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International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics

CIBB 2018: Computational Intelligence Methods for Bioinformatics and Biostatistics pp 118–127Cite as

Recurrent Deep Neural Networks for Nucleosome Classification

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Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 11925))

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.

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Author information

Authors and Affiliations

  1. Dipartmento di Matematica e Informatica, Università degli studi di Palermo, Palermo, Italy

    Domenico Amato & Giosuè Lo Bosco

  2. Fondazione Bruno Kessler, Trento, Italy

    Mattia Antonino Di Gangi

  3. ICAR-CNR - National Research Council of Italy, Palermo, Italy

    Riccardo Rizzo

Authors
  1. Domenico Amato
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  2. Mattia Antonino Di Gangi
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  3. Giosuè Lo Bosco
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  4. Riccardo Rizzo
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Corresponding author

Correspondence to Giosuè Lo Bosco .

Editor information

Editors and Affiliations

  1. Universidade Nova de Lisboa, Caparica, Portugal

    Maria Raposo

  2. Universidade Nova de Lisboa, Caparica, Portugal

    Paulo Ribeiro

  3. University of Lisbon, Caparica, Portugal

    Susana Sério

  4. Università degli Studi di Napoli Parthenope, Naples, Italy

    Antonino Staiano

  5. Università degli Studi di Napoli Parthenope, Naples, Italy

    Angelo Ciaramella

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Amato, D., Di Gangi, M.A., Lo Bosco, G., Rizzo, R. (2020). Recurrent Deep Neural Networks for Nucleosome Classification. In: Raposo, M., Ribeiro, P., Sério, S., Staiano, A., Ciaramella, A. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2018. Lecture Notes in Computer Science(), vol 11925. Springer, Cham. https://doi.org/10.1007/978-3-030-34585-3_11

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  • DOI: https://doi.org/10.1007/978-3-030-34585-3_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34584-6

  • Online ISBN: 978-3-030-34585-3

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