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Related Concept Videos

Genetic Variation01:25

Genetic Variation

Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
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Pleiotropy is the phenomenon in which a single gene impacts multiple, seemingly unrelated phenotypic traits. For example, defects in the SOX10 gene cause Waardenburg Syndrome Type 4, or WS4, which can cause defects in pigmentation, hearing impairments, and an absence of intestinal contractions necessary for elimination. This diversity of phenotypes results from the expression pattern of SOX10 in early embryonic and fetal development. SOX10 is found in neural crest cells that form melanocytes,...
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Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays
14:06

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Published on: November 12, 2012

Local coherence in genetic interaction patterns reveals prevalent functional versatility.

Shuye Pu1, Karen Ronen, James Vlasblom

  • 1Molecular Structure and Function Program, Hospital for Sick Children, Toronto, ON, Canada. shuyepu@sickkids.ca

Bioinformatics (Oxford, England)
|August 23, 2008
PubMed
Summary
This summary is machine-generated.

We developed a new algorithm, local coherence detection (LCD), to analyze gene interaction data. This method identifies functional gene groups, allowing genes to belong to multiple groups and revealing complex gene functions.

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Area of Science:

  • Genetics
  • Systems Biology
  • Bioinformatics

Background:

  • Epistatic interactions reveal functional gene relationships.
  • The epistatic miniarray profiles (E-MAP) method systematically identifies gene interactions.
  • Current E-MAP analysis methods like hierarchical clustering assign genes to single functional groups, missing multifunctional roles.

Purpose of the Study:

  • To develop a novel algorithm for analyzing E-MAP data.
  • To identify groups of functionally related genes, allowing for pleiotropic gene functions.
  • To improve the understanding of gene relationships and functions.

Main Methods:

  • Developed the local coherence detection (LCD) algorithm.
  • Applied LCD to E-MAP datasets from yeast (Saccharomyces cerevisiae).
  • Utilized an E-MAP-like in silico dataset for validation.

Main Results:

  • The LCD algorithm successfully identifies functional gene groups from E-MAP data.
  • Genes can be assigned to multiple functional groups, reflecting pleiotropy.
  • The algorithm recapitulates known functional modules and protein complexes.
  • Uncovered novel multifunctional gene relationships and potential new gene roles.

Conclusions:

  • The LCD algorithm is a valuable tool for analyzing gene interaction data.
  • It enhances the discovery of gene functions and relationships, including pleiotropic effects.
  • Facilitates the mapping of gene and protein groups into biological pathways.