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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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DNA replication is a well-evolved process that copies millions of base pairs with high fidelity during each cell division. Occasionally a wrong base or a long stretch of wrong bases may get added to the daughter strands. If the errors are left unchecked, cells might accumulate several mutations that might endanger their  survival. Therefore, the copying errors are checked and repaired at three levels.
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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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Related Experiment Video

Updated: Oct 17, 2025

Detection of Copy Number Alterations Using Single Cell Sequencing
09:45

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Published on: February 17, 2017

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Sensitivity to copy number variation analysis in single cell genomics.

Jing Tu1, Yue Zhou1, Yuhan Tao1

  • 1State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China.

Gene
|October 10, 2021
PubMed
Summary

Optimizing single-cell copy number analysis requires careful consideration of sequencing depth and resolution. A practical guideline suggests 0.75× sequencing depth with a 250 kb bin size for effective copy number variation detection.

Keywords:
Copy number variationSingle cell sequencingTumor evolutionVariant callingWhole genome amplification

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Single-cell genomics enables high-resolution analysis of cellular heterogeneity.
  • Comprehensive sensitivity of copy number calling in single-cell genomics remains a challenge.
  • Previous studies offer limited guidance on optimizing copy number analysis parameters.

Purpose of the Study:

  • To investigate the impact of sequencing depth and other factors on single-cell copy number analysis sensitivity.
  • To establish practical guidelines for cost-effective and accurate copy number variation detection in single cells.
  • To evaluate the influence of whole genome amplification (WGA) methods and cell types on CNV calling.

Main Methods:

  • Analysis of 26 single-cell genomics datasets comprising 2946 cells.
  • Downsampling experiments to evaluate copy number variation (CNV) detection sensitivity at various bin sizes (e.g., 250 kb).
  • Assessment of WGA approaches and cell type effects on CNV calling using downsampled data.
  • t-distributed Stochastic Neighbor Embedding (t-SNE) based cluster analysis for performance evaluation.

Main Results:

  • A sequencing depth of 0.75× with a 250 kb bin size emerged as a practical recommendation for copy number calling.
  • CNV detection sensitivity is influenced by sequencing depth, bin size, WGA method, and cell type.
  • t-SNE clustering provided a framework for assessing the performance of CNV calling.

Conclusions:

  • A sequencing depth of 0.75× and a 250 kb bin size offer a balanced approach to single-cell copy number analysis.
  • Optimization of CNV calling parameters should consider amplification strategy, cell type, and sample complexity.
  • These findings provide valuable guidance for researchers conducting single-cell genomics studies.