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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%...
DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

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Related Experiment Video

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

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Published on: February 17, 2017

Bayesian hierarchical mixture modeling to assign copy number from a targeted CNV array.

Niall Cardin1, Chris Holmes,

  • 1Department of Statistics, University of Oxford, 1 South Parks Road, Oxford, United Kingdom. NiallC@gmail.com

Genetic Epidemiology
|July 20, 2011
PubMed
Summary

Accurate copy number variant (CNV) calling is crucial for genomic studies. CNVCALL, a new automated algorithm using Bayesian models, robustly assigns copy numbers and identifies classes, even with complex data.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Accurate copy number assignment at copy number variant (CNV) loci is vital for understanding genome evolution and disease association studies.
  • Copy number assignment is challenging due to CNV assays having lower signal-to-noise ratios, complex intensity distributions, and potential technical variations between cohorts.

Purpose of the Study:

  • To develop an automated and robust algorithm for copy number assignment from array data.
  • To address the challenges in copy number calling, including unknown copy number classes and noisy data.

Main Methods:

  • Developed CNVCALL, a copy number assignment algorithm for targeted CNV arrays.
  • Employed a Bayesian hierarchical mixture model to identify copy number classes and assign relative copy numbers.
  • The approach is fully automated for analyzing large-scale CNV data.

Main Results:

  • The CNVCALL algorithm robustly identifies the number of copy number classes at specific loci.
  • It accurately assigns relative copy numbers for individuals within a sample.
  • Performance was validated using real data from the Wellcome Trust Case Control Consortium and simulated data.

Conclusions:

  • CNVCALL provides a fully automated and robust solution for copy number assignment in large-scale genomic studies.
  • The Bayesian hierarchical mixture model effectively handles the complexities of CNV data.
  • This algorithm facilitates accurate copy number analysis for association studies and genomic research.