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Genome-wide prediction of topoisomerase IIB binding by architectural factors and chromatin accessibility

ABSTRACT: Genome-wide prediction of topoisomerase IIB binding by architectural factors and chromatin accessibility

PROVIDER: PRJNA593812 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR10597079.fastq.gz	Fastqsanger.gz
	SRR10597080.fastq.gz	Fastqsanger.gz
	SRR10597081.fastq.gz	Fastqsanger.gz

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Genome-wide prediction of topoisomerase IIB binding by architectural factors and chromatin accessibility

Project description:Aberrant activity of type II topoisomerases (TOP2) often causes blocked double-strand breaks (DSBs), whose inefficient repair can seriously compromise genomic stability. One of the two TOP2 paralogs encoded in vertebrates is TOP2B, which has been linked to essential processes such as transcription or genome organization. Few TOP2B genome-wide maps have been profiled, and a comprehensive study of the mechanisms involved in TOP2B-DNA binding is still lacking. Here, we conduct an in silico approach for the prediction of TOP2B binding sites using publicly available sequencing data. We achieve highly accurate predictions and find that open chromatin and architectural factors are the most informative features. We also validate our predictions on experimental data and generate predicted TOP2B tracks that mirror experimental ones with high precision.

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Genome-wide prediction of topoisomerase IIβ binding by architectural factors and chromatin accessibility.

Project description:DNA topoisomerase II-β (TOP2B) is fundamental to remove topological problems linked to DNA metabolism and 3D chromatin architecture, but its cut-and-reseal catalytic mechanism can accidentally cause DNA double-strand breaks (DSBs) that can seriously compromise genome integrity. Understanding the factors that determine the genome-wide distribution of TOP2B is therefore not only essential for a complete knowledge of genome dynamics and organization, but also for the implications of TOP2-induced DSBs in the origin of oncogenic translocations and other types of chromosomal rearrangements. Here, we conduct a machine-learning approach for the prediction of TOP2B binding using publicly available sequencing data. We achieve highly accurate predictions, with accessible chromatin and architectural factors being the most informative features. Strikingly, TOP2B is sufficiently explained by only three features: DNase I hypersensitivity, CTCF and cohesin binding, for which genome-wide data are widely available. Based on this, we develop a predictive model for TOP2B genome-wide binding that can be used across cell lines and species, and generate virtual probability tracks that accurately mirror experimental ChIP-seq data. Our results deepen our knowledge on how the accessibility and 3D organization of chromatin determine TOP2B function, and constitute a proof of principle regarding the in silico prediction of sequence-independent chromatin-binding factors.

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