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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 25, 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 pipeline for copy number variation detection based on principal component analysis.

Jiayu Chen1, Jingyu Liu, David Boutte

  • 1Electrical Engineering Department, University of New Mexico, Albuquerque, NM 87131, USA. jychen@ece.unm.edu

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|January 19, 2012
PubMed
Summary

Detecting DNA copy number variations (CNVs) from SNP array data is difficult. This study introduces a Principal Component Analysis (PCA) correction method to improve CNV detection reliability and reduce errors.

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Array Comparative Genomic Hybridization (Array CGH) for Detection of Genomic Copy Number Variants
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Published on: February 21, 2015

Area of Science:

  • Genomics
  • Bioinformatics
  • Human Genetics

Background:

  • DNA copy number variation (CNV) is a significant structural genomic alteration linked to disease susceptibility.
  • Detecting CNVs from single-nucleotide polymorphism (SNP) array data is hindered by a low signal-to-noise ratio.

Purpose of the Study:

  • To develop a robust method for reliable CNV detection using SNP array data.
  • To address the challenges posed by low signal-to-noise ratios in existing CNV detection techniques.

Main Methods:

  • A novel processing pipeline incorporating Principal Component Analysis (PCA) for data correction was developed.
  • The proposed method was evaluated using both simulated and real SNP array datasets.

Main Results:

  • PCA-based correction significantly reduced the false positive rate in simulated CNV detection.
  • A notable improvement in data quality was observed for real SNP array data post-correction.

Conclusions:

  • The PCA-based approach enhances the reliability of CNV detection from SNP array data.
  • This method offers a promising solution for accurate CNV analysis in genomic studies and disease research.