An integrated machine-learning model to predict nucleosome architecture.

We demonstrate that nucleosomes placed in the gene body can be accurately located from signal decay theory assuming two emitters located at the beginning and at the end of genes. These generated wave signals can be in phase (leading to well defined nucleosome arrays) or in antiphase (leading to fuzzy nucleosome architectures). We found that the first (+1) and the last (-last) nucleosomes are contiguous to regions signaled by transcription factor binding sites and unusual DNA physical properties that hinder nucleosome wrapping. Based on these analyses, we developed a method that combines Machine Learning and signal transmission theory able to predict the basal locations of the nucleosomes with an accuracy similar to that of experimental MNase-seq based methods.