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Related Concept Videos

Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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%...
RNA-seq03:21

RNA-seq

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 microarray-based...
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

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,...
Next-generation Sequencing03:00

Next-generation Sequencing

The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features.

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Related Experiment Video

Updated: May 14, 2026

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

A single cell level based method for copy number variation analysis by low coverage massively parallel sequencing.

Chunlei Zhang1, Chunsheng Zhang, Shengpei Chen

  • 1Science and Technology, BGI-Shenzhen, Shenzhen, China.

Plos One
|February 2, 2013
PubMed
Summary
This summary is machine-generated.

We developed a bioinformatic method to detect copy number variations (CNVs) in single cells using low-coverage sequencing. This approach overcomes whole genome amplification bias, enabling accurate CNV detection for research and clinical applications.

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Last Updated: May 14, 2026

Detection of Copy Number Alterations Using Single Cell Sequencing
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Pre-Implantation Genetic Testing for Aneuploidy on a Semiconductor Based Next-Generation Sequencing Platform
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Pre-Implantation Genetic Testing for Aneuploidy on a Semiconductor Based Next-Generation Sequencing Platform

Published on: August 17, 2022

Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Copy number variations (CNVs) are genomic mutations linked to diseases.
  • Single-cell CNV analysis offers insights but is limited by whole genome amplification (WGA) bias.
  • Accurate CNV detection in single cells is crucial for research and clinical diagnostics.

Purpose of the Study:

  • To develop a bioinformatic methodology for accurate single-cell CNV detection using low-coverage sequencing.
  • To address and overcome the bias introduced by whole genome amplification (WGA).
  • To provide a sensitive and specific tool for CNV analysis in limited DNA samples.

Main Methods:

  • Developed a bioinformatic pipeline incorporating GC correction for WGA bias removal.
  • Utilized a binary segmentation algorithm to identify CNV breakpoints.
  • Implemented dynamic threshold determination for filtering and signal validation.
  • Validated the method on single-cell samples from peripheral blood and blastocysts using low-coverage sequencing (4–9.5%).

Main Results:

  • Achieved high consistency with confirmed karyotypes for CNVs larger than 1 Mb.
  • Demonstrated 99.63% sensitivity and 97.71% specificity at the base-pair level.
  • Successfully detected CNVs in single cells despite low sequencing coverage.

Conclusions:

  • The developed method effectively overcomes WGA bias for single-cell CNV detection.
  • Low-coverage massively parallel sequencing is a viable approach for CNV analysis in single cells.
  • This method holds promise for applications like pre-implantation genetic diagnosis, fetal research, and cancer heterogeneity analysis.