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Related Concept Videos

What is Variation?01:14

What is Variation?

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Apart from the measures of central tendency, distribution, outliers, and the changing characteristics of data with time, an important characteristic of any data set is its variation or spread. In some data sets, the data values are concentrated closely near the mean; in others, the data values are more widely spread out from the mean.
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Variation01:19

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An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
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Comparing Copy Number Variations and SNPs02:26

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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.
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The coefficient of variation measures the dispersion of the data points or distribution around the mean. Using the coefficient of variation, we can compare two data series with drastically different means or different units of measurement. The coefficient of variation for a sample and a population is expressed as a percentage of the ratio of standard deviation to the mean.
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CNV-seq, a new method to detect copy number variation using high-throughput sequencing.

Chao Xie1, Martti T Tammi

  • 1Department of Biological Sciences, National University of Singapore, Singapore. xie@nus.edu.sg

BMC Bioinformatics
|March 10, 2009
PubMed
Summary
This summary is machine-generated.

We developed CNV-seq, a novel method using shotgun sequencing to detect DNA copy number variation (CNV). This approach leverages next-generation sequencing, offering high resolution and accuracy for genetic variation analysis.

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • DNA copy number variation (CNV) is a significant source of genetic diversity.
  • Array comparative genomic hybridization (aCGH) is a common but limited method for CNV detection.

Purpose of the Study:

  • To introduce CNV-seq, a new method for detecting CNV using shotgun sequencing.
  • To establish a robust statistical model for CNV analysis and confidence value computation.

Main Methods:

  • Utilized shotgun sequencing for CNV detection.
  • Developed a statistical model to analyze sequencing data and determine CNV confidence.
  • Investigated the impact of read number versus read length on detection resolution.

Main Results:

  • CNV-seq demonstrates that the number of sequencing reads, not their length, is critical for CNV detection resolution.
  • The method is well-suited for next-generation sequencing platforms generating numerous short reads.
  • Simulations show high specificity (91.7-99.9%) and sensitivity (72.2-96.5%) across various sequencing coverages.

Conclusions:

  • CNV-seq provides an accurate and high-resolution method for detecting DNA copy number variation.
  • The method is advantageous for next-generation sequencing technologies.
  • Successfully applied CNV-seq to assess CNV between two human genomes.