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Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...
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Related Experiment Video

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Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
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Decoding topologically associating domains with ultra-low resolution Hi-C data by graph structural entropy.

Angsheng Li1,2,3, Xianchen Yin4,5, Bingxiang Xu6,7

  • 1State Key Laboratory of Software Development Environment, School of Computer Science, Beihang University, 100083, Beijing, P.R. China. angsheng@ios.ac.cn.

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|August 17, 2018
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We developed deDoc, a fast, normalization-free method using information theory to identify chromosomal domains (TADs) from Hi-C data. This approach reveals fundamental genome organization principles even in small single-cell cohorts.

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Area of Science:

  • Genomics
  • Computational Biology
  • Structural Biology

Background:

  • Topologically associating domains (TADs) are crucial for genome organization, but their detection using high-throughput chromatin interaction (Hi-C) data is challenging.
  • Current methods require deep sequencing and complex normalization, limiting scalability and accessibility.

Purpose of the Study:

  • To develop a rapid and normalization-free computational method for identifying chromosomal domains from Hi-C data.
  • To leverage structural information theory for robust domain detection and optimal bin size determination.

Main Methods:

  • Proposed 'deDoc' (decode domains of chromosomes), a novel algorithm utilizing structural information theory.
  • Represented Hi-C contact matrices as graphs and partitioned them into segments with minimal structural entropy.
  • Applied deDoc to pooled Hi-C data from 10 single cells.

Main Results:

  • Successfully detected megabase-size TAD-like domains using the deDoc algorithm.
  • Demonstrated that structural entropy can effectively determine optimal bin sizes for Hi-C data analysis.
  • Identified conserved domain structures across a small cohort of single cells.

Conclusions:

  • The deDoc method offers a fast and efficient alternative for TAD detection without normalization.
  • The findings suggest that genome spatial organization into modular domains is a fundamental principle, observable even at the single-cell level.
  • This algorithm facilitates large-scale investigations into chromosomal domain organization.