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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.
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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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Related Experiment Video

Updated: Jun 9, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
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stSNV: a comprehensive resource of SNVs in spatial transcriptome.

Changbo Yang1, Yujie Liu2, Xiaohua Wang3

  • 1College of Bioinformatics Science and Technology, Harbin Medical University, No.157 Baojian Road, Harbin, Heilongjiang 150081, China.

Nucleic Acids Research
|October 29, 2024
PubMed
Summary

This study introduces stSNV, a spatial mutation resource detailing single nucleotide variants (SNVs) in human and mouse tissues. It aids in understanding genetic heterogeneity and SNV roles in disease.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Spatial Biology

Background:

  • Single nucleotide variants (SNVs) are key to genetic variation, influencing gene expression, function, and phenotypes.
  • Understanding the spatial distribution of SNVs in diseased and normal tissues is crucial for insights into cell lineage, aging, and disease.
  • Characterizing SNVs' roles requires comprehensive spatial mutation data and analytical tools.

Purpose of the Study:

  • To develop and present stSNV, a comprehensive spatial mutation resource for human and mouse tissues.
  • To provide an atlas of spatial SNVs, including their distribution and characteristics.
  • To facilitate the exploration of SNVs' impact on gene expression, cell communication, and biological functions.

Main Methods:

  • Compiled a dataset of 898,908 SNVs across 42,202 mutated genes from 730,067 spots in 450 tissue slices.
  • Analyzed SNV perturbation effects on gene expression, spatial communication, and biological functions.
  • Integrated data into a user-friendly interface with visualization tools and analytical functionalities for exploring SNV-cell co-localization.

Main Results:

  • stSNV documents spatial SNVs in 19 diseased and 28 normal human and mouse tissues.
  • Analysis revealed insights into SNV characteristics, including region-specific mutated genes, spatial mutant signatures, and mutation core regions.
  • The resource enables exploration of co-localization among cell types, genes, and SNVs within tissue slices.

Conclusions:

  • stSNV serves as a valuable resource for dissecting intra-tissue genetic heterogeneity.
  • The platform supports understanding the biological regulatory mechanisms of SNVs in spatial contexts.
  • This work lays the groundwork for future research on spatial genomics and disease mechanisms.