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

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...
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.
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%...

You might also read

Related Articles

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

Sort by
Same author

Polygenic Risk Scores for Breast Cancer Among African American Women With High Risk.

JAMA network open·2026
Same author

Development and validation of a digital pathology artificial intelligence (DPAI)-derived risk score predicting Gleason grade group reclassification for patients who are candidates for active surveillance.

Future oncology (London, England)·2026
Same author

Robust Meta-Analysis of a Clinical Cell-Cycle Risk (CCR) Score Demonstrates Broadly Applicable Metastasis and Disease-Specific Mortality Risk Stratification in Men With Localized Prostate Cancer.

Clinical genitourinary cancer·2026
Same author

Role of cathepsin B in spontaneous pancreatitis in T7D23A trypsinogen mutant mice.

Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.]·2026
Same author

Reply to "Progress on Pharmacogenetic Tests for Antidepressant Drug Selection Requires Properly Designed Studies".

Journal of clinical psychopharmacology·2026
Same author

Water-soluble N-heterocyclic carbene stabilized gold nanoparticles by top-down synthesis: performance in catalysis and photoacoustic imaging.

Nanoscale·2025

Related Experiment Video

Updated: Jun 10, 2026

Array Comparative Genomic Hybridization (Array CGH) for Detection of Genomic Copy Number Variants
09:16

Array Comparative Genomic Hybridization (Array CGH) for Detection of Genomic Copy Number Variants

Published on: February 21, 2015

Computational method for estimating DNA copy numbers in normal samples, cancer cell lines, and solid tumors using

Victor Abkevich1, Diana Iliev, Kirsten M Timms

  • 1Myriad Genetics Inc., Salt Lake City, UT 84108, USA. victor@myriad.com

Journal of Biomedicine & Biotechnology
|August 14, 2010
PubMed
Summary

We developed a new computational method to detect genomic copy number variations (CNVs) and estimate actual copy numbers in cancer. This method accurately identifies CNVs even in samples with significant benign tissue contamination.

More Related Videos

Technical Demonstration of Whole Genome Array Comparative Genomic Hybridization
16:37

Technical Demonstration of Whole Genome Array Comparative Genomic Hybridization

Published on: August 5, 2008

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

Related Experiment Videos

Last Updated: Jun 10, 2026

Array Comparative Genomic Hybridization (Array CGH) for Detection of Genomic Copy Number Variants
09:16

Array Comparative Genomic Hybridization (Array CGH) for Detection of Genomic Copy Number Variants

Published on: February 21, 2015

Technical Demonstration of Whole Genome Array Comparative Genomic Hybridization
16:37

Technical Demonstration of Whole Genome Array Comparative Genomic Hybridization

Published on: August 5, 2008

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

Area of Science:

  • Genomics
  • Cancer Biology
  • Bioinformatics

Background:

  • Genomic copy number variations (CNVs) are common in cancer and impact patient outcomes and treatment efficacy.
  • Existing computational methods often detect CNVs without estimating actual copy numbers (CN).

Purpose of the Study:

  • To develop and validate a novel computational method for detecting CNVs and estimating CN in cancer.
  • To assess the method's performance in various cancer types and contamination levels.

Main Methods:

  • Developed a method relating probe signal intensity to CN, accounting for genome size and tissue contamination.
  • Utilized a Hidden Markov Model for CN estimation.
  • Implemented the method for Affymetrix 500K and Agilent 244K arrays.

Main Results:

  • The method accurately detects copy number alterations in tumor samples with up to 80% benign tissue contamination.
  • Analysis of 178 cancer cell lines identified common homozygous deletions and amplifications.
  • Detected known tumor suppressor genes, oncogenes, and novel cancer-related genes.

Conclusions:

  • The developed computational method provides accurate estimation of copy numbers in cancer.
  • This tool aids in identifying key genes involved in cancer development and progression.