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.
Giacomini, G., Graziani, C., Lachi, V., Bongini, P., Pancino, N., Bianchini, M., et al. (2022). A Neural Network Approach for the Analysis of Reproducible Ribo–Seq Profiles. ALGORITHMS, 15(8) [10.3390/a15080274].
A Neural Network Approach for the Analysis of Reproducible Ribo–Seq Profiles
Graziani, Caterina;Lachi, Veronica;Bongini, Pietro;Bianchini, Monica;Andreini, Paolo
2022-01-01
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.File | Dimensione | Formato | |
---|---|---|---|
algorithms-15-00274 (1).pdf
accesso aperto
Tipologia:
PDF editoriale
Licenza:
Creative commons
Dimensione
1.05 MB
Formato
Adobe PDF
|
1.05 MB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
https://hdl.handle.net/11365/1214434