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Related Concept Videos

Chromatin Position Affects Gene Expression02:35

Chromatin Position Affects Gene Expression

Chromatin is the massive complex of DNA and proteins packaged inside the nucleus. The complexity of chromatin folding and how it is packaged inside the nucleus greatly influences  access to genetic information. Generally, the nucleus' periphery is considered transcriptionally repressive, while the cell's interior is considered a transcriptionally active area. 
Topologically Associated Domains (TADs)
The 3-dimensional positioning of chromatin in the nucleus influences the timing and level of...

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Related Experiment Video

Updated: May 21, 2026

Generation of Native Chromatin Immunoprecipitation Sequencing Libraries for Nucleosome Density Analysis
10:05

Generation of Native Chromatin Immunoprecipitation Sequencing Libraries for Nucleosome Density Analysis

Published on: December 12, 2017

NORMAL: accurate nucleosome positioning using a modified Gaussian mixture model.

Anton Polishko1, Nadia Ponts, Karine G Le Roch

  • 1Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA. polishka@cs.ucr.edu

Bioinformatics (Oxford, England)
|June 13, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a new probabilistic method for accurately determining nucleosome positions and characteristics from sequencing data. The novel approach improves upon existing methods by better handling complex configurations and detecting more nucleosomes.

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Area of Science:

  • Molecular Biology
  • Genomics
  • Bioinformatics

Background:

  • Nucleosomes are fundamental units of chromatin, regulating DNA packaging and gene expression by controlling transcription factor access.
  • Genome-wide studies using second-generation sequencing have advanced understanding of nucleosome positioning.
  • Existing methods for nucleosome positioning have limitations in handling complex configurations and providing detailed molecular information.

Purpose of the Study:

  • To develop a novel method for precise nucleosome positioning and characterization.
  • To overcome limitations of current methods in resolving complex nucleosome arrangements and providing richer data.
  • To offer molecular biologists enhanced insights into nucleosome probability, DNA fragment size, and positional fuzziness.

Main Methods:

  • A parametric probabilistic model is employed to infer nucleosome parameters.
  • An expectation-maximization algorithm is utilized for parameter inference.
  • The method's performance is evaluated against the state-of-the-art Template Filtering on synthetic and real datasets.

Main Results:

  • The novel method demonstrates superior accuracy in resolving complex nucleosome configurations on synthetic data.
  • It exhibits increased robustness to user-defined parameters compared to existing methods.
  • On real datasets, the proposed method successfully identifies a significantly greater number of nucleosomes.

Conclusions:

  • The developed probabilistic method offers a significant advancement in nucleosome positioning analysis.
  • It provides more comprehensive and accurate information about nucleosome structure and occupancy.
  • This approach enhances the utility of sequencing data for molecular biology research.