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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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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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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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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.
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Molecular taxonomy has revolutionized the understanding and classification of bacteria, providing precise insights into their diversity, evolutionary relationships, and ecological roles. By utilizing molecular techniques such as DNA sequencing and fingerprinting, researchers have made significant strides in various fields related to bacterial studies.Resolving Taxonomic AmbiguitiesMolecular taxonomy has been instrumental in distinguishing closely related bacterial species initially thought to...
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Related Experiment Video

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Characterizing Mutational Load and Clonal Composition of Human Blood
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SNPmanifold: detecting single-cell clonality and lineages from single-nucleotide variants using binomial variational

Hoi Man Chung1, Yuanhua Huang2,3

  • 1School of Biomedical Sciences, University of Hong Kong, Hong Kong SAR, China.

Genome Biology
|September 27, 2025
PubMed
Summary

SNPmanifold, a new Python package, simplifies single-cell lineage tracing by learning mutation patterns. It accurately assigns single-nucleotide-variant (SNV) clones, improving analysis of complex cellular data.

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Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Single-cell lineage tracing is crucial for understanding biological processes but faces challenges with complex mutation data.
  • Accurate clone assignment from single-nucleotide-variant (SNV) data is difficult due to hierarchical mutation structures and missing signals.

Purpose of the Study:

  • To develop an efficient and interpretable computational tool for single-nucleotide-variant (SNV) clone assignment in high-covariance single-cell lineage tracing data.
  • To provide a novel method for analyzing complex single-cell mutation data, enhancing insights into cellular clonality and lineage.

Main Methods:

  • Developed SNPmanifold, a Python package utilizing a binomial variational autoencoder to learn an SNV embedding manifold.
  • Implemented an efficient and interpretable cell-cell distance metric derived from the learned manifold.

Main Results:

  • SNPmanifold effectively handles complex, single-cell SNV mutation data, including demultiplexing numerous donors.
  • Demonstrated utility in somatic lineage tracing using mitochondrial SNV data.
  • Achieved more accurate and comprehensive insights into single-cell clonality and lineages compared to existing methods.

Conclusions:

  • SNPmanifold offers a robust solution for SNV clone assignment in challenging single-cell lineage tracing scenarios.
  • The package enhances the accuracy and comprehensiveness of analyzing cellular mutations and inferring clonal relationships.
  • SNPmanifold provides a valuable tool for researchers in genomics and computational biology.