Background: Nucleosomes wrap the DNA into the nucleus of the Eukaryote cell and regulate its transcription phase. Several studies indicate that nucleosomes are determined by the combined effects of several factors, including DNA sequence organization. Interestingly, the identification of nucleosomes on a genomic scale has been successfully performed by computational methods using DNA sequence as input data. Results: In this work, we propose CORENup, a deep learning model for nucleosome identification. CORENup processes a DNA sequence as input using one-hot representation and combines in a parallel fashion a fully convolutional neural network and a recurrent layer. These two parallel levels are devoted to catching both non-periodic and periodic DNA string features. A dense layer is devoted to their combination to give a final classification. Conclusions: Results computed on public data sets of different organisms show that CORENup is a state of the art methodology for nucleosome positioning identification based on a Deep Neural Network architecture. The comparisons have been carried out using two groups of datasets, currently adopted by the best performing methods, and CORENup has shown top performance both in terms of classification metrics and elapsed computation time.

Amato, D., Lo Bosco, G., & Riccardo, R. (2020). CORENup: a combination of convolutional and recurrent deep neural networks for nucleosome positioning identification. BMC BIOINFORMATICS, 21(8), 326 [10.1186/s12859-020-03627-x].

CORENup: a combination of convolutional and recurrent deep neural networks for nucleosome positioning identification

Amato, Domenico;Lo Bosco, Giosue'
;
Riccardo, Rizzo
2020

Abstract

Background: Nucleosomes wrap the DNA into the nucleus of the Eukaryote cell and regulate its transcription phase. Several studies indicate that nucleosomes are determined by the combined effects of several factors, including DNA sequence organization. Interestingly, the identification of nucleosomes on a genomic scale has been successfully performed by computational methods using DNA sequence as input data. Results: In this work, we propose CORENup, a deep learning model for nucleosome identification. CORENup processes a DNA sequence as input using one-hot representation and combines in a parallel fashion a fully convolutional neural network and a recurrent layer. These two parallel levels are devoted to catching both non-periodic and periodic DNA string features. A dense layer is devoted to their combination to give a final classification. Conclusions: Results computed on public data sets of different organisms show that CORENup is a state of the art methodology for nucleosome positioning identification based on a Deep Neural Network architecture. The comparisons have been carried out using two groups of datasets, currently adopted by the best performing methods, and CORENup has shown top performance both in terms of classification metrics and elapsed computation time.
Settore INF/01 - Informatica
Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazioni
https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-03627-x
Amato, D., Lo Bosco, G., & Riccardo, R. (2020). CORENup: a combination of convolutional and recurrent deep neural networks for nucleosome positioning identification. BMC BIOINFORMATICS, 21(8), 326 [10.1186/s12859-020-03627-x].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10447/427331
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