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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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Genome Copying Errors02:46

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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

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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RNA-seq03:21

RNA-seq

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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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Related Experiment Video

Updated: Mar 6, 2026

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

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Published on: February 17, 2017

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Detection of Copy Number Alterations Using Single Cell Sequencing.

Kristin A Knouse1, Jie Wu2, Austin Hendricks3

  • 1Koch Institute for Integrative Cancer Research, Department of Biology, Massachusetts Institute of Technology; Howard Hughes Medical Institute; Division of Health Sciences and Technology, Harvard Medical School; kknouse@mit.edu.

Journal of Visualized Experiments : Jove
|March 14, 2017
PubMed
Summary

This study introduces a new protocol for single cell sequencing to detect genomic changes. The method reliably identifies large copy number variants in individual cells, improving the study of genetic heterogeneity.

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Characterizing genetic heterogeneity and evolution in tissues and diseases requires high-resolution genomic analysis.
  • Traditional methods for assessing genetic variations lack the necessary resolution, sensitivity, and specificity.
  • Single cell sequencing offers a powerful approach to overcome these limitations.

Purpose of the Study:

  • To present a comprehensive protocol for single cell analysis.
  • To enable the detection of genomic alterations at single cell resolution.
  • To facilitate the study of genetic heterogeneity in various biological contexts.

Main Methods:

  • Isolation of single cells.
  • Whole genome amplification (WGA) of single cell DNA.
  • Next-generation sequencing (NGS) of amplified DNA.
  • Bioinformatic analysis for variant detection.

Main Results:

  • A reliable protocol for single cell whole genome sequencing was established.
  • The method successfully identified megabase-scale copy number variants (CNVs) in single cells.
  • The protocol demonstrates high sensitivity and specificity for genomic alteration detection.

Conclusions:

  • The developed protocol significantly advances the capability to analyze single cell genomes.
  • This method is crucial for understanding genetic heterogeneity and evolution in normal and diseased states.
  • The protocol's framework can be adapted for investigating diverse genetic alterations in single cells.