimation of crucial translation properties for entire transcriptomes, based on easily obtainable transcript expression levels.