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Abstract MotivationHigh-throughput conformation capture experiments, such as Hi-C provide genome-wide maps of chromatin interactions, enabling life scientists to investigate the role of the three-dimensional structure of genomes in gene regulation and other essential cellular functions. A fundamental problem in the analysis of Hi-C data is how to compare two contact maps derived from Hi-C experiments. Detecting similarities and differences between contact maps are critical in evaluating the reproducibility of replicate experiments and for identifying differential genomic regions with biological significance. Due to the complexity of chromatin conformations and the presence of technology-driven and sequence-specific biases, the comparative analysis of Hi-C data is analytically and computationally challenging. ResultsWe present a novel method called Selfish for the comparative analysis of Hi-C data that takes advantage of the structural self-similarity in contact maps. We define a novel self-similarity measure to design algorithms for (i) measuring reproducibility for Hi-C replicate experiments and (ii) finding differential chromatin interactions between two contact maps. Extensive experimental results on simulated and real data show that Selfish is more accurate and robust than state-of-the-art methods. Availability and implementationhttps://github.com/ucrbioinfo/Selfish
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Genome wide clustering on integrated chromatin states and Micro-C contacts reveals chromatin interaction signatures
Abstract We can now analyze 3D physical interactions of chromatin regions with chromatin conformation capture technologies, in addition to the 1D chromatin state annotations, but methods to integrate this information are lacking. We propose a method to integrate the chromatin state of interacting regions into a vector representation through the contact-weighted sum of chromatin states. Unsupervised clustering on integrated chromatin states and Micro-C contacts reveals common patterns of chromatin interaction signatures. This provides an integrated view of the complex dynamics of concurrent change occurring in chromatin state and in chromatin interaction, adding another layer of annotation beyond chromatin state or Hi-C contact separately.
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- Award ID(s):
- 1750532
- PAR ID:
- 10546703
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- NAR Genomics and Bioinformatics
- Volume:
- 6
- Issue:
- 4
- ISSN:
- 2631-9268
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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