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Related Concept Videos

What is Variation?01:14

What is Variation?

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Apart from the measures of central tendency, distribution, outliers, and the changing characteristics of data with time, an important characteristic of any data set is its variation or spread. In some data sets, the data values are concentrated closely near the mean; in others, the data values are more widely spread out from the mean.
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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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Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
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Variation01:19

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An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
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Related Experiment Video

Updated: Sep 29, 2025

Generating Strictly Controlled Stimuli for Figure Recognition Experiments
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The GA4GH Variation Representation Specification: A computational framework for variation representation and

Alex H Wagner1,2,3, Lawrence Babb4, Gil Alterovitz5,6

  • 1Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH 43210, USA.

Cell Genomics
|March 21, 2022
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Summary

The Variation Representation Specification (VRS) offers a computable framework for reliable genetic variation data exchange. This enables consistent identification of biomolecular variations globally, enhancing genomic data

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Reliable exchange of genetic variation data is crucial for maximizing the value of genomic information in personal, public, research, and clinical settings.
  • Existing human-readable and flat file standards for genomic variation representation have limitations in computable precision and federated identification.

Purpose of the Study:

  • To introduce the Variation Representation Specification (VRS), an extensible framework for the computable representation of biomolecular variation.
  • To enable federated identification of biomolecular variation with globally consistent and unique computed identifiers.

Main Methods:

  • Development of VRS as a framework with a terminology and information model.
  • Creation of a machine-readable schema, data sharing conventions, and a reference implementation.
  • Collaboration among national information resource providers, public initiatives, and diagnostic testing laboratories under GA4GH.

Main Results:

  • VRS provides semantically precise representations of genetic variation.
  • The framework enables the generation of globally consistent and unique computed identifiers for biomolecular variations.
  • VRS is designed to be broadly useful and freely available for community use.

Conclusions:

  • VRS offers a standardized and computable approach to representing and exchanging genetic variation data.
  • The framework is expected to significantly improve the reliability and consistency of genomic data sharing and analysis.
  • VRS has the potential to enhance the clinical, research, and public value derived from genomic information.