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pdf:docinfo:title: A Neural Networks Approach for the Analysis of Reproducible Ribo?Seq Profiles 
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Keywords: Ribo?seq profiling; neural networks; prediction of translation speed; ribosome dynamics; CNN
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subject: 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. 
dc:creator: Giorgia Giacomini, Caterina Graziani, Veronica Lachi, Pietro Bongini, Niccolò Pancino, Monica Bianchini, Davide Chiarugi, Angelo Valleriani, Paolo Andreini
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title: A Neural Networks Approach for the Analysis of Reproducible Ribo?Seq Profiles 
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pdf:docinfo:keywords: Ribo?seq profiling; neural networks; prediction of translation speed; ribosome dynamics; CNN
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dc:title: A Neural Networks Approach for the Analysis of Reproducible Ribo?Seq Profiles 
modified: 2022-08-04T10:35:11Z
cp:subject: 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. 
pdf:docinfo:subject: 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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pdf:docinfo:creator: Giorgia Giacomini, Caterina Graziani, Veronica Lachi, Pietro Bongini, Niccolò Pancino, Monica Bianchini, Davide Chiarugi, Angelo Valleriani, Paolo Andreini
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creator: Giorgia Giacomini, Caterina Graziani, Veronica Lachi, Pietro Bongini, Niccolò Pancino, Monica Bianchini, Davide Chiarugi, Angelo Valleriani, Paolo Andreini
meta:author: Giorgia Giacomini, Caterina Graziani, Veronica Lachi, Pietro Bongini, Niccolò Pancino, Monica Bianchini, Davide Chiarugi, Angelo Valleriani, Paolo Andreini
dc:subject: Ribo?seq profiling; neural networks; prediction of translation speed; ribosome dynamics; CNN
meta:creation-date: 2022-08-04T10:30:33Z
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meta:keyword: Ribo?seq profiling; neural networks; prediction of translation speed; ribosome dynamics; CNN
Author: Giorgia Giacomini, Caterina Graziani, Veronica Lachi, Pietro Bongini, Niccolò Pancino, Monica Bianchini, Davide Chiarugi, Angelo Valleriani, Paolo Andreini
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