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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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Genome Copying Errors

DNA replication is a well-evolved process that copies millions of base pairs with high fidelity during each cell division. Occasionally a wrong base or a long stretch of wrong bases may get added to the daughter strands. If the errors are left unchecked, cells might accumulate several mutations that might endanger their  survival. Therefore, the copying errors are checked and repaired at three levels.
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

Updated: Jun 2, 2026

Detection of Copy Number Alterations Using Single Cell Sequencing
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Published on: February 17, 2017

cn.FARMS: a latent variable model to detect copy number variations in microarray data with a low false discovery

Djork-Arné Clevert1, Andreas Mitterecker, Andreas Mayr

  • 1Institute of Bioinformatics, Johannes Kepler University Linz, Linz, Austria.

Nucleic Acids Research
|April 14, 2011
PubMed
Summary

This study introduces cn.FARMS, a new Bayesian model to accurately detect DNA copy number variations (CNVs) from genotyping arrays. cn.FARMS significantly reduces false discoveries, improving disease association studies.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Oligonucleotide genotyping arrays are standard for DNA copy number variation (CNV) detection.
  • Current microarray-based CNV detection methods often overestimate CNV regions, leading to a high false discovery rate (FDR).
  • A high FDR reduces the power of clinical studies to identify disease-associated CNVs.

Purpose of the Study:

  • To develop a novel probabilistic model, cn.FARMS, for accurate CNV detection.
  • To control the false discovery rate (FDR) in CNV analysis.
  • To improve the sensitivity and specificity of CNV detection compared to existing methods.

Main Methods:

  • A probabilistic latent variable model (cn.FARMS) was developed.
  • The model utilizes a Bayesian maximum a posteriori approach for optimization.
  • cn.FARMS controls FDR by leveraging information gain from posterior over prior distributions.

Main Results:

  • cn.FARMS demonstrated superior performance on HapMap data compared to prevalent methods.
  • The model showed improved sensitivity and a reduced false discovery rate (FDR).
  • The software cn.FARMS is available as an R package.

Conclusions:

  • cn.FARMS offers a more accurate approach to CNV detection using genotyping arrays.
  • The method effectively controls FDR, enhancing the reliability of CNV association studies.
  • This tool has the potential to improve clinical research by increasing discovery power.