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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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Next-generation Sequencing03:00

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The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
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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.
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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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Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
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Related Experiment Video

Updated: Mar 15, 2026

Detection of Copy Number Alterations Using Single Cell Sequencing
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CNV-RF Is a Random Forest-Based Copy Number Variation Detection Method Using Next-Generation Sequencing.

Getiria Onsongo1, Linda B Baughn2, Matthew Bower3

  • 1Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota.

The Journal of Molecular Diagnostics : JMD
|September 7, 2016
PubMed
Summary
This summary is machine-generated.

A new bioinformatics algorithm, copy number variation-random forest (CNV-RF), effectively identifies small copy number variations (CNVs) and single-nucleotide variants from targeted next-generation sequencing (NGS) data. This tool enhances clinical diagnostics by improving CNV detection accuracy and sensitivity.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Diagnostics

Background:

  • Simultaneous detection of small copy number variations (CNVs) and single-nucleotide variants (SNVs) is crucial for clinical diagnostics.
  • Challenges in targeted next-generation sequencing (NGS) include analytical variability and lack of robust bioinformatics tools for CNV detection.

Purpose of the Study:

  • To develop and implement a machine learning-based bioinformatics algorithm, copy number variation-random forest (CNV-RF), for identifying CNVs from targeted NGS data.
  • To assess the performance of CNV-RF in detecting clinically relevant CNVs.

Main Methods:

  • Development of the CNV-RF algorithm incorporating a machine learning component.
  • Application of CNV-RF to targeted NGS data for CNV identification.
  • Validation of detected CNVs using quantitative PCR (qPCR).

Main Results:

  • CNV-RF identified 12 of 13 known deletions and two duplications, along with 22 novel deletions.
  • No CNVs were detected in control samples (14 cases with normal copy number and 104 with clinical suspicion).
  • CNV-RF demonstrated 50% specificity for heterozygous deletions and duplications across 4813 genes, with all positive findings confirmed by qPCR.

Conclusions:

  • CNV-RF provides a sensitive and accurate method for detecting clinically relevant CNVs from targeted NGS data.
  • The algorithm, combined with qPCR validation, offers a robust framework for comprehensive NGS-based variant detection in clinical molecular diagnostics.