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Related Concept Videos

Protein Networks02:26

Protein Networks

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,...
Protein Networks02:26

Protein Networks

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,...
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...
Genome Annotation and Assembly03:36

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
Phylogeny01:23

Phylogeny

Phylogeny is concerned with the evolutionary diversification of organisms or groups of organisms. A group of organisms with a name is called a taxon (singular). Taxa (plural) can span different levels of the evolutionary hierarchy. For instance, the group containing all birds is a taxon (comprising the class Aves), and the group of all species of daisies (the genus Bellis) is a taxon. Phylogenies can likewise include just one genus (i.e., depict species relationships) or span an entire kingdom.
Phylogenetic Trees03:21

Phylogenetic Trees

Phylogenetic trees come in many forms. It matters in which sequence the organisms are arranged from the bottom to the top of the tree, but the branches can rotate at their nodes without altering the information. The lines connecting individual nodes can be straight, angled, or even curved.

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GraphAlignment: Bayesian pairwise alignment of biological networks.

Michal Kolář1, Jörn Meier, Ville Mustonen

  • 1Institut für Theoretische Physik, Universität zu Köln, Zülpicher Straße 77, D-50937 Köln, Germany.

BMC Systems Biology
|November 23, 2012
PubMed
Summary
This summary is machine-generated.

GraphAlignment, a new tool for aligning biomolecular networks, excels at handling noisy data and identifying homologous vertices. It offers robust performance for comparative and evolutionary network analysis.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Systems Biology

Background:

  • Increasing availability and accuracy of biomolecular networks necessitate advanced tools for comparative and evolutionary analysis.
  • Network alignment is a critical component for understanding biological network evolution and function.

Purpose of the Study:

  • To introduce the Bioconductor package GraphAlignment for pairwise alignment of biomolecular networks.
  • To evaluate the performance of GraphAlignment against existing algorithms like Græmlin 2.0 using simulated and empirical data.

Main Methods:

  • GraphAlignment utilizes an explicit evolutionary model incorporating both vertex and edge information.
  • Scoring parameters are inferred directly from empirical data.
  • Performance comparison with Græmlin 2.0 on simulated datasets and empirical protein-protein interaction (PIN) and gene co-expression networks.

Main Results:

  • GraphAlignment outperforms Græmlin 2.0 on simulated data in several benchmarks, particularly with noisy data containing spurious vertex associations.
  • On empirical bacterial networks, GraphAlignment shows improved coverage and specificity over Græmlin 2.0.
  • Græmlin 2.0 demonstrates superior performance on large eukaryotic PINs; GraphAlignment's computational complexity is approximately O(N^2.6).

Conclusions:

  • GraphAlignment is robust to spurious associations and effectively resolves paralogs.
  • The algorithm demonstrates strong performance in identifying homologous vertices based on similarity.
  • GraphAlignment's general edge scoring mechanism makes it suitable for global network alignment.