6533b855fe1ef96bd12afdc7
RESEARCH PRODUCT
Recurrent Deep Neural Networks for Nucleosome Classification
Mattia Antonino Di GangiDomenico AmatoGiosuè Lo BoscoRiccardo Rizzosubject
0301 basic medicineSettore ING-INF/05 - Sistemi Di Elaborazione Delle InformazionibiologySettore INF/01 - InformaticaComputer scienceComputational biologyChromatin03 medical and health scienceschemistry.chemical_compound030104 developmental biologyHistoneRecurrent neural networkchemistryFragment (logic)biology.proteinNucleosomeNucleosome classification Epigenetic Deep learning networks Recurrent Neural NetworksGeneDNASequence (medicine)description
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2020-01-01 |