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

Giacomini, G., Graziani, C., Lachi, V., Bongini, P., Pancino, N., Bianchini, M., et al. (2022). A neural networks approach for the analysis of reproducible ribo–seq profiles. Algorithms, 15(8): 274. doi:10.3390/a15080274.

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 Creators:
Giacomini, Giorgia, Author
Graziani, Caterina, Author
Lachi, Veronica, Author
Bongini, Pietro, Author
Pancino, Niccolò, Author
Bianchini, Monica, Author
Chiarugi, Davide, Author
Valleriani, Angelo1, Author           
Andreini, Paolo, Author
Affiliations:
1Angelo Valleriani, Theorie & Bio-Systeme, Max Planck Institute of Colloids and Interfaces, Max Planck Society, ou_1863324              

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Free keywords: Ribo–seq profiling; neural networks; prediction of translation speed; ribosome dynamics; CNN
 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.

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Language(s): eng - English
 Dates: 2022-08-042022
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.3390/a15080274
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Title: Algorithms
Source Genre: Journal
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Publ. Info: Basel : MDPI
Pages: - Volume / Issue: 15 (8) Sequence Number: 274 Start / End Page: - Identifier: ISSN: 1999-4893