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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

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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Cell Specific Gene Expression01:58

Cell Specific Gene Expression

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Related Experiment Video

Updated: Jun 23, 2025

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations
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ScSNViz: a user-friendly toolset for visualization and analysis of Cell-Specific Expressed SNVs.

Allen Kim, Siera Martinez, Nathan Edwards

    Biorxiv : the Preprint Server for Biology
    |June 19, 2024
    PubMed
    Summary

    scSNViz is a new R-based toolset for visualizing and analyzing cell-specific expressed Single Nucleotide Variants (sceSNVs) in single-cell RNA-sequencing (scRNA-seq) data. This tool aids in understanding cellular heterogeneity and gene expression regulation.

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    Area of Science:

    • Genomics
    • Computational Biology
    • Bioinformatics

    Background:

    • Understanding genetic variation at the single-cell level is essential for studying cellular heterogeneity, clonal evolution, and gene expression.
    • Existing tools for visualizing and analyzing cell-level genetic variants are limited, creating a need for new solutions.

    Purpose of the Study:

    • To introduce scSNViz, a comprehensive R-based toolset for the visualization and analysis of cell-specific expressed Single Nucleotide Variants (sceSNVs).
    • To enable 3D visualization of sceSNVs within dimensionally reduced scRNA-seq data.
    • To facilitate the analysis of sceSNV distribution and expression across individual cells.

    Main Methods:

    • Developed scSNViz as an R-based toolset.
    • Integrated compatibility with popular scRNA-seq tools (Seurat) and cell-type classifiers (SingleR, scType).
    • Incorporated trajectory inference using Slingshot.
    • Implemented estimation, summary, and graphical representation of sceSNV statistical metrics.

    Main Results:

    • scSNViz provides 3D visualization of sceSNVs in scRNA-seq data.
    • The tool supports analysis of individual and multiple sceSNVs.
    • It offers compatibility with existing single-cell analysis pipelines.
    • ScSNViz facilitates the estimation and visualization of sceSNV metrics.

    Conclusions:

    • scSNViz is a user-friendly R-based toolset that addresses the scarcity of tools for analyzing cell-level genetic variants.
    • It enhances the understanding of cellular heterogeneity and gene expression regulation through effective visualization and analysis of sceSNVs.
    • The tool is freely available and requires no specialized bioinformatics skills.