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.
|Title of host publication||Computational Intelligence Methods for Bioinformatics and Biostatistics, 15th International Meeting, CIBB 2018, Caparica, Portugal, September 6–8, 2018|
|Number of pages||10|
|Publication status||Published - 2020|
|Name||LECTURE NOTES IN COMPUTER SCIENCE|
- Theoretical Computer Science
- General Computer Science