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Microtubules in Signaling

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The primary cilium, made up of microtubules, acts as antennae on the cell surfaces for relaying external stimuli into the cells. These fine hair-like structures are present, generally one per cell. These are non-motile cilia in a 9+0 microtubules arrangement, where the central pair of microtubules are absent. The primary cilia arise from the basal body embedded in the cell membrane. Intraflagellar transport (IFT) carries requisite proteins from the cytoplasm to the cilium because the primary...

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Related Experiment Video

Updated: May 6, 2026

Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.

Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.

Published on: May 6, 2010

C2c: Predicting Micro-C from Hi-C.

Hao Zhu¹, Tong Liu¹, Zheng Wang¹

¹Department of Computer Science, University of Miami, 330M Ungar Building, 1365 Memorial Drive, Coral Gables, FL 33124-4245, USA.

|June 27, 2024

Summary

This summary is machine-generated.

A new computational tool, C2c, predicts high-resolution Micro-C data from standard Hi-C data. This method reveals more chromatin loops and enhances the understanding of gene regulation and interactions.

Keywords:

Micro-C deep learning predicting Micro-C from Hi-C residue network

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Last Updated: May 6, 2026

Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.

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Mapping Mammalian 3D Genome Interactions with Micro-C-XL

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Published on: November 3, 2023

Area of Science:

Genomics
Molecular Biology
Computational Biology

Background:

High-resolution Hi-C data advances gene regulation studies by detecting chromatin features below Topologically Associating Domains (TADs).
Micro-C, a Hi-C variant, offers nucleosome-pair interaction insights but presents technical challenges.
Deriving Micro-C data computationally from existing Hi-C datasets can maximize data utility and reduce costs.

Purpose of the Study:

To develop a computational method for predicting Micro-C contact matrices from Hi-C data.
To overcome the technical limitations and high costs associated with experimental Micro-C data generation.

Main Methods:

Development of C2c, a computational tool utilizing a residual neural network.
Training the model to learn the mapping between Hi-C and Micro-C contact matrices.
Evaluating the predicted Micro-C matrices against experimental data and other interaction datasets.

Main Results:

Predicted Micro-C contact matrices revealed more chromatin loops than input Hi-C data.
Loops identified from predicted Micro-C showed better matching with promoter-enhancer interactions.
Mutual loops from predicted Micro-C demonstrated superior concordance with ChIA-PET data compared to Hi-C and real Micro-C loops.
Predicted Micro-C data led to the detection of more TAD boundaries than Hi-C data.

Conclusions:

The C2c tool effectively predicts Micro-C data from Hi-C datasets, offering higher resolution insights.
This computational approach enhances the discovery of regulatory elements like chromatin loops and TAD boundaries.
C2c provides a cost-effective strategy to leverage existing Hi-C data for advanced genomic analyses.