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Related Concept Videos

Gene-Environment Interactions01:20

Gene-Environment Interactions

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Gene expression is a dynamic process that is significantly influenced by environmental factors. This interaction underlies the complex nature of biological development and the phenotypic differences observed among individuals, even among those with identical genetic makeups. Factors such as radiation, temperature, behavior, nutrition, and stress play pivotal roles in determining how genes are expressed. The concept of the reaction range is central to understanding this interaction. It posits...
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Predators consume prey for energy. Predators that acquire prey and prey that avoid predation both increase their chances of survival and reproduction (i.e., fitness). Routine predator-prey interactions elicit mutual adaptations that improve predator offenses, such as claws, teeth, and speed, as well as prey defenses, including crypsis, aposematism, and mimicry. Thus, predator-prey interactions resemble an evolutionary arms race.
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Gene flow is the transfer of genes among populations, resulting from either the dispersal of gametes or from the migration of individuals.
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Gene Families01:57

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Gene families consist of groups of genes proposed to have originated from a common ancestor. Typically these arise through events in which a gene or genes are mistakenly duplicated during cell division. Unlike their parent genes (which are subject to selection pressure to maintain function), these gene copies do not need to preserve their sequences and may evolve at a relatively faster rate.
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Gene Therapy00:59

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Gene therapy is a technique where a gene is inserted into a person’s cells to prevent or treat a serious disease. The added gene may be a healthy version of the gene that is mutated in the patient, or it could be a different gene that inactivates or compensates for the patient’s disease-causing gene. For example, in patients with severe combined immunodeficiency (SCID) due to a mutation in the gene for the enzyme adenosine deaminase, a functioning version of the gene can be...
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Overview
Gene expression is the process in which DNA directs the synthesis of functional products, that is, proteins. Cells can regulate gene expression at various stages. It allows organisms to generate different cell types and enables cells to adapt to internal and external factors.
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Driving Simulation in the Clinic: Testing Visual Exploratory Behavior in Daily Life Activities in Patients with Visual Field Defects
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Exploratory Gene Ontology Analysis with Interactive Visualization.

Junjie Zhu1, Qian Zhao2, Eugene Katsevich2

  • 1Department of Electrical Engineering, Stanford University, Stanford, CA, USA. jjzhu@stanford.edu.

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|May 26, 2019
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Summary
This summary is machine-generated.

New visualization tools simplify exploring the vast Gene Ontology (GO) for functional genomics. These interactive displays aid researchers in interpreting complex biological data and generating hypotheses from genomic studies.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • The Gene Ontology (GO) is a critical resource for functional genomics, providing extensive annotations.
  • The large scale of GO (>30,000 concepts, >70,000 relationships) poses challenges for comprehensive exploration and understanding.
  • Scientists use GO annotations with high-throughput data for hypothesis generation and result interpretation.

Purpose of the Study:

  • To introduce novel visualization strategies for exploring and utilizing information within the Gene Ontology.
  • To develop interactive graphical displays and software architecture for enhanced GO exploration.
  • To demonstrate the utility of these visualizations in analyzing high-throughput genomic data.

Main Methods:

  • Development of new interactive visualization strategies and software architecture.
  • Application of visualization techniques to high-throughput genomic analyses.
  • Integration of visualization tools with pipelines for gene set analysis and data simulation.

Main Results:

  • Demonstrated visualization strategies using examples from chromatin immunoprecipitation and genome-wide association studies.
  • Enabled identification of gene sets with coordinated expression changes.
  • Facilitated simulation of single-cell RNA sequencing data and power studies for differential gene expression.

Conclusions:

  • The novel visualization strategies significantly enhance the exploration and application of Gene Ontology data.
  • These tools support hypothesis generation and interpretation of complex genomic datasets.
  • The developed software provides a powerful resource for functional genomics research.