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Related Concept Videos

Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

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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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Comparing Copy Number Variations and SNPs02:26

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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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Genome-wide Association Studies-GWAS01:11

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
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Modern Molecular Taxonomy01:29

Modern Molecular Taxonomy

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Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...
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Related Experiment Video

Updated: Jul 28, 2025

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Published on: August 21, 2016

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PanVA: Pangenomic Variant Analysis.

Astrid van den Brandt, Eef M Jonkheer, Dirk-Jan M van Workum

    IEEE Transactions on Visualization and Computer Graphics
    |June 2, 2023
    PubMed
    Summary
    This summary is machine-generated.

    Genomics researchers can now explore complex genotype-phenotype relationships using PanVA, a novel visual analytics tool for pangenomic variant analysis. This system aids in understanding genetic variations and their impact on organism traits.

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    Related Experiment Videos

    Last Updated: Jul 28, 2025

    Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
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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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    Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
    14:06

    Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

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    Area of Science:

    • Genomics
    • Bioinformatics
    • Computational Biology

    Background:

    • Genomics research increasingly utilizes multiple reference genomes for comprehensive genetic variant exploration.
    • Pangenomes offer efficient data representation for multiple related genomes and their metadata.
    • Existing visual analysis tools struggle with complex genotype-phenotype relationships, often lacking genomic context and heterogeneous data support.

    Purpose of the Study:

    • Introduce PanVA, a visual analytics design for pangenomic variant analysis.
    • Facilitate exploration of genotype-phenotype relationships within a pangenomic context.
    • Address limitations of current visual analysis approaches in genomics.

    Main Methods:

    • Developed PanVA through active participation of genomics researchers.
    • Integrated tailored visual representations with interactive features (sorting, grouping, aggregation).
    • Evaluated PanVA in plant and pathogen research contexts.

    Main Results:

    • PanVA enables navigation and exploration of diverse perspectives on genotype-phenotype relations.
    • The tool supports interpretation of variants within their genomic context.
    • Facilitates exploration of variants in genes and hypothesis generation.

    Conclusions:

    • PanVA enhances the exploration of pangenomic variant data.
    • The design aids researchers in understanding the role of genetic variants in phenotypic variation.
    • PanVA represents a significant advancement for visual analytics in pangenomics.