Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

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%...
Genome Copying Errors02:46

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.
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

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,...
Sanger Sequencing01:57

Sanger Sequencing

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...
Genetic Variation01:25

Genetic Variation

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.
Genes exist in different versions called alleles, which...
Next-generation Sequencing03:00

Next-generation Sequencing

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.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Subclinical primary aldosteronism and major adverse cardiovascular events: evidence for a continuum of renin-independent aldosterone excess and a proposal for early detection.

Frontiers in cardiovascular medicine·2026
Same author

SPOTTER: Automated Tissue-Barcoding Platform for Spatial Proteomics and Phosphoproteomics.

bioRxiv : the preprint server for biology·2026
Same author

Superficial intercostal plane blocks for post-sternotomy pain control rationale and design for the EPOCH CardioLink-10 randomized clinical trial.

Current opinion in cardiology·2026
Same author

Microneedle array platforms for drug delivery and biomarker sensing: From skin mechanics guided design to scalable manufacture for clinical utility.

Journal of controlled release : official journal of the Controlled Release Society·2026
Same author

A deep learning approach for predicting linear accelerator output settings in automated radiotherapy planning of oligometastatic cancer.

Physics and imaging in radiation oncology·2026
Same author

The Benefits and Risks of Glucagon-Like Peptide-1 Receptor Agonists on Ocular Diseases: A Narrative Review.

Cureus·2026

Related Experiment Video

Updated: May 14, 2026

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

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

CoNVEX: copy number variation estimation in exome sequencing data using HMM.

Kaushalya C Amarasinghe1, Jason Li, Saman K Halgamuge

  • 1Department of Mechanical Engineering, University of Melbourne, Parkville, VIC 3010, Australia. kca@student.unimelb.edu.au

BMC Bioinformatics
|February 2, 2013
PubMed
Summary

Copy Number Variations (CNV) are key genetic changes in cancer. A new method, CoNVEX, uses whole exome sequencing (WES) data with noise reduction and a Hidden Markov Model (HMM) to accurately detect CNV, outperforming existing techniques.

More Related Videos

Flow-sorting and Exome Sequencing of the Reed-Sternberg Cells of Classical Hodgkin Lymphoma
08:53

Flow-sorting and Exome Sequencing of the Reed-Sternberg Cells of Classical Hodgkin Lymphoma

Published on: June 10, 2017

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
11:02

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Published on: October 18, 2013

Related Experiment Videos

Last Updated: May 14, 2026

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

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

Flow-sorting and Exome Sequencing of the Reed-Sternberg Cells of Classical Hodgkin Lymphoma
08:53

Flow-sorting and Exome Sequencing of the Reed-Sternberg Cells of Classical Hodgkin Lymphoma

Published on: June 10, 2017

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
11:02

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Published on: October 18, 2013

Area of Science:

  • Genomics
  • Cancer Research
  • Bioinformatics

Background:

  • Copy Number Variations (CNV) represent a significant category of genetic alterations in cancer.
  • Whole Exome Sequencing (WES) is a cost-effective alternative to Whole Genome Sequencing (WGS) for studying cancer-specific genomic variations.
  • Detecting CNVs in cancer samples using WES data remains an underexplored area.

Purpose of the Study:

  • To introduce CoNVEX, a novel method for estimating CNVs from WES data.
  • To address the challenge of intrinsic noise in WES data that hinders reliable CNV detection.
  • To improve the accuracy and reliability of CNV detection in cancer exome sequencing.

Main Methods:

  • CoNVEX utilizes the ratio of tumor and matched normal average read depths in exonic regions.
  • Discrete Wavelet Transform (DWT) is employed for noise reduction in WES data.
  • A Hidden Markov Model (HMM) is applied for the identification of copy number gains and losses.

Main Results:

  • CoNVEX demonstrated superior performance compared to existing methods in terms of precision.
  • The proposed method achieved a sensitivity exceeding 92%.
  • CoNVEX attained a precision rate greater than 50% in CNV detection.

Conclusions:

  • Hidden Markov Models (HMM) are established for CNV identification across various technologies like aCGH and WGS.
  • This study proposes an HMM-based approach for detecting CNVs specifically in cancer exome data.
  • CoNVEX shows significant improvements in CNV detection accuracy from WES data, outperforming current methods.