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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%...

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Related Experiment Video

Updated: May 11, 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 method for finding consensus breakpoints in the cancer genome from copy number data.

Laura Toloşi1, Jessica Theißen, Konstantin Halachev

  • 1Department of Computational Biology and Applied Algorithmics, Max-Planck-Institute for Informatics, Campus E1.4, 66123 Saarbrücken, Germany.

Bioinformatics (Oxford, England)
|May 30, 2013
PubMed
Summary

Identifying recurrent DNA breakpoints in cancer genomes is crucial for discovering new therapeutic targets. Our new method accurately pinpoints these consensus breakpoints, aiding in tumor classification and understanding cancer development.

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Last Updated: May 11, 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

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
11:02

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Published on: October 18, 2013

Array Comparative Genomic Hybridization (Array CGH) for Detection of Genomic Copy Number Variants
09:16

Array Comparative Genomic Hybridization (Array CGH) for Detection of Genomic Copy Number Variants

Published on: February 21, 2015

Area of Science:

  • Genomics
  • Cancer Research
  • Bioinformatics

Background:

  • Recurrent DNA breakpoints in cancer genomes highlight critical elements for tumor development and potential therapeutic targets.
  • High-dimensional array comparative genomic hybridization (arrayCGH) experiments precisely identify DNA copy number breakpoints.

Purpose of the Study:

  • To introduce a computational method for identifying recurrent breakpoints (consensus breakpoints) from copy number aberration datasets.
  • To demonstrate the utility of consensus breakpoints in sample segmentation and dimension reduction for predicting tumor phenotype.
  • To apply the method for classifying neuroblastoma tumors and investigating the functional genomic properties at breakpoint locations.

Main Methods:

  • A weighted kernel counting method was developed to identify significant recurrent breakpoints from copy number data.
  • The method was applied to three arrayCGH datasets for consensus segmentation and dimension reduction.
  • The approach was used for classifying neuroblastoma tumors by age and analyzing genomic properties at consensus breakpoints across seven datasets.

Main Results:

  • The developed method successfully identifies consensus breakpoints, which facilitate consensus segmentation of samples.
  • Application to arrayCGH data resulted in significant dimension reduction, improving tumor phenotype prediction.
  • Classification of neuroblastoma tumors by age confirmed established treatment cut-offs, and analysis revealed enrichment of consensus breakpoints in important functional genomic regions.

Conclusions:

  • Consensus breakpoints derived from copy number data are valuable for cancer research, enabling improved tumor classification and identification of functionally relevant genomic regions.
  • The method provides a robust approach for dimension reduction and phenotype prediction in cancer genomics.
  • The findings support the use of consensus breakpoints for understanding cancer development and identifying novel therapeutic strategies.