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Related Concept Videos

RNA-seq03:21

RNA-seq

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. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...
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%...
Real Time RT-PCR02:57

Real Time RT-PCR

Real-time reverse transcription-polymerase chain reaction, or Real-time RT-PCR, is an analytical tool used to determine the expression level of target genes. The method involves converting mRNA to complementary DNA with the help of an enzyme known as reverse transcriptase, followed by the PCR amplification of the cDNA. These two processes can be performed simultaneously in a single tube or separately as a two-step reaction.
The real-time quantification of the number of amplified products is...

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Related Experiment Video

Updated: Jun 2, 2026

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing
11:11

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

Published on: August 24, 2017

Accurate and exact CNV identification from targeted high-throughput sequence data.

Alex S Nord1, Ming Lee, Mary-Claire King

  • 1Department of Genome Sciences, University of Washington, Seattle, 98195-7720, USA. nordalex@u.washington.edu

BMC Genomics
|April 14, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for detecting copy number variations (CNVs) in targeted DNA sequencing data. The approach combines coverage and mapping information to accurately identify deletions and duplications, enhancing mutation screening efficiency.

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High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)

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Related Experiment Videos

Last Updated: Jun 2, 2026

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing
11:11

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

Published on: August 24, 2017

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

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)
09:06

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)

Published on: October 5, 2018

Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Massively parallel sequencing enhances gene screening efficiency.
  • Short read aligners excel at single nucleotide and indel detection.
  • Targeted sequencing lacks robust copy number variation (CNV) detection methods.

Purpose of the Study:

  • To develop and present a novel method for CNV identification in targeted sequencing data.
  • To combine coverage and mapping information for deletion and duplication detection.

Main Methods:

  • Utilized normalized coverage data comparison between samples to scan for gains and losses.
  • Confirmed CNV calls by identifying spanning sequences at breakpoints.
  • Developed a method applicable whether breakpoints are within or outside targeted regions.

Main Results:

  • The method detected CNVs as small as 31 bp in a test dataset of 96 subjects.
  • Accurately predicted quantitative copy counts.
  • Demonstrated a low false-positive rate for CNV detection.

Conclusions:

  • The developed method enables the identification of gains and losses in targeted sequence data.
  • This approach provides comprehensive mutation screening when integrated with short read aligners.