# A Neural Network Approach for the Analysis of Reproducible Ribo-Seq Profiles
## 1 Introduction  
Ribosomes perform protein synthesis from mRNA templates by a highly regulated process called translation. Translation control plays a key role in the regulation of gene expression, both in physiological and pathological conditions [1]. The advent of high-throughput methods to measure the levels of gene expression has revealed the implications of multiple factors that might impact the rate at which an mRNA is translated. In recent years, the Ribosome profiling technique (Ribo-seq) has emerged as a powerful method for globally monitoring the translation process in vivo at single nucleotide resolution [2]. The application of this method to a different number of organisms subjected to different conditions, from the deprivation of nutrients in bacterial cells to the development of cancer in human cells, has allowed to investigate fundamental aspect of cell biology [3]. Interestingly, the nucleotide-level resolution of Ribo-seq experiments reveals the density of the ribosomes at each position along the mRNA template. Local differences in the density of Ribosome Protected Fragments (RPFs) along the Open Reading Frame (ORF) reflect differences in the speed of translation and elongation, determining regions where the translation is slower or faster. Figure 1 illustrates how the translation speed is not uniform, highlighting the differences in ribosome occupancy. This piece of information is well visible in Ribo-seq profiling data and can be used to infer how the codon usage, the protein sequences, and other features can regulate the speed of translation [4]. Unfortunately, the reproducibility of Ribo-seq experiments can be affected by multiple variables due to the complexity of the experimental protocol and the lack of standardization in computational data analysis [5]. Therefore, our work aims to overcome the aforementioned limitations by introducing a new statistical approach, designed to extract a set of highly reproducible profiles. In particular, inspired by the seminal work proposed in [6], we perform a novel analysis procedure for Ribo-seq data that allows to identify the reproducible Ribo-seq profiles emerging from the comparison of independent Ribo-seq experiments performed in different laboratories under the same conditions. These significantly reproducible profiles are then collected into a library of consensus sequences, in which sub-regions characterized by different translation speeds can be simulated. The aforementioned procedure has been applied to _E. coli_ sequences (_Escherichia Coli_ (E. coli) is a bacterium that lives in the lower intestine of warm-blooded animals and has a genome composed by approximately 4,600,000 base pairs. E. coli contains a total of 4288 genes, with coding sequences which are long, on average, 950 base pairs and separated, on average, from 118 bases. Considering the protein counterpart, in E. coli, the average length of a coding region is 316.8 codons, whereas less than 1.8% of the genes are shorter than 60 codons. ), resulting in 40 highly reproducible profiles. Differently from [6], based on the collected data, a statistical analysis has been carried out that gave new insights on the dynamics of the ribosome translation, showing a statistically significant difference in the nucleotide composition between sub-sequences characterized by different translation speeds. Moreover, to validate the procedure, the selected highly reproducible profiles have been analyzed through state-of-the-art Machine Learning (ML) models, accurately classifying subsequences according to their speed of translation (slow or fast).We have made our source code public available (https://github. Figure 1: Ribosome footprint density along the mRNA. The schematic distribution of translating ribosomes along the mRNA (**a**) and their ribosome profiles (**b**). Ribo–seq data show differences in the density of ribosomes: regions of fast elongation accumulate fewer ribosomes (low density) with respect to regions of slow elongation (high density).