Training-free measures based on algorithmic probability identify high nucleosome occupancy in DNA sequences.

Zenil H, Minary P

Nucleic Acids Res. 47 (20) e129 [2019-11-18; online 2019-09-13]

We introduce and study a set of training-free methods of an information-theoretic and algorithmic complexity nature that we apply to DNA sequences to identify their potential to identify nucleosomal binding sites. We test the measures on well-studied genomic sequences of different sizes drawn from different sources. The measures reveal the known in vivo versus in vitro predictive discrepancies and uncover their potential to pinpoint high and low nucleosome occupancy. We explore different possible signals within and beyond the nucleosome length and find that the complexity indices are informative of nucleosome occupancy. We found that, while it is clear that the gold standard Kaplan model is driven by GC content (by design) and by k-mer training; for high occupancy, entropy and complexity-based scores are also informative and can complement the Kaplan model.

PubMed 31511887

DOI 10.1093/nar/gkz750

Crossref 10.1093/nar/gkz750

pmc: PMC6846163
pii: 5568206