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Related Concept Videos

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...
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...
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...
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
Variance01:15

Variance

The deviations show how spread out the data are about the mean. A positive deviation occurs when the data value exceeds the mean, whereas a negative deviation occurs when the data value is less than the mean. If the deviations are added, the sum is always zero. So one cannot simply add the deviations to get the data spread. By squaring the deviations, the numbers are made positive; thus, their sum will also be positive.The standard deviation measures the spread in the same units as the data.
Multiple Comparison Tests01:13

Multiple Comparison Tests

Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...

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Related Experiment Video

Updated: May 19, 2026

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07:26

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VariBench: a benchmark database for variations.

Preethy Sasidharan Nair1, Mauno Vihinen

  • 1Institute of Biomedical Technology, University of Tampere, Tampere, Finland.

Human Mutation
|August 21, 2012
PubMed
Summary

A new benchmark database, VariBench, provides high-quality, experimentally verified variation data. This resource enables unbiased comparison and training of computational tools for predicting the effects of genetic variations.

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Computational methods for predicting genetic variation effects are rapidly advancing.
  • Comparing these prediction tools is challenging due to diverse training and testing datasets.
  • A lack of unbiased, representative benchmark datasets hinders accurate performance evaluation.

Purpose of the Study:

  • To develop a comprehensive benchmark database suite, VariBench, for evaluating variation effect prediction tools.
  • To provide standardized, high-quality datasets for training and testing computational models.
  • To facilitate unbiased comparison of different prediction methods.

Main Methods:

  • Curated experimentally verified variation data from literature and databases.

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  • Mapping of variation positions across protein, RNA, and DNA sequences.
  • Integration of identifier mapping to relevant biological databases.
  • Development of the VariBench database suite.

    • Main Results:

    • VariBench offers the first benchmark datasets specifically for variation effect analysis.
    • Datasets include experimentally validated, high-quality variation data.
    • Provides comprehensive mapping of variation data to different biological levels and databases.

    Conclusions:

    • VariBench addresses the critical need for standardized benchmark datasets in variation effect prediction.
    • The database enables objective performance testing and training of novel computational tools.
    • VariBench encourages community contribution to expand its dataset resources.