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Your search keyword '"Valleriani, Angelo"' showing total 4 results
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4 results on '"Valleriani, Angelo"'

3. A neural networks approach for the analysis of reproducible ribo–seq profiles

Author

Giacomini, Giorgia, Graziani, Caterina, Lachi, Veronica, Bongini, Pietro, Pancino, Niccolò, Bianchini, Monica, Chiarugi, Davide, Valleriani, Angelo, Andreini, Paolo, Graziani, Caterina [0000-0002-7606-9405], Lachi, Veronica [0000-0002-6947-7304], Bongini, Pietro [0000-0001-9074-0587], Pancino, Niccolò [0000-0003-2212-4728], Chiarugi, Davide [0000-0003-2752-3189], and Apollo - University of Cambridge Repository

Subjects
Numerical Analysis, CNNs, Bioengineering, Ribo–seq profiling, neural networks, prediction of translation speed, ribosome dynamics, CNN, Theoretical Computer Science, Computational Mathematics, 46 Information and Computing Sciences, Computational Theory and Mathematics, FOS: Biological sciences, 49 Mathematical Sciences, Genetics, Ribo-seq profiling, Generic health relevance, 40 Engineering
Abstract

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. Based on deep sequencing of mRNA fragments, Ribo–seq allows to obtain profiles that reflect the time spent by ribosomes in translating each part of an open reading frame. Unfortunately, the profiles produced by this method can vary significantly in different experimental setups, being characterized by a poor reproducibility. To address this problem, we have employed a statistical method for the identification of highly reproducible Ribo–seq profiles, which was tested on a set of E. coli genes. State-of-the-art artificial neural network models have been used to validate the quality of the produced sequences. Moreover, new insights into the dynamics of ribosome translation have been provided through a statistical analysis on the obtained sequences.

Published
2022

4. A data-driven estimation of the ribosome drop-off rate in S. cerevisiae reveals a correlation with the genes length.

Author

Awad S, Valleriani A, and Chiarugi D

Abstract

Ribosomes are the molecular machinery that catalyse all the fundamental steps involved in the translation of mRNAs into proteins. Given the complexity of this process, the efficiency of protein synthesis depends on a large number of factors among which ribosome drop-off (i.e. the premature detachment of the ribosome from the mRNA template) plays an important role. However, an in vitro quantification of the extent to which ribosome drop-off occurs is not trivial due to difficulties in obtaining the needed experimental evidence. In this work we focus on the study of ribosome drop-off in Saccharomyces cerevisiae by using 'Ribofilio', a novel software tool that relies on a high sensitive strategy to estimate the ribosome drop-off rate from ribosome profiling data. Our results show that ribosome drop-off events occur at a significant rate also when S. cerevisiae is cultured in standard conditions. In this context, we also identified a correlation between the ribosome drop-off rate and the genes length: the longer the gene, the lower the drop-off rate., (© The Author(s) 2024. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.)

Published
2024
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