Point-Wise Ribosome Translation Speed Prediction with Recurrent Neural Networks

Abstract

Escherichia coli is a benchmark organism, which has been deeply studied by the scientific community for decades, obtaining a vast amount of metabolic and genetic data. Among these data, estimates of the translation speed of ribosomes over their genome are available. These estimates are based on Ribo-Seq profiles, where the abundance of a particular fragment of mRNA in a profile indicates that it was sampled many times inside a cell. Various measurements of Ribo-Seq profiles are available for Escherichia coli, yet they do not always show a high degree of correspondence, which means that they can vary significantly in different experimental setups, being characterized by poor reproducibility. Indeed, within Ribo-Seq profiles, the translation speed for some sequences is easier to estimate, while for others, an uneven distribution of consensus among the different estimates is evidenced. Our goal is to develop an artificial intelligence method that can be trained on a small pool of highly reproducible sequences to establish their translation rate, which can then be exploited to calculate a more reliable estimate of the translation speed on the rest of the genome.