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

ABSTRACT: 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.

SUBMITTER: Zenil H

PROVIDER: S-EPMC6846163 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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

Zenil Hector H Minary Peter P

Nucleic acids research 20191101 20

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 possibl ...[more]

PMID: 31511887

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Project description:BackgroundDNA methylation is an epigenetic modification that is enriched in heterochromatin but depleted at active promoters and enhancers. However, the debate on whether or not DNA methylation is a reliable indicator of high nucleosome occupancy has not been settled. For example, the methylation levels of DNA flanking CTCF sites are higher in linker DNA than in nucleosomal DNA, while other studies have shown that the nucleosome core is the preferred site of methylation. In this study, we make progress toward understanding these conflicting phenomena by implementing a bioinformatics approach that combines MNase-seq and NOMe-seq data and by comprehensively profiling DNA methylation and nucleosome occupancy throughout the human genome.ResultsThe results demonstrated that increasing methylated CpG density is correlated with nucleosome occupancy in the total genome and within nearly all subgenomic regions. Features with elevated methylated CpG density such as exons, SINE-Alu sequences, H3K36-trimethylated peaks, and methylated CpG islands are among the highest nucleosome occupied elements in the genome, while some of the lowest occupancies are displayed by unmethylated CpG islands and unmethylated transcription factor binding sites. Additionally, outside of CpG islands, the density of CpGs within nucleosomes was shown to be important for the nucleosomal location of DNA methylation with low CpG frequencies favoring linker methylation and high CpG frequencies favoring core particle methylation. Prominent exceptions to the correlations between methylated CpG density and nucleosome occupancy include CpG islands marked by H3K27me3 and CpG-poor heterochromatin marked by H3K9me3, and these modifications, along with DNA methylation, distinguish the major silencing mechanisms of the human epigenome.ConclusionsThus, the relationship between DNA methylation and nucleosome occupancy is influenced by the density of methylated CpG dinucleotides and by other epigenomic components in chromatin.

Dataset Information

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

Publications

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

Similar Datasets

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