Quantitative models for statistical nucleosome occupancy prediction

Abstract

Nucleosome is the basic unit of DNA in eukaryotic cells. As nucleosomes limit the accessibility of the wrapped DNA to transcription factors and other DNA-binding proteins, their positions play an essential role in regulations of gene activities. Experiments have indicated that DNA sequence strongly influences nucleosome positioning by enhancing or reducing their binding affinity to nucleosomes, therefore providing an intrinsic cell regulatory mechanism. While some sequence features are known to be nucleosome forming or nucleosome inhibiting, however, existing models have limited accuracy in predicting quantitatively nucleosomes occupancy (i.e., statistical nucleosome positioning) based on DNA sequence. In this paper, we propose new quantitative models for DNA sequence-based nucleosome-occupancy prediction based on dinucleotide-matching features, where the parameters are learned through regression algorithms. Experimental results on a genome-wide set of yeast dataset show that our models give more accurate predictions than existing models.