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Related Concept Videos

Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.
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MetaHiCNet: a web server for normalizing and visualizing microbial Hi-C interaction networks.

Chentong Sun1, Zhen Qin1, Ruishan Liu2

  • 1Department of Electrical and Computer Engineering, The University of Texas at San Antonio, San Antonio, Texas 78249, United States.

Nucleic Acids Research
|April 27, 2025
PubMed
Summary
This summary is machine-generated.

MetaHiCNet simplifies microbial genome analysis by offering user-friendly normalization and visualization for metagenomic Hi-C (metaHi-C) data. This platform enhances the study of microbial interactions and community dynamics.

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Related Experiment Videos

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Area of Science:

  • Microbiology
  • Genomics
  • Bioinformatics

Background:

  • Metagenomic Hi-C (metaHi-C) reveals microbial genome organization and interactions.
  • Raw metaHi-C data contain biases and spurious contacts, hindering analysis.
  • Existing pipelines lack accessible normalization and visualization tools.

Purpose of the Study:

  • Introduce MetaHiCNet, a web platform for metaHi-C data analysis.
  • Provide user-friendly normalization and visualization for microbial interaction networks.
  • Facilitate deeper insights into microbial community structures and dynamics.

Main Methods:

  • MetaHiCNet offers customizable normalization methods for bias correction.
  • The platform implements stepwise workflows for spurious contact removal.
  • Interactive visualization modes include taxonomic treemaps and cross-taxa networks.

Main Results:

  • MetaHiCNet enables seamless exploration from community-wide to specific taxon analyses.
  • The platform supports investigation of host-microbe interactions.
  • It aids in understanding relationships between mobile genetic elements and hosts.

Conclusions:

  • MetaHiCNet enhances the analysis of microbial genome organization and interactions.
  • The platform democratizes metaHi-C data exploration with intuitive tools.
  • Accessible insights into microbial communities are now more readily available.