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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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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

SnoopCGH: software for visualizing comparative genomic hybridization data.

Jacob Almagro-Garcia1, Magnus Manske, Celine Carret

  • 1Wellcome Trust Sanger Institute, Hinxton, The Weatherall Institute of Molecular Medicine and Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK. jg10@sanger.ac.uk

Bioinformatics (Oxford, England)
|August 19, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces SnoopCGH, a software tool designed for analyzing genomic structural variations (SVs) from array comparative genomic hybridization (CGH) data. The tool aids in visualizing and summarizing complex genomic data for efficient SV identification and analysis.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Array-based comparative genomic hybridization (CGH) is crucial for identifying genomic structural variations (SVs).
  • Effective visualization and summarization of array CGH data are essential for SV analysis across multiple samples.

Purpose of the Study:

  • To develop a software tool for the analysis of structural variants (SVs) using array CGH data.
  • To provide a method for visualizing and summarizing array CGH outputs for SV identification.

Main Methods:

  • Developed a Java-based software tool named SnoopCGH.
  • The tool processes data from array CGH technologies.
  • The software is also compatible with short-read sequencing data.

Main Results:

  • SnoopCGH facilitates the discovery and validation of genomic structural variations.
  • The software aids in the visualization and summarization of array CGH data.
  • The tool supports analysis across multiple samples for comprehensive SV identification.

Conclusions:

  • SnoopCGH is a valuable software tool for structural variant analysis.
  • The tool enhances the interpretation of array CGH and short-read sequencing data for genomic variation studies.