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Related Concept Videos

Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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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%...
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Single Nucleotide Polymorphisms-SNPs01:05

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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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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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What is Variation?01:14

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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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Variability: Analysis01:11

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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.
The range is a simple measure of variability, indicating the difference between the highest and...
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Isolation of Fidelity Variants of RNA Viruses and Characterization of Virus Mutation Frequency
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VariSNP, a benchmark database for variations from dbSNP.

Gerard C P Schaafsma1, Mauno Vihinen

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

Human Mutation
|November 12, 2014
PubMed
Summary

New VariSNP datasets provide essential benchmark data for predicting genetic variant effects. These curated, large-scale datasets focus on benign variants, addressing a critical gap in current resources.

Keywords:
benchmarkdbSNPgenetic variationmutationvariant effect analysisvariant effect predictionvariant position mapping

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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
  • Computational Biology

Background:

  • Predicting the functional impact of genetic variations is crucial for understanding disease.
  • Existing benchmark datasets for variant effect prediction are often outdated or lack sufficient examples of benign variants.
  • There is a need for comprehensive, up-to-date resources to evaluate and develop predictive methods.

Purpose of the Study:

  • To introduce the VariSNP datasets, a novel resource for the development and evaluation of variant effect prediction tools.
  • To provide large-scale, curated benchmark sets specifically for benign genetic variants.
  • To facilitate research in variant interpretation and personalized medicine.

Main Methods:

  • VariSNP datasets were generated by selecting subsets from the dbSNP database.
  • Subsets were rigorously filtered against known disease-related variants from ClinVar, UniProtKB/Swiss-Prot, and PhenCode.
  • Variant annotations include comprehensive mapping across chromosomal, genomic, coding DNA, and protein levels.

Main Results:

  • The VariSNP datasets offer a substantial collection of benign variants.
  • The filtering process ensures high confidence in the nonpathogenic classification of included variants.
  • Detailed multi-level annotations are provided for each variant.

Conclusions:

  • The VariSNP datasets fill a critical gap for benchmarking variant effect prediction methods.
  • These freely available resources will aid researchers in developing more accurate predictive tools.
  • The datasets are designed for regular updates, ensuring continued relevance.