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Recurrent Deep Neural Networks for Nucleosome Classification

  • Autori: Amato, Domenico; Di Gangi, Mattia Antonino; Lo Bosco, Giosuè; Rizzo, Riccardo
  • Anno di pubblicazione: 2020
  • Tipologia: Contributo in atti di convegno pubblicato in volume
  • OA Link:


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.