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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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Related Experiment Video

Updated: May 31, 2026

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

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Published on: February 17, 2017

An algorithm for detecting high frequency copy number polymorphisms using SNP arrays.

Bjarni V Halldórsson1, Daníel F Gudbjartsson

  • 1School of Science and Engineering, Reykjavík University, Reykjavík, Iceland. bjarnivh@ru.is

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|July 7, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a novel algorithm for detecting diverse genomic variants, including single nucleotide polymorphisms (SNPs), copy number polymorphisms, and microsatellites, using the Illumina iSelect platform. The method efficiently genotypes large populations without prior variant knowledge.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Population Genetics

Background:

  • The Illumina iSelect platform is primarily designed for single nucleotide polymorphism (SNP) detection.
  • Existing algorithms may require prior knowledge of polymorphism types, limiting their scope.
  • Genotyping large cohorts efficiently and comprehensively remains a challenge.

Purpose of the Study:

  • To develop a versatile algorithm for detecting a broad range of genomic variants.
  • To enable simultaneous genotyping of numerous individuals using the Illumina iSelect platform.
  • To overcome the limitations of algorithms requiring a priori polymorphism information.

Main Methods:

  • A generalized algorithm was developed for genomic variant detection on the Illumina iSelect platform.
  • The algorithm includes intensity normalization and batch effect correction.
  • A modified Gaussian mixture model was employed for marker clustering, accounting for individual and probe-level variances.
  • Genotypes were determined from the clustered data.

Main Results:

  • The algorithm successfully detects various genomic variations beyond SNPs, such as copy number polymorphisms and microsatellites.
  • It processes large datasets, demonstrated by its application to 35,000 Icelandic individuals.
  • The method is robust, not requiring prior information on the specific type of polymorphism.

Conclusions:

  • The developed algorithm offers a flexible and powerful approach for comprehensive genomic variant detection.
  • This method enhances the utility of the Illumina iSelect platform for large-scale population studies.
  • The algorithm facilitates efficient and accurate genotyping across diverse genetic variations.