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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.
The range, standard deviation, standard error, and variance are the different measures of variation.
Range: The range is the difference between its maximum and...
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Variation01:19

Variation

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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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Genetic Variation01:25

Genetic Variation

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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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Variability: Analysis01:11

Variability: Analysis

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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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Genome Annotation and Assembly03:36

Genome Annotation and Assembly

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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.
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Variation: Normal Distribution, Range, and Standard Deviation02:32

Variation: Normal Distribution, Range, and Standard Deviation

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In the field of psychology, there are several ways to organize measurements of a trait, feature, or characteristic (i.e., variables). Qualitative data, such as ethnicity, can be tabulated into a frequency count to provide information about the proportion, as well as the variety of groups in a sample or population. On the other hand, researchers can perform a wider set of calculations on quantitative data. The mean, mode, and median, for instance, are central tendency measures to identify a...
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Related Experiment Video

Updated: Mar 27, 2026

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information
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VariOtator, a Software Tool for Variation Annotation with the Variation Ontology.

Gerard C P Schaafsma1, Mauno Vihinen1

  • 1Protein Structure and Bioinformatics, Department of Experimental Medical Science, Lund University, BMC B13, Lund, SE-221 84, Sweden.

Human Mutation
|January 17, 2016
PubMed
Summary

The Variation Ontology (VariO) and its annotation tool, VariOtator, streamline the description of genetic variations and their effects. This facilitates consistent data integration and analysis in genomics research.

Keywords:
LOVDLSDBVariation Ontologyannotationbioinformaticsdatabasemutationontologysoftwarevariation

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Area of Science:

  • Genomics
  • Bioinformatics
  • Ontology Development

Background:

  • The Variation Ontology (VariO) provides a standardized framework for annotating genetic variations.
  • Consistent and efficient annotation of variation data is crucial for genomic research and database integration.
  • Existing methods for variation annotation can be time-consuming and lack standardization.

Purpose of the Study:

  • To introduce VariOtator, an online application designed to automate and standardize the annotation of genetic variations using the Variation Ontology (VariO).
  • To facilitate the generation of consistent VariO terms for variation types, effects, consequences, and mechanisms.
  • To enable programmatic access and batch processing for large-scale variation annotation.

Main Methods:

  • VariOtator accepts variant descriptions in Human Genome Variation Society (HGVS) format for automated annotation of variation types.
  • Integration with Mutalyzer for validation of coding DNA variant descriptions.
  • Automated generation of predicted RNA and protein descriptions with VariO annotations.
  • Support for manual annotation of variation effects (function, structure, property) using attribute and evidence code terms.
  • Provision of online batch, stand-alone batch, and SOAP Web services for accessibility.

Main Results:

  • VariOtator fully automates variation type annotations from HGVS descriptions, generating VariO terms with or without lineage.
  • Predicted RNA and protein descriptions are generated with corresponding VariO annotations for coding DNA variants.
  • Manual annotation support is available for variation effects, structure, and property, incorporating evidence codes.
  • Multiple access methods (online, batch, SOAP Web service) enhance usability and integration.

Conclusions:

  • VariOtator significantly improves the efficiency and consistency of variation annotation using the Variation Ontology (VariO).
  • The tool supports automated and manual annotation processes, catering to diverse user needs and data types.
  • VariOtator facilitates the seamless integration of variation data and their consequences into genomic databases and research workflows.