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Related Concept Videos

Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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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.
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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Single Nucleotide Polymorphisms-SNPs01:05

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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...
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Exon Recombination

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The evolution of new genes is critical for speciation. Exon recombination, also known as exon shuffling or domain shuffling, is an important means of new gene formation. It is observed across vertebrates, invertebrates, and in some plants such as potatoes and sunflowers. During exon recombination, exons from the same or different genes recombine and produce new exon-intron combinations, which might evolve into new genes. 
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Related Experiment Video

Updated: Jan 2, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
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CoNVaDING: Single Exon Variation Detection in Targeted NGS Data.

Lennart F Johansson1,2, Freerk van Dijk1,2, Eddy N de Boer1

  • 1University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands.

Human Mutation
|February 12, 2016
PubMed
Summary
This summary is machine-generated.

We developed CoNVaDING, a tool for detecting single exon copy-number variations (CNVs) in next-generation sequencing data. It offers high sensitivity and specificity, improving both research and clinical diagnostics.

Keywords:
CNVNGSclinical diagnosticsexon deletion/duplicationtargeted next-generation sequencing

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Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Copy-number variations (CNVs) are crucial genetic alterations.
  • Accurate detection of single exon CNVs in targeted next-generation sequencing (NGS) data is challenging.
  • Existing tools often lack precision in identifying low-quality samples and regions.

Purpose of the Study:

  • To develop and validate a novel tool, CoNVaDING (Copy Number Variation Detection In Next-generation sequencing Gene panels), for detecting single exon CNVs.
  • To implement a stringent quality control (QC) metric within the tool.
  • To enhance the reliability of CNV detection in both research and clinical settings.

Main Methods:

  • Development of CoNVaDING, a computational tool for CNV detection in targeted NGS data.
  • Integration of a quality control (QC) metric to assess exon reliability.
  • Validation using 320 samples and 308,574 exons to evaluate performance.

Main Results:

  • CoNVaDING achieved 100% sensitivity and 99.998% specificity in validation.
  • The tool successfully identified all known CNVs in high-quality targets.
  • CoNVaDING effectively flagged low-quality exons and samples, guiding analysis.

Conclusions:

  • CoNVaDING provides a robust and accurate method for single exon CNV detection.
  • The tool's stringent QC enhances reliability for research and clinical diagnostics.
  • CoNVaDING outperforms existing methods in sensitivity, specificity, and sample/region quality assessment.