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Each human somatic cell contains 6 billion base-pairs of DNA. Each base-pair is 0.34 nm long, which means that each diploid cell contains a staggering 2 meters of DNA. How is such a long DNA strand packed inside a nucleus measuring only 10 - 20 microns in diameter? 
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Each human somatic cell contains 6 billion base pairs of DNA. Each base pair is 0.34 nm long, meaning each diploid cell contains a staggering 2 meters of DNA. This long DNA strand is packed inside a nucleus measuring only 10-20 microns in diameter with the help of specialized DNA-binding proteins called histones. Together they form a compact DNA-protein complex called chromatin. The chromatin is further compacted into higher-order structures. The highest level of compaction is achieved during...
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Predicting CTCF-mediated chromatin loops using CTCF-MP.

Ruochi Zhang1, Yuchuan Wang1, Yang Yang1

  • 1Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.

Bioinformatics (Oxford, England)
|June 29, 2018
PubMed
Summary
This summary is machine-generated.

Sequence-based features predict chromatin loop formation. Motif conservation and a new machine learning tool, CTCF-MP, identify sequence determinants for CCCTC-binding factor (CTCF)-mediated loops.

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Chromosomal organization in the cell nucleus is regulated.
  • CCCTC-binding factor (CTCF) is crucial for mediating long-range chromatin loops.
  • Convergent CTCF binding motif pairs are common at loop anchor regions, but sequence-level determinants remain unclear.

Purpose of the Study:

  • To investigate sequence-based features that determine CTCF-mediated chromatin loop formation.
  • To assess the predictive power of sequence features for loop formation.
  • To develop a computational tool for predicting loop formation.

Main Methods:

  • Analysis of sequence-based features, including motif conservation ('branch-of-origin').
  • Development of a machine learning algorithm (CTCF-MP) using word2vec.
  • Integration of functional genomic data (CTCF ChIP-seq, DNase-seq).

Main Results:

  • Motif conservation is an important feature for predicting loop formation.
  • Sequence-based features alone can predict if convergent CTCF motifs form loops.
  • CTCF-MP accurately predicts loop formation across cell types when combined with functional genomic signals.

Conclusions:

  • Sequence determinants play a significant role in guiding the formation of chromatin architectures.
  • CTCF-MP provides a powerful tool for understanding sequence-based regulation of chromatin loops.
  • This study advances the understanding of the molecular mechanisms underlying 3D genome organization.