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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.
Genes exist in different versions called alleles, which...
What is Variation?01:14

What is Variation?

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...
Variability: Analysis01:11

Variability: Analysis

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.
The range is a simple measure of variability, indicating the difference between the highest and...
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved DNA...
Genome Annotation and Assembly03:36

Genome Annotation and Assembly

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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Related Experiment Video

Updated: May 18, 2026

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information
09:37

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information

Published on: August 15, 2019

VarioML framework for comprehensive variation data representation and exchange.

Myles Byrne1, Ivo Fac Fokkema, Owen Lancaster

  • 1Institute for Molecular Medicine Finland-FIMM, University of Helsinki, Helsinki, Finland. juha.muilu@helsinki.fi

BMC Bioinformatics
|October 4, 2012
PubMed
Summary
This summary is machine-generated.

VarioML simplifies human variant data sharing with a flexible format and toolkit. This improves data availability and clarity for researchers, labs, and clinics.

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09:37

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Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
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Area of Science:

  • Genomics
  • Bioinformatics
  • Data Science

Background:

  • Sharing human variation and phenotype data is crucial but hindered by complexity.
  • Existing standards are often too difficult and time-consuming to implement.
  • A need exists for a flexible and accessible variant data standard.

Purpose of the Study:

  • To develop a comprehensive data model and format for capturing and sharing variant information.
  • To create a toolkit for adapting the format to diverse research workflows.
  • To facilitate easier data submission to variant databases and integration into applications.

Main Methods:

  • Developed Observ-OM, a data model for biomedical observations.
  • Created VarioML, a format built around Observ-OM with a simplified variant specification.
  • Developed a Java toolkit for VarioML integration and data transformation (XML, web application formats).

Main Results:

  • VarioML allows straightforward capture, federation, and exchange of variant data with no overhead.
  • Complex variant data can be described without loss of compatibility.
  • Enabled push-button submission to gene variant databases and bidirectional data transformation.

Conclusions:

  • VarioML enhances the availability, quality, and comprehensibility of human variation data.
  • It provides an easy, clear, and unambiguous method for sharing variant information.
  • Facilitates data sharing among researchers, diagnostic laboratories, and clinics.