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Related Concept Videos

Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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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.
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CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data
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Graphlet-based measures are suitable for biological network comparison.

Wayne Hayes1, Kai Sun, Nataša Pržulj

  • 1Department of Computer Science, University of California, Irvine, CA 92697-3435, USA.

Bioinformatics (Oxford, England)
|January 26, 2013
PubMed
Summary
This summary is machine-generated.

Graphlet-based network analysis is stable for biological networks, even at low densities. This study shows real protein-protein interaction networks are stable, unlike some models, and fit theoretical models well.

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Area of Science:

  • Computational Biology
  • Network Science
  • Bioinformatics

Background:

  • Biological networks provide insights analogous to sequence comparison.
  • Graphlet-based methods are valuable for network comparison.
  • Concerns exist regarding graphlet measure stability in low-density networks and suitable network models.

Purpose of the Study:

  • To address the stability concerns of graphlet-based measures in low edge density networks.
  • To evaluate the suitability of existing network models for biological networks.
  • To investigate the topological properties of biological networks, particularly protein-protein interaction (PPI) networks.

Main Methods:

  • Analysis of graphlet-based measures on model and biological networks.
  • Distinguishing between average and local edge density in network topology.
  • Application of a non-parametric statistical test for network model fitting.
  • Modeling viral protein-protein interaction networks.

Main Results:

  • Model networks, not graphlet measures, exhibit instability at low edge density.
  • Biological networks, especially PPI networks, demonstrate stable topology.
  • PPI networks exhibit high local edge density despite low average edge density, ensuring measure stability.
  • Several theoretical models show good fit to PPI networks, including newly modeled viral PPI networks.

Conclusions:

  • Graphlet-based measures are stable and applicable to biological networks, including low-density PPI networks.
  • Real biological networks possess distinct topological properties (high local density) that ensure stability.
  • Existing network models can be refined, and new models identified, for better representation of biological networks.