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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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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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Sanger Sequencing01:57

Sanger Sequencing

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DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...
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RNA-seq03:21

RNA-seq

10.4K
RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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Updated: Sep 6, 2025

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

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

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Somatic Single-Nucleotide Variant Calling from Single-Cell DNA Sequencing Data Using SCAN-SNV.

Sajedeh Bahonar1, Hesam Montazeri2

  • 1Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran.

Methods in Molecular Biology (Clifton, N.J.)
|June 25, 2022
PubMed
Summary

SCAN-SNV is a new computational tool for identifying somatic single-nucleotide variants (SNVs) in single-cell DNA sequencing data. It refines mutation detection by analyzing allele-specific amplification balance, improving accuracy in variant calling.

Keywords:
Allele-specific amplification balanceConda environmentGaussian processHeterozygous SNPsSCAN-SNVSingle-cell DNA sequencingSomatic variant calling

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Last Updated: Sep 6, 2025

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Somatic single-nucleotide variant (SNV) identification is crucial for understanding cancer evolution and development.
  • Analyzing single-cell DNA sequencing data presents unique challenges due to high noise levels and potential artifacts.
  • Existing tools may struggle with accurate SNV detection in the complex landscape of single-cell genomics.

Purpose of the Study:

  • To introduce SCAN-SNV, a novel computational tool designed for robust somatic SNV identification from single-cell DNA sequencing data.
  • To provide a practical, step-by-step guide for installing and utilizing the SCAN-SNV variant caller.
  • To demonstrate the application of SCAN-SNV in a real-world scenario for accurate mutation detection.

Main Methods:

  • SCAN-SNV processes single-cell and matched bulk sequencing data to identify candidate somatic SNVs and heterozygous single-nucleotide polymorphisms (hSNPs).
  • It employs a probabilistic spatial statistical model to estimate genome-wide allele-specific amplification balance (AB).
  • Artifactual candidate somatic SNVs are filtered out based on AB predictions, leading to the identification of putative mutations.

Main Results:

  • The SCAN-SNV workflow effectively distinguishes true somatic SNVs from technical artifacts.
  • Accurate estimation of allele-specific amplification balance aids in refining variant calls.
  • The package provides a user-friendly interface for practical application in genomic research.

Conclusions:

  • SCAN-SNV offers an advanced computational approach for reliable somatic SNV identification in single-cell DNA sequencing.
  • The tool enhances mutation detection accuracy by integrating allele-specific amplification balance analysis.
  • This guide facilitates the adoption of SCAN-SNV for researchers studying somatic mutations at the single-cell level.