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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%...
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,...

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Detection of Copy Number Alterations Using Single Cell Sequencing
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Copy number variant detection in inbred strains from short read sequence data.

Jared T Simpson1, Rebecca E McIntyre, David J Adams

  • 1Wellcome Trust Sanger Institute, Hinxton, CB10 1HH, UK.

Bioinformatics (Oxford, England)
|December 22, 2009
PubMed
Summary

Researchers developed a new algorithm to detect copy number variants (CNVs) in homozygous organisms like mice using short read sequencing data. This hidden Markov model (HMM) approach accurately identifies genomic copy number variations in inbred strains.

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Area of Science:

  • Genomics
  • Bioinformatics

Background:

  • Copy number variants (CNVs) are significant sources of genetic variation.
  • Detecting CNVs in homozygous organisms, such as inbred mouse strains, presents unique challenges.
  • Existing methods may not be optimal for homozygous genomes.

Purpose of the Study:

  • To develop and validate a novel algorithm for detecting CNVs in homozygous organisms.
  • To leverage short read sequencing data for accurate CNV identification.
  • To improve the analysis of genetic variation in inbred model organisms.

Main Methods:

  • Developed a hidden Markov model (HMM) algorithm specifically for homozygous organisms.
  • The HMM integrates sequence read density and apparent heterozygous single nucleotide polymorphism (SNP) rates.
  • Applied the algorithm to re-sequencing data of mouse chromosome 17 from strains A/J and CAST/EiJ.

Main Results:

  • Identified a total of 118 CNVs across the two mouse strains (43 in A/J, 75 in CAST/EiJ).
  • Demonstrated the algorithm's performance by comparing results to established array-comparative genomic hybridization (array CGH) data.
  • Validated a subset of differing CNV calls using quantitative-PCR (qPCR).

Conclusions:

  • The developed HMM algorithm is effective for detecting CNVs in homozygous organisms from short read sequencing data.
  • This method offers a valuable tool for genomic variation studies in inbred laboratory strains.
  • The findings contribute to a better understanding of genetic diversity and its impact in model organisms.