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EnglishNucleosomes are the fundamental repeating unit of chromatin. A nucleosome is an 8 histone proteins complex, in which approximately 147–150 pairs of DNA bases bind. Several biological studies have clearly stated that the regulation of cell type-specific gene activities are influenced by nucleosome positioning. Bioinformatic studies have improved those results showing proof of sequence specificity in nucleosomes’ DNA fragment. In this work, we present a recurrent neural network that uses nucleosome sequence features representation for their classification. In particular, we implement an architecture which stacks convolutional and long short-term memory layers, with the main purpose to avoid the features extraction and selection steps. We have computed classifications using eight datasets of three different organisms with a growing genome complexity, from yeast to human. We have also studied the capability of the model trained on the highest complex species in recognizing nucleosomes of the other organisms.
Amato, D., Di Gangi, M.A., Lo Bosco, G., & Rizzo, R. (2020). Recurrent Deep Neural Networks for Nucleosome Classification. In M. Raposo, P. Ribeiro, S. Sério, A. Staiano, & A. Ciaramella (a cura di), Computational Intelligence Methods for Bioinformatics and Biostatistics, 15th International Meeting, CIBB 2018, Caparica, Portugal, September 6–8, 2018 (pp. 118-127).
| Data di pubblicazione: | 2020 | |
| Titolo: | Recurrent Deep Neural Networks for Nucleosome Classification | |
| Autori: | ||
| Citazione: | Amato, D., Di Gangi, M.A., Lo Bosco, G., & Rizzo, R. (2020). Recurrent Deep Neural Networks for Nucleosome Classification. In M. Raposo, P. Ribeiro, S. Sério, A. Staiano, & A. Ciaramella (a cura di), Computational Intelligence Methods for Bioinformatics and Biostatistics, 15th International Meeting, CIBB 2018, Caparica, Portugal, September 6–8, 2018 (pp. 118-127). | |
| Abstract: | Nucleosomes are the fundamental repeating unit of chromatin. A nucleosome is an 8 histone proteins complex, in which approximately 147–150 pairs of DNA bases bind. Several biological studies have clearly stated that the regulation of cell type-specific gene activities are influenced by nucleosome positioning. Bioinformatic studies have improved those results showing proof of sequence specificity in nucleosomes’ DNA fragment. In this work, we present a recurrent neural network that uses nucleosome sequence features representation for their classification. In particular, we implement an architecture which stacks convolutional and long short-term memory layers, with the main purpose to avoid the features extraction and selection steps. We have computed classifications using eight datasets of three different organisms with a growing genome complexity, from yeast to human. We have also studied the capability of the model trained on the highest complex species in recognizing nucleosomes of the other organisms. | |
| ISBN: | 978-3-030-34584-6 978-3-030-34585-3 | |
| Digital Object Identifier (DOI): | 10.1007/978-3-030-34585-3_11 | |
| Settore Scientifico Disciplinare: | Settore INF/01 - Informatica Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazioni | |
| Appare nelle tipologie: | 2.07 Contributo in atti di convegno pubblicato in volume |
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