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Evolutionary Relationships07:32

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ExpandNOTE: A cladogram is an important tool for forming an evolutionary hypothesis. A cladogram is a tree-shaped chart used to depict the hypothetical genealogical relationships between species. The tips or leaves of the chart represent specific species and the branches of the tree are different lengths. The different lengths represent the degree of change between each of the species. The common ancestor of all of the species that a specific line branches...
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Humans have been attempting to properly classify living things since Aristotle made the first attempt during the 4th century BC. Aristotle’s system was improved upon during the Renaissance and then, subsequently, by Carolus Linnaeus in the mid 1700’s. These more formal classification and organization systems grouped species by their physical similarity to one another. For example, all vertebrates have a backbone, but invertebrates do not. Traits like the backbone are called...
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ExpandOpen the BLAST website on each computer that the students will be using. https://blast.ncbi.nlm.nih.gov/Blast.cgi
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Related Experiment Video

Updated: Jan 20, 2026

Evolutionary Relationships: Using BLAST to Test Evolutionary Hypotheses - Procedure
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A novel evolutionary model for constructing gene coexpression networks with comprehensive features.

Yuexi Gu1, Jian Zu2, Yu Li1

  • 1School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, 710049, People's Republic of China.

BMC Bioinformatics
|September 8, 2019
PubMed
Summary
This summary is machine-generated.

Gene duplication and de novo genes drive coexpression network evolution. New genes integrate via a "rich-gets-richer" mechanism, shaping network architecture and scale-free properties.

Keywords:
De novo genesDuplication genesGenomeNetwork biologyNetwork evolution

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Area of Science:

  • Genomics
  • Systems Biology
  • Evolutionary Biology

Background:

  • Understanding gene coexpression network evolution is crucial for characterizing new genes.
  • Existing models often overlook topological properties beyond gene degree and de novo gene integration.
  • Comprehensive models are needed to explain how new genes join ancestral networks.

Purpose of the Study:

  • To develop a novel evolutionary model for gene coexpression networks.
  • To incorporate both gene duplication and de novo gene evolution.
  • To analyze the integration mechanisms of new genes into existing networks.

Main Methods:

  • Constructed a human gene coexpression network using RNA-seq data (8061 genes, 638624 links).
  • Utilized human gene age data to model the evolutionary process.
  • Analyzed gene duplication randomness and new gene interaction preferences.

Main Results:

  • Identified 1394 duplication and 126 de novo genes.
  • Estimated generation rates: 3.58/Myr for duplication, 0.31/Myr for de novo genes.
  • Duplication is random; new genes (both types) preferentially interact with high-degree, high-coreness genes.

Conclusions:

  • Gene duplication and de novo genes are key evolutionary forces shaping coexpression networks.
  • A 'rich-gets-richer' mechanism drives new gene interactions, contributing to scale-free and hierarchical network properties.
  • The proposed model accurately reconstructs networks with biological characteristics.