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Genome-wide nucleosome profiles are predominantly characterized using MNase-seq, which involves extensive MNase digestion and size selection to enrich for mononucleosome-sized fragments. Most available MNase-seq analysis packages assume that nucleosomes uniformly protect 147 bp DNA fragments. However, some nucleosomes with atypical histone or chemical compositions protect shorter lengths of DNA. The rigid assumptions imposed by current nucleosome analysis packages potentially prevent investigators from understanding the regulatory roles played by atypical nucleosomes. To enable the characterization of different nucleosome types from MNase-seq data, we introduce the size-based expectation maximization (SEM) nucleosome-calling package. SEM employs a hierarchical Gaussian mixture model to estimate nucleosome positions and subtypes. Nucleosome subtypes are automatically identified based on the distribution of protected DNA fragments. Benchmark analysis indicates that SEM is on par with existing packages in terms of standard nucleosome-calling accuracy metrics, while uniquely providing the ability to characterize nucleosome subtype identities. Applying SEM to a low-dose MNase-H2B-ChIP-seq data set from mouse embryonic stem cells, we identified three nucleosome types: short-fragment nucleosomes, canonical nucleosomes, and di-nucleosomes. Short-fragment nucleosomes can be divided further into two subtypes based on their chromatin accessibility. Short-fragment nucleosomes in accessible regions exhibit high MNase sensitivity and are enriched at transcription start sites (TSSs) and CTCF peaks, similar to previously reported “fragile nucleosomes.” These SEM-defined accessible short-fragment nucleosomes are found not just in promoters but also in distal regulatory regions. Additional analyses reveal their colocalization with the chromatin remodelers CHD6, CHD8, and EP400. In summary, SEM provides an effective platform for exploration of nonstandard nucleosome subtypes.
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DNAcycP: a deep learning tool for DNA cyclizability prediction
Abstract DNA mechanical properties play a critical role in every aspect of DNA-dependent biological processes. Recently a high throughput assay named loop-seq has been developed to quantify the intrinsic bendability of a massive number of DNA fragments simultaneously. Using the loop-seq data, we develop a software tool, DNAcycP, based on a deep-learning approach for intrinsic DNA cyclizability prediction. We demonstrate DNAcycP predicts intrinsic DNA cyclizability with high fidelity compared to the experimental data. Using an independent dataset from in vitro selection for enrichment of loopable sequences, we further verified the predicted cyclizability score, termed C-score, can well distinguish DNA fragments with different loopability. We applied DNAcycP to multiple species and compared the C-scores with available high-resolution chemical nucleosome maps. Our analyses showed that both yeast and mouse genomes share a conserved feature of high DNA bendability spanning nucleosome dyads. Additionally, we extended our analysis to transcription factor binding sites and surprisingly found that the cyclizability is substantially elevated at CTCF binding sites in the mouse genome. We further demonstrate this distinct mechanical property is conserved across mammalian species and is inherent to CTCF binding DNA motif.
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- Award ID(s):
- 1764421
- PAR ID:
- 10365313
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Nucleic Acids Research
- Volume:
- 50
- Issue:
- 6
- ISSN:
- 0305-1048
- Page Range / eLocation ID:
- p. 3142-3154
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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